[ { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Batch number sensitivity analyze Batch.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/100.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Batch number sensitivity analyze Batch." ], "reasoning": "Increasing the number of batches reduces training, validation, and test errors, improving the accuracy of the deep learning process.", "description": "Looking at table.III in more details, by increasing the number of batches training and test errors have been decreased. it is also noticeable that the validation data fall by increasing the number of batches. during the learning process the main aim is to decrease the validation data error which have been satisfied properly.moreover, increment of the batches directly influence the accuracy of the deep learning process." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Batch number sensitivity analyze Batch.'\nProblem: 'What is the validation error when the number of batches is 700?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/100.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Batch number sensitivity analyze Batch." ], "reasoning": "To find the validation error for 700 batches, locate the row with 700 batches and read the corresponding validation error value.", "question": "What is the validation error when the number of batches is 700?", "answer": "So, the answer is {\"answer\": \"3.1497\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe test error decreases as the number of batches increases.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/100.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Batch number sensitivity analyze Batch." ], "reasoning": "The table shows that the test error values decrease from 9.1574 to 3.0214 as the number of batches increases from 100 to 1000.", "statement": "The test error decreases as the number of batches increases.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Accuracy analyze of deep learning solution based on changing coefficie.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/101.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Accuracy analyze of deep learning solution based on changing coefficie." ], "reasoning": "The accuracy of the deep learning solution depends on the correlation between the Rate of reaction (k) and the Diffusion coefficient (De). Lower k values or higher De values reduce the accuracy.", "description": "The variable parameters in this part are k and De which play a central role in the behavior of the equation. Table.IV demonstrate the accuracy of deep learning solution by changing the k and De values, where C0 and L are 75.5 and 0.05 respectively.. According to the table.IV, it is understood that the accuracy of the solution strongly depends on the correlation of Rate of reaction and Diffusion coefficient. In the case of constant Diffusion coefficient where k value is less than the particular amount the accuracy of the solution drop considerably. It is also similar for the case of the constant Rate of reaction where if De value chose larger than specific value, the solution will not be reliable. When it comes to physics of the phenomena, reduction of the accuracy of the deep learning solution become" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Accuracy analyze of deep learning solution based on changing coefficie.'\nProblem: 'What is the accuracy of the deep learning solution for Thr(1) when k is 2.125*10^-7?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/101.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Accuracy analyze of deep learning solution based on changing coefficie." ], "reasoning": "To find the accuracy for Thr(1) when k is 2.125*10^-7, locate the row where k is 2.125*10^-7 and then find the corresponding value under the Thr(1) column.", "question": "What is the accuracy of the deep learning solution for Thr(1) when k is 2.125*10^-7?", "answer": "So, the answer is {\"answer\": \"84.58%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe accuracy of the deep learning solution for Thr(2) is higher when k is 2.125*10^-7 compared to when k is 2.125*10^-2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/101.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Accuracy analyze of deep learning solution based on changing coefficie." ], "reasoning": "To verify this statement, compare the accuracy values for Thr(2) when k is 2.125*10^-7 and 2.125*10^-2. The table shows 88.69% for k = 2.125*10^-7 and 87.56% for k = 2.125*10^-2.", "statement": "The accuracy of the deep learning solution for Thr(2) is higher when k is 2.125*10^-7 compared to when k is 2.125*10^-2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Accuracy analyze of deep learning solution based on changing coefficie.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/102.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Accuracy analyze of deep learning solution based on changing coefficie." ], "reasoning": "The accuracy of the deep learning solution decreases significantly as the Damkohler number decreases from 2.6*10^-10 to 2.6*10^-15.", "description": "It is needed to note that based on the Damkohler concept, the values for Damkohler must be chosen carefully between specific intervals. In the table.V it is clear that the accuracy of deep learning solution dramatically decreases when the Damkohler number gets smaller from a specific value." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Accuracy analyze of deep learning solution based on changing coefficie.'\nProblem: 'What is the accuracy of the deep learning solution at Thr(2) when the Damkohler number is 2.6*10^-10?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/102.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Accuracy analyze of deep learning solution based on changing coefficie." ], "reasoning": "To find the accuracy at Thr(2) for a Damkohler number of 2.6*10^-10, locate the row with De = 2.6*10^-10 and the column for Thr(2). The value is 89.29%.", "question": "What is the accuracy of the deep learning solution at Thr(2) when the Damkohler number is 2.6*10^-10?", "answer": "So, the answer is {\"answer\": \"89.29%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe accuracy of the deep learning solution at Thr(1) is higher for De = 2.6*10^-7 than for De = 2.6*10^-5.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/102.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Accuracy analyze of deep learning solution based on changing coefficie." ], "reasoning": "To verify this statement, compare the values in the Thr(1) column for De = 2.6*10^-7 and De = 2.6*10^-5. The accuracy for De = 2.6*10^-7 is 82.48%, which is higher than the 66.39% for De = 2.6*10^-5.", "statement": "The accuracy of the deep learning solution at Thr(1) is higher for De = 2.6*10^-7 than for De = 2.6*10^-5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: A comparison of the best performing activity recognition models for the naturally sparse clinical room datasets. Numbers and design choices for the baselines with asterisks are quoted from [Wickramasinghe and Ranasinghe2015]. The remaining baselines are replicated following their original paper descriptions. For a fair comparison, the reported results are for the best performing configuration of interpolants and window durations for each baseline.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/103.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: A comparison of the best performing activity recognition models for the naturally sparse clinical room datasets. Numbers and design choices for the baselines with asterisks are quoted from [Wickramasinghe and Ranasinghe2015]. The remaining baselines are replicated following their original paper descriptions. For a fair comparison, the reported results are for the best performing configuration of interpolants and window durations for each baseline." ], "reasoning": "The SparseSense network outperforms all baseline models in sparse data-stream classification, with the highest F-score among all configurations.", "description": "In Table 1, we report the mean F-measure (F-scorem) as the widely adopted evaluation metric. report the highest achieving configurations in Table 1.. From the outlined results, we observe that the SparseSense network outperforms all the baseline models with a large margin in the task of sparse data-stream classification.. Taking into account the superior performance of DeepConvLSTM among the baselines in Table 1, here we only present comparisons with this model." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: A comparison of the best performing activity recognition models for the naturally sparse clinical room datasets. Numbers and design choices for the baselines with asterisks are quoted from [Wickramasinghe and Ranasinghe2015]. The remaining baselines are replicated following their original paper descriptions. For a fair comparison, the reported results are for the best performing configuration of interpolants and window durations for each baseline.'\nProblem: 'Which model achieved the highest F-score in Roomset2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/103.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: A comparison of the best performing activity recognition models for the naturally sparse clinical room datasets. Numbers and design choices for the baselines with asterisks are quoted from [Wickramasinghe and Ranasinghe2015]. The remaining baselines are replicated following their original paper descriptions. For a fair comparison, the reported results are for the best performing configuration of interpolants and window durations for each baseline." ], "reasoning": "To find the highest F-score in Roomset2, compare the F-scores of all models listed under Roomset2. The highest F-score is 96.97±0.37 achieved by SparseSense.", "question": "Which model achieved the highest F-score in Roomset2?", "answer": "So, the answer is {\"answer\": \"SparseSense\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDeepConvLSTM achieved the highest F-score in Roomset1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/103.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: A comparison of the best performing activity recognition models for the naturally sparse clinical room datasets. Numbers and design choices for the baselines with asterisks are quoted from [Wickramasinghe and Ranasinghe2015]. The remaining baselines are replicated following their original paper descriptions. For a fair comparison, the reported results are for the best performing configuration of interpolants and window durations for each baseline." ], "reasoning": "To verify this statement, compare the F-scores of all models listed under Roomset1. SparseSense achieved the highest F-score of 94.51±0.62, not DeepConvLSTM.", "statement": "DeepConvLSTM achieved the highest F-score in Roomset1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: [ATG4D] Ablation study of the different contributions at 95% recall'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/104.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: [ATG4D] Ablation study of the different contributions at 95% recall" ], "reasoning": "The ablation study in Table III shows that Rotated RoI Align outperforms simple feature indexing, and adding spatial awareness to GNNs improves performance, with the best results achieved by including relative bounding boxes and predicted probability distributions.", "description": "We show the advantages of projecting the output state of node u to the local coordinate system of node v when computing the message m(k) u→v in the ablation study in Table III.. However, we conduct ablation studies for our model at 95% recall in the next sections, which is closer to the demand that self-driving cars should meet.. Ablation Study: We first study the per-actor feature extraction mechanism of our model by comparing simple feature indexing versus Rotated RoI Align. We do this study in a model that does not contemplate interactions for better isolation. In particular, we omit our SPAGNN from Fig. 2 and use the initial trajectories predicted by CNNR as the final output. As shown in the first 2 rows of Table III, Rotated RoI Align clearly outperforms simple feature indexing. Our RRoI pooled features provide better information about the surroundings of the target vehicle since they contain features in a region spanning by 25 meters whereas the feature indexing variant consists of just accessing the feature map at the anchor location associated to the detection.. e) Graph Neural Network architectures: We evaluate several GNN architectures with different levels of spatial. awareness to demonstrate the effectiveness of our SPAGNN.. The second to third rows of Table III show that adding a standard GNN without spatial awareness does not improve performance. From the third to the forth row we observe an improvement by including the detection bounding boxes in global (G) coordinate frame as part of the state at every message passing iteration of the GNN. Then, we make these bounding boxes relative (R) to the actor receiving the message, which gives us a boost across most metrics. Finally, we add the parameters of the predicted probability distributions of the future trajectories to the message passing algorithm to recover our SPAGNN. Interestingly, the models become more certain, i.e. lower entropy (H), as we add better spatial-awareness mechanisms." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: [ATG4D] Ablation study of the different contributions at 95% recall'\nProblem: 'What is the Centroid @ 3s L2 value for the GNN model with relative bounding boxes and predicted probability distributions?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/104.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: [ATG4D] Ablation study of the different contributions at 95% recall" ], "reasoning": "To find the Centroid @ 3s L2 value for the GNN model with relative bounding boxes and predicted probability distributions, locate the row in the table where the Decoder is GNN, R is ✓, B is R, and T is R. The corresponding Centroid @ 3s L2 value is 105.", "question": "What is the Centroid @ 3s L2 value for the GNN model with relative bounding boxes and predicted probability distributions?", "answer": "So, the answer is {\"answer\": \"105\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe GNN model with global bounding boxes has a lower Heading @ 3s NLL value than the GNN model with relative bounding boxes.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/104.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: [ATG4D] Ablation study of the different contributions at 95% recall" ], "reasoning": "To verify this statement, compare the Heading @ 3s NLL values for the GNN model with global bounding boxes (G) and the GNN model with relative bounding boxes (R). The Heading @ 3s NLL value for the GNN model with global bounding boxes is -7.31, while for the GNN model with relative bounding boxes, it is -6.99. Since -7.31 is lower than -6.99, the statement is supported.", "statement": "The GNN model with global bounding boxes has a lower Heading @ 3s NLL value than the GNN model with relative bounding boxes.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: [ATG4D] Social interaction and motion forecasting metrics at 80% detection recall'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/105.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: [ATG4D] Social interaction and motion forecasting metrics at 80% detection recall" ], "reasoning": "Our model shows significant improvements in collision rate, centroid error, and heading error on the ATG4D and NUSCENES datasets, outperforming state-of-the-art methods.", "description": "Comparison Against the State-of-the-art: We benchmark our method against a variety of baselines, which can be classified in three groups.. Methods that use past trajectory as their main motion cues: SocialLSTM , Convolutional Social Pooling , CAR-Net .. we employ our object detector and a vehicle tracker consisting of an Interactive Multiple Model with Unscented Kalman Filter and Hungarian Matching. FaF , IntentNet and NMP .. Tables. and II show the cumulative collision rate, the centroid L2 error and the absolute heading error for different future horizons for the ATG4D and NUSCENES datasets respectively. The results reveal a clear improvement in detection and interaction understanding, as well as a solid gain in long-term motion forecasting. Our model substantially improves both in collision rate and centroid error on both datasets.. We obtain the lowest heading error on ATG4D. Note that we perform this comparison at 80% recall at IoU 0.5 in ATG4D because some of the baselines do not reach higher recalls.. Our approach resulted in significant improvements over the state-of-theart on the challenging ATG4D and NUSCENES autonomous driving datasets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: [ATG4D] Social interaction and motion forecasting metrics at 80% detection recall'\nProblem: 'Which model has the lowest collision rate for the 0-1s horizon?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/105.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: [ATG4D] Social interaction and motion forecasting metrics at 80% detection recall" ], "reasoning": "To find the model with the lowest collision rate for the 0-1s horizon, look at the 'Col. (%) 0-1s' column and identify the model with the smallest value. The SpAGNN (Ours) model has the lowest collision rate of 0.03%.", "question": "Which model has the lowest collision rate for the 0-1s horizon?", "answer": "So, the answer is {\"answer\": \"SpAGNN (Ours)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe D+T+CSP model has the highest L2 x,y error at 3s.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/105.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: [ATG4D] Social interaction and motion forecasting metrics at 80% detection recall" ], "reasoning": "To verify this statement, look at the 'L2 x,y (cm) 3s' column and compare the values for all models. The D+T+CSP model has an L2 x,y error of 282 cm at 3s, which is not the highest among all models.", "statement": "The D+T+CSP model has the highest L2 x,y error at 3s.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: [nuScenes] Social interaction and motion forecasting metrics at 60% recall'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/106.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: [nuScenes] Social interaction and motion forecasting metrics at 60% recall" ], "reasoning": "The SpAGNN model achieves the lowest collision rate and heading error across all time intervals, indicating superior performance compared to other models.", "description": "we get results that are on par with the best baseline in NUSCENES.. In NUSCENES we use 60% recall" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: [nuScenes] Social interaction and motion forecasting metrics at 60% recall'\nProblem: 'Which model has the lowest collision rate in the 0-1s interval and what is its corresponding L2 x,y (cm) error in the 3s interval?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/106.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: [nuScenes] Social interaction and motion forecasting metrics at 60% recall" ], "reasoning": "The SpAGNN model has the lowest collision rate of 0.25% in the 0-1s interval. Its corresponding L2 x,y (cm) error in the 3s interval is 145 cm.", "question": "Which model has the lowest collision rate in the 0-1s interval and what is its corresponding L2 x,y (cm) error in the 3s interval?", "answer": "So, the answer is {\"answer\": \"145 cm\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe E2E CAR-Net model has the lowest heading error in the 0s interval.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/106.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: [nuScenes] Social interaction and motion forecasting metrics at 60% recall" ], "reasoning": "The table shows that the E2E CAR-Net model has a heading error of 2.84 degrees in the 0s interval, which is the lowest among all models.", "statement": "The E2E CAR-Net model has the lowest heading error in the 0s interval.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: A quantitative comparison using various image generation scores. In order to support a fair comparison, our model does not use location attributes and employs random appearance attributes.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/107.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: A quantitative comparison using various image generation scores. In order to support a fair comparison, our model does not use location attributes and employs random appearance attributes." ], "reasoning": "The table compares different methods and resolutions using inception, FID, and classification accuracy scores, ensuring fair comparison by using zero location attributes and randomly sampled appearance attributes.", "description": "Tab. 1 compares our method with the baselines and the real test images using the inception, FID, and classification accuracy scores. We make sure not to use information that the baseline method of is not using and use zero location attributes and appearance attributes that are randomly sam. pled (see 3.2). employs bounding boxes and not masks. However, we follow the same comparison (to masked based methods) given in their paper." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: A quantitative comparison using various image generation scores. In order to support a fair comparison, our model does not use location attributes and employs random appearance attributes.'\nProblem: 'What is the FID score for the 256x256 resolution using the GT Layout method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/107.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: A quantitative comparison using various image generation scores. In order to support a fair comparison, our model does not use location attributes and employs random appearance attributes." ], "reasoning": "To find the FID score for the 256x256 resolution using the GT Layout method, locate the row with 256x256 resolution and GT Layout method, then check the FID column.", "question": "What is the FID score for the 256x256 resolution using the GT Layout method?", "answer": "So, the answer is {\"answer\": \"65.2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe inception score for the 128x128 resolution using the Ours method is higher than that of the GT Layout method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/107.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: A quantitative comparison using various image generation scores. In order to support a fair comparison, our model does not use location attributes and employs random appearance attributes." ], "reasoning": "Compare the inception scores for the 128x128 resolution using the Ours method (10.4±0.4) and the GT Layout method (12.5±0.3). The statement is refuted as 10.4 is not higher than 12.5.", "statement": "The inception score for the 128x128 resolution using the Ours method is higher than that of the GT Layout method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: The diversity score of [27]. The results of [9] are computed by us and are considerably higher than those reported for the same method by [28]. The results of [28] are from their paper.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/108.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The diversity score of [27]. The results of [9] are computed by us and are considerably higher than those reported for the same method by [28]. The results of [28] are from their paper." ], "reasoning": "The method using the random vector zi produces higher diversity compared to the baseline methods, and varying location or appearance attributes further increases diversity.", "description": "Tab. 2 reports the diversity of our method in comparison to the two baseline methods. The source of stochasticity we employ (the random vector zi used in Eq. 2) produces a higher diversity than the two baseline methods (which also include a random element), even when not changing the location vector l1 or appearance attributes ai. Varying either one of these factors adds a sizable amount of diversity. In the experiments of the table, the location attributes, when varied, are sampled using per-class Gaussian distribution that fit to the location vectors of the training set images." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: The diversity score of [27]. The results of [9] are computed by us and are considerably higher than those reported for the same method by [28]. The results of [28] are from their paper.'\nProblem: 'Which method and resolution combination produced the highest diversity?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/108.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The diversity score of [27]. The results of [9] are computed by us and are considerably higher than those reported for the same method by [28]. The results of [28] are from their paper." ], "reasoning": "To find the highest diversity, compare the diversity values across all methods and resolutions. The highest value is 0.67±0.05 for 'Ours full method' at 256x256 resolution.", "question": "Which method and resolution combination produced the highest diversity?", "answer": "So, the answer is {\"answer\": \"Ours full method at 256x256\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe method 'Ours zeroed location attributes' at 64x64 resolution produced a higher diversity than 'Ours fixed appearance attributes' at 256x256 resolution.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/108.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The diversity score of [27]. The results of [9] are computed by us and are considerably higher than those reported for the same method by [28]. The results of [28] are from their paper." ], "reasoning": "Comparing the diversity values, 'Ours zeroed location attributes' at 64x64 resolution has a diversity of 0.35±0.01, while 'Ours fixed appearance attributes' at 256x256 resolution has a diversity of 0.62±0.05. Therefore, the statement is refuted.", "statement": "The method 'Ours zeroed location attributes' at 64x64 resolution produced a higher diversity than 'Ours fixed appearance attributes' at 256x256 resolution.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3. Comparison of predicted bounding boxes'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/109.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Comparison of predicted bounding boxes" ], "reasoning": "The table shows that our method, even without location attributes, outperforms Johnson's method in bounding box placement accuracy, and further improves with location attributes.", "description": "Tab. 3 presents a comparison with the method of , regarding the placement accuracy of the bounding boxes. Even when not using the location attribute vectors li, our bounding box placement better matches the test images. As expected, adding the location vectors improves the results." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3. Comparison of predicted bounding boxes'\nProblem: 'How does the IoU score of our method with location attributes compare to Johnson's method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/109.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Comparison of predicted bounding boxes" ], "reasoning": "The IoU score for our method with location attributes is 0.61, while Johnson's method has an IoU score of 0.37. Therefore, our method with location attributes has a higher IoU score.", "question": "How does the IoU score of our method with location attributes compare to Johnson's method?", "answer": "So, the answer is {\"answer\": \"0.61\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur method without location attributes has a higher R@0.3 score than Johnson's method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/109.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Comparison of predicted bounding boxes" ], "reasoning": "The table shows that our method without location attributes has an R@0.3 score of 0.62, while Johnson's method has an R@0.3 score of 0.52. Therefore, the statement is supported.", "statement": "Our method without location attributes has a higher R@0.3 score than Johnson's method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4. User study results'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/110.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. User study results" ], "reasoning": "The user study results show that the proposed method significantly outperforms the baseline method in terms of realism, adherence to the scene graph, and the ratio of observed objects.", "description": "User study Following , we perform a user study to compare with the baseline method the realism of the generated image, the adherence to the scene graph, as well as to verify that the objects in the scene graph appear in the output image. The user study involved n = 20 computer graphics and computer vision students. Each student was shown the output images for 30 random test scene-graphs from the COCO-stuff dataset and was asked to select the preferable method, according to two criteria: \"which image is more realistic\" and \"which image better reflects the scene graph\". In addition, the list of objects in the scene graph was presented, and the users were asked to count the number of objects that appear in each of the images. The two images, one for the method of and one for our method, were presented in a random order. To allow for a fair comparison, the appearance archetypes were selected at random, the location vectors were set to zero for all objects, and we have used images from our 64 × 64 resolution model. The results, listed in Tab. 4, show that our method significantly outperforms the baseline method in all aspects tested." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4. User study results'\nProblem: 'What is the percentage difference in the ratio of observed objects among all COCO objects between the baseline method and the proposed method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/110.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. User study results" ], "reasoning": "The ratio of observed objects among all COCO objects for the baseline method is 27.31%, and for the proposed method, it is 45.38%. The percentage difference is calculated as 45.38% - 27.31%.", "question": "What is the percentage difference in the ratio of observed objects among all COCO objects between the baseline method and the proposed method?", "answer": "So, the answer is {\"answer\": \"18.07%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed method has a better adherence to the scene graph compared to the baseline method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/110.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. User study results" ], "reasoning": "The table shows that the adherence to the scene graph for the proposed method is 80.7%, while for the baseline method, it is 19.3%. This indicates that the proposed method has better adherence to the scene graph.", "statement": "The proposed method has a better adherence to the scene graph compared to the baseline method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Ablation Study'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/111.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation Study" ], "reasoning": "Removing each loss from the method results in noticeable degradation, with the perceptual loss and mask discriminator being the most detrimental.", "description": "Ablation analysis The relative importance of the various losses is approximated, by removing it from the method and training the 128x128 model. For this study, we use both the inception and the FID scores. The results are reported in Tab. 5. As can be seen, removing each of the losses results in a noticeable degradation. Removing the perceptual loss is extremely detrimental. Out of the three discriminators, removing the mask discriminator is the most damaging, since, due to the random component zi, we do not have a direct loss on the mask. Finally, replacing our image discriminator with the one in , results in some loss of accuracy." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Ablation Study'\nProblem: 'Which loss removal results in the highest FID score degradation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/111.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation Study" ], "reasoning": "By examining the table, the highest FID score is observed when the LD-mask is removed, resulting in an FID score of 183.6.", "question": "Which loss removal results in the highest FID score degradation?", "answer": { "answer": "No LD-mask" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRemoving the perceptual loss results in the highest degradation in inception score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/111.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation Study" ], "reasoning": "The table shows that removing the perceptual loss results in an inception score of 6.2, which is not the lowest among all the models listed.", "statement": "Removing the perceptual loss results in the highest degradation in inception score.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: General search space for FFNNs'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/112.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: General search space for FFNNs" ], "reasoning": "Table 2 outlines the hyperparameters for FFNNs, including their lower and upper bounds and step sizes. The number of layers ranges from one to six with a step size of one.", "description": "Table 2 shows the hyperparameters we considered for FFNNs. For each hyperparameter, we show the lower bound, upper bound, and step size. For the number of layers, we considered one to six hidden layers in the architecture search space. with a step size of one." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: General search space for FFNNs'\nProblem: 'What is the range of neurons per layer considered in the hyperparameter search?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/112.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: General search space for FFNNs" ], "reasoning": "The table shows that the lower bound for neurons per layer is 50 and the upper bound is 600, with a step size of 25.", "question": "What is the range of neurons per layer considered in the hyperparameter search?", "answer": "So, the answer is {\"answer\": \"50 to 600\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe step size for DR ratio is 0.1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/112.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: General search space for FFNNs" ], "reasoning": "The table indicates that the step size for DR ratio is indeed 0.1.", "statement": "The step size for DR ratio is 0.1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Highest test accuracy comparison on small- to medium-size datasets (the highest number is highlighted)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/113.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Highest test accuracy comparison on small- to medium-size datasets (the highest number is highlighted)" ], "reasoning": "Table 3 shows the test accuracy results for different datasets using various methods, including STEERAGE with global search (GS) and both global and local search (GS+LS) modules.", "description": "Table 3 shows the test accuracy results. There are two rows associated with STEERAGE. The first one shows the result obtained by just using the global search module, and the second one when both the global and local search modules are used." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Highest test accuracy comparison on small- to medium-size datasets (the highest number is highlighted)'\nProblem: 'What is the test accuracy for the HAR dataset when using both the global and local search modules in STEERAGE?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/113.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Highest test accuracy comparison on small- to medium-size datasets (the highest number is highlighted)" ], "reasoning": "To find the test accuracy for the HAR dataset using both the global and local search modules in STEERAGE, locate the row labeled 'STEERAGE (GS+LS)' and then find the corresponding value under the 'HAR' column.", "question": "What is the test accuracy for the HAR dataset when using both the global and local search modules in STEERAGE?", "answer": "So, the answer is {\"answer\": \"96.43%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe test accuracy for the Letter dataset using only the global search module in STEERAGE is higher than 97%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/113.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Highest test accuracy comparison on small- to medium-size datasets (the highest number is highlighted)" ], "reasoning": "To verify this statement, locate the row labeled 'STEERAGE (GS)' and then find the corresponding value under the 'Letter' column. The value is 97.20%, which is higher than 97%.", "statement": "The test accuracy for the Letter dataset using only the global search module in STEERAGE is higher than 97%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VII: Comparison of results for LeNet-5 (the size of the image is shown in parentheses for STEERAGE)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/114.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: Comparison of results for LeNet-5 (the size of the image is shown in parentheses for STEERAGE)" ], "reasoning": "The most accurate architecture based on the LeNet-5 model has an error rate of 0.66% and uses both global and local search modules with images of size 21 × 21, having 7.2k parameters. Another model with global+local search has 5.2k parameters and an error rate of 0.72%.", "description": "Table 7 presents the results. The most accurate architecture based on the LeNet-5 model has an error rate of 0.66%. It is based on using both global and local search modules and corresponds to images of size 21 × 21. This model only has 7.2k parameters. Moreover, another model based on global+local search only has 5.2k parameters, with an error rate of 0.72%. These two results are highlighted in bold." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VII: Comparison of results for LeNet-5 (the size of the image is shown in parentheses for STEERAGE)'\nProblem: 'What is the error rate of the model with the fewest parameters that uses both global and local search modules?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/114.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: Comparison of results for LeNet-5 (the size of the image is shown in parentheses for STEERAGE)" ], "reasoning": "The model with the fewest parameters that uses both global and local search modules has 5.0k parameters and an error rate of 1.2%.", "question": "What is the error rate of the model with the fewest parameters that uses both global and local search modules?", "answer": "So, the answer is {\"answer\": \"1.2%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model with the highest error rate uses images of size 14 × 14.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/114.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: Comparison of results for LeNet-5 (the size of the image is shown in parentheses for STEERAGE)" ], "reasoning": "The model with the highest error rate (4.96%) uses images of size 14 × 14, as indicated in the table.", "statement": "The model with the highest error rate uses images of size 14 × 14.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Mean and median normalized scores.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/115.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Mean and median normalized scores." ], "reasoning": "The median performance across 20 games increased from 63.13% to 118.02%, and the mean performance increased from 92.48% to 139.75%.", "description": "Table 1: Mean and median normalized scores.. We also give the summary statistics in terms of mean and median score in Table 1. Compared to Adam, the median performance across 20 games increases from 63.13% to 118.02% and the mean performance increases from 92.48% to 139.75%. Noteworthy examples include Seaquest (from 27.42% to 267.94%), Gopher (from 53.22% to 145.42%)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Mean and median normalized scores.'\nProblem: 'What is the percentage increase in the mean performance from Double DQN to SVR-DQN?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/115.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Mean and median normalized scores." ], "reasoning": "The mean performance for Double DQN is 92.48% and for SVR-DQN is 139.75%. The percentage increase is calculated as ((139.75 - 92.48) / 92.48) * 100.", "question": "What is the percentage increase in the mean performance from Double DQN to SVR-DQN?", "answer": "So, the answer is {\"answer\": \"51.12%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe median performance of SVR-DQN is almost double that of Double DQN.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/115.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Mean and median normalized scores." ], "reasoning": "The median performance of Double DQN is 63.13%, and the median performance of SVR-DQN is 118.02%. 118.02% is almost double 63.13%.", "statement": "The median performance of SVR-DQN is almost double that of Double DQN.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Loss Selection. We explore the influence of different training losses and evaluation metrics on 3 models with different degrees of structured guidance. For each class, we show validation scores for all pairwise combinations of 5 training losses (rows) and the same 5 evaluation metrics (columns). The best model for each evaluation metric is shown in bold. SSIM/MS-SSIM are expressed as dissimilarities (Eq. 7).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/116.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Loss Selection. We explore the influence of different training losses and evaluation metrics on 3 models with different degrees of structured guidance. For each class, we show validation scores for all pairwise combinations of 5 training losses (rows) and the same 5 evaluation metrics (columns). The best model for each evaluation metric is shown in bold. SSIM/MS-SSIM are expressed as dissimilarities (Eq. 7)." ], "reasoning": "The table shows validation scores for different training losses and evaluation metrics across three models with varying structured guidance, highlighting the best model for each metric.", "description": "Table 1: Loss Selection. We explore the influence of different training losses and evaluation metrics on 3 models with different degrees of structured guidance. For each class, we show validation scores for all pairwise combinations of 5 training losses (rows) and the same 5 evaluation metrics (columns). The best model for each evaluation metric is shown in bold. SSIM/MS-SSIM are expressed as dissimilarities (Eq. (7))." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Loss Selection. We explore the influence of different training losses and evaluation metrics on 3 models with different degrees of structured guidance. For each class, we show validation scores for all pairwise combinations of 5 training losses (rows) and the same 5 evaluation metrics (columns). The best model for each evaluation metric is shown in bold. SSIM/MS-SSIM are expressed as dissimilarities (Eq. 7).'\nProblem: 'Which training loss and evaluation metric combination resulted in the lowest SSIM score for the 'none' guidance model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/116.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Loss Selection. We explore the influence of different training losses and evaluation metrics on 3 models with different degrees of structured guidance. For each class, we show validation scores for all pairwise combinations of 5 training losses (rows) and the same 5 evaluation metrics (columns). The best model for each evaluation metric is shown in bold. SSIM/MS-SSIM are expressed as dissimilarities (Eq. 7)." ], "reasoning": "To find the lowest SSIM score for the 'none' guidance model, look at the 'none' rows under the SSIM column. The lowest SSIM score is 0.1378, achieved with the SSIM training loss.", "question": "Which training loss and evaluation metric combination resulted in the lowest SSIM score for the 'none' guidance model?", "answer": "So, the answer is {\"answer\": \"SSIM training loss\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'partial' guidance model with L2 training loss has the lowest L2 evaluation metric score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/116.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Loss Selection. We explore the influence of different training losses and evaluation metrics on 3 models with different degrees of structured guidance. For each class, we show validation scores for all pairwise combinations of 5 training losses (rows) and the same 5 evaluation metrics (columns). The best model for each evaluation metric is shown in bold. SSIM/MS-SSIM are expressed as dissimilarities (Eq. 7)." ], "reasoning": "To verify this, check the L2 evaluation metric scores for the 'partial' guidance model. The L2 score for L2 training loss is 0.0056, which is not the lowest. The lowest L2 score for 'partial' guidance is 0.0040 with SSIM training loss.", "statement": "The 'partial' guidance model with L2 training loss has the lowest L2 evaluation metric score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Baseline Comparison. We show a quantitative comparison of our approach to baseline methods. Performance is reported under the assumption of both known (‘with’) and unknown (‘w/o’) source illumination. All models have been trained with the SSIM loss.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/117.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Baseline Comparison. We show a quantitative comparison of our approach to baseline methods. Performance is reported under the assumption of both known (‘with’) and unknown (‘w/o’) source illumination. All models have been trained with the SSIM loss." ], "reasoning": "The table compares the performance of various models with and without source illumination guidance using multiple evaluation metrics.", "description": "Table 2: Baseline Comparison. We show a quantitative comparison of our approach to baseline methods. Performance is reported under the assumption of both known ('with') and unknown ('w/o') source illumination. All models have been trained with the SSIM loss." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Baseline Comparison. We show a quantitative comparison of our approach to baseline methods. Performance is reported under the assumption of both known (‘with’) and unknown (‘w/o’) source illumination. All models have been trained with the SSIM loss.'\nProblem: 'Which model has the lowest Evaluation Metric L1 value when the source illumination is known?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/117.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Baseline Comparison. We show a quantitative comparison of our approach to baseline methods. Performance is reported under the assumption of both known (‘with’) and unknown (‘w/o’) source illumination. All models have been trained with the SSIM loss." ], "reasoning": "To find the model with the lowest Evaluation Metric L1 value when the source illumination is known, look at the 'with' rows under the 'Evaluation Metric L1' column and identify the smallest value.", "question": "Which model has the lowest Evaluation Metric L1 value when the source illumination is known?", "answer": "So, the answer is {\"answer\": \"PMS\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model with partial guidance and unknown source illumination has a higher Evaluation Metric SSIM value than the model with full guidance and unknown source illumination.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/117.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Baseline Comparison. We show a quantitative comparison of our approach to baseline methods. Performance is reported under the assumption of both known (‘with’) and unknown (‘w/o’) source illumination. All models have been trained with the SSIM loss." ], "reasoning": "Compare the Evaluation Metric SSIM values for the models with partial guidance and full guidance under the 'w/o' source illumination category. The partial guidance model has an SSIM value of .1801, which is higher than the full guidance model's SSIM value of .1763.", "statement": "The model with partial guidance and unknown source illumination has a higher Evaluation Metric SSIM value than the model with full guidance and unknown source illumination.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Performance on Yale Faces data sets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/118.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Performance on Yale Faces data sets." ], "reasoning": "As K increases, the percentage of data needed increases, and the performance results are shown in the last section of Table III.", "description": "From the results shown in Table III, we see that the percentage of data needed goes up as K increases.. The performance results are shown in the last section of Table III." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Performance on Yale Faces data sets.'\nProblem: 'What is the percentage of data needed for the SCAL strategy when K=8?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/118.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Performance on Yale Faces data sets." ], "reasoning": "To find the percentage of data needed for the SCAL strategy when K=8, locate the SCAL row and the column corresponding to K=8.", "question": "What is the percentage of data needed for the SCAL strategy when K=8?", "answer": "So, the answer is {\"answer\": \"63.48%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe MinMargin strategy requires more than 80% of data when K=10.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/118.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Performance on Yale Faces data sets." ], "reasoning": "To verify this statement, locate the MinMargin row and the column corresponding to K=10. The value is 99.84%, which is more than 80%.", "statement": "The MinMargin strategy requires more than 80% of data when K=10.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Identical domain and domain shift accuracies for 5-way 5-shot classification using a ResNet-10 backbone and mini-ImageNet and CUB datasets. The best-performing method is highlighted.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/119.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Identical domain and domain shift accuracies for 5-way 5-shot classification using a ResNet-10 backbone and mini-ImageNet and CUB datasets. The best-performing method is highlighted." ], "reasoning": "Subspace networks outperform other distance-metric learning methods in both mini-ImageNet → CUB and CUB → mini-ImageNet settings, showing better generalization across datasets.", "description": "(See Table 1 for detailed statistics and 95% confidence intervals.). Table 1 and Figure 3 present our results in the context of existing state-of-the-art distance-metric learning based methods.. The intra-domain difference between classes is higher in mini-ImageNet than in CUB (Chen et al. ), which is reflected by a drop of the test accuracies of all methods. It can also be seen that when the domain difference between the training and test stage classes increases (left to right in Figure 3) the performance of all few-shot algorithms lowers.. As expected, the classification accuracy lowers slightly when going from the mini-ImageNet → CUB to the CUB → mini-ImageNet setting. Interestingly though, it does not decrease substantially. This shows that the different feature embeddings generalize well, at least across these two different datasets. Compared to other distance-metric learning based methods, subspace networks show significantly better performance in both the mini-ImageNet → CUB setting and CUB → mini-ImageNet setting." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Identical domain and domain shift accuracies for 5-way 5-shot classification using a ResNet-10 backbone and mini-ImageNet and CUB datasets. The best-performing method is highlighted.'\nProblem: 'Which method shows the highest classification accuracy in the CUB → mini-ImageNet setting?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/119.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Identical domain and domain shift accuracies for 5-way 5-shot classification using a ResNet-10 backbone and mini-ImageNet and CUB datasets. The best-performing method is highlighted." ], "reasoning": "By comparing the classification accuracies in the CUB → mini-ImageNet column, SubspaceNet has the highest accuracy of 56.66 ± 0.68.", "question": "Which method shows the highest classification accuracy in the CUB → mini-ImageNet setting?", "answer": "So, the answer is {\"answer\": \"SubspaceNet\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nProtoNet has the highest accuracy in the mini-ImageNet → CUB setting.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/119.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Identical domain and domain shift accuracies for 5-way 5-shot classification using a ResNet-10 backbone and mini-ImageNet and CUB datasets. The best-performing method is highlighted." ], "reasoning": "By comparing the classification accuracies in the mini-ImageNet → CUB column, SubspaceNet has the highest accuracy of 62.71 ± 0.71, not ProtoNet.", "statement": "ProtoNet has the highest accuracy in the mini-ImageNet → CUB setting.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Experiment results for REDD data.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/120.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experiment results for REDD data." ], "reasoning": "Tables 2 and 3 compare the performance of previous works and SGN on the REDD and UK-DALE datasets, highlighting the best performing algorithms and instances where SGN outperforms previous works.", "description": "Table 2 and 3 show the performance of the previous works and SGN for the REDD and UK-DALE dataset.. In the two tables, the bold font denotes the best performing algorithm for each appliance, and the shade denotes that SGN has outperformed the best of the previous works." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Experiment results for REDD data.'\nProblem: 'Which model has the lowest MAE for the dishwasher?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/120.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experiment results for REDD data." ], "reasoning": "To find the model with the lowest MAE for the dishwasher, look at the 'Dish washer' column under the 'MAE' metric and identify the smallest value. The smallest value is 14.97, which corresponds to the SGN model.", "question": "Which model has the lowest MAE for the dishwasher?", "answer": "So, the answer is {\"answer\": \"SGN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSGN-sp has the highest average improvement in MAE.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/120.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experiment results for REDD data." ], "reasoning": "To verify this statement, compare the 'Average improvement' percentages under the 'MAE' metric. SGN-sp has an average improvement of 18.36%, which is the highest.", "statement": "SGN-sp has the highest average improvement in MAE.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Results achieved by our SummaryNet model for different optical flow grid sizes. We choose to use a grid size of 15×15 for the remaining experiments in this paper. The values in this table correspond to the TvSum50 dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/121.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results achieved by our SummaryNet model for different optical flow grid sizes. We choose to use a grid size of 15×15 for the remaining experiments in this paper. The values in this table correspond to the TvSum50 dataset." ], "reasoning": "The grid size in dense optical flow computation affects accuracy, with a trade-off between noise and signal. The optimal grid size for best performance is 15 × 15.", "description": "From Table 1, we see that grid size in the dense optical flow computation has a notable effect on accuracy. There is a trade-off between having too small a grid size (which would result in too much noise in the optical flow videos), and too large a grid size (which would result in not enough signal being present in the optical flow videos). We choose the grid size value that results in the best performance (i.e. 15 × 15) for all subsequent experiments." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Results achieved by our SummaryNet model for different optical flow grid sizes. We choose to use a grid size of 15×15 for the remaining experiments in this paper. The values in this table correspond to the TvSum50 dataset.'\nProblem: 'What is the F-score (%) for the grid size that results in the best performance in dense optical flow computation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/121.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results achieved by our SummaryNet model for different optical flow grid sizes. We choose to use a grid size of 15×15 for the remaining experiments in this paper. The values in this table correspond to the TvSum50 dataset." ], "reasoning": "The description states that the grid size of 15 × 15 results in the best performance. According to the table, the F-score for the 15 × 15 grid size is 71.7%.", "question": "What is the F-score (%) for the grid size that results in the best performance in dense optical flow computation?", "answer": "So, the answer is {\"answer\": \"71.7\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe grid size of 8 × 8 results in the highest F-score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/121.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results achieved by our SummaryNet model for different optical flow grid sizes. We choose to use a grid size of 15×15 for the remaining experiments in this paper. The values in this table correspond to the TvSum50 dataset." ], "reasoning": "The table shows that the grid size of 15 × 15 has the highest F-score of 71.7%, while the grid size of 8 × 8 has an F-score of 70.9%.", "statement": "The grid size of 8 × 8 results in the highest F-score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparison of video summarisation methods using F-score (%) on TvSum50 dataset. These results clearly show the advantage of using spatio-temporal features.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/122.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of video summarisation methods using F-score (%) on TvSum50 dataset. These results clearly show the advantage of using spatio-temporal features." ], "reasoning": "I3D-based features provide better video representation than ResNet features, as shown by higher accuracy scores, with SummaryNet achieving the highest combined score.", "description": "In Table 2, we see the results of baseline-ResNet is more than 3% lower than the results obtained by who used GoogLeNet features with a similar model architecture. In this table we also clearly see the advantage of using spatiotemporal features.. Since we are using a two-stream network, we get two separate predictions for a video. The results are combined and the average is taken as the final prediction. These scores are represented by the last column in the table.. Our proposed method, SummaryNet is shown in the last row of Table 2.. In Table 2, we see that I3D-based features serve as a better, more rich representation of the videos than ResNet features. This is evident from the respective accuracies of both approaches–with the baseline I3D approach achieving an accuracy of 68.64%, compared to the 50.48% from the ResNet baseline approach.. This large difference can be attributed to the fact that the ResNet features only incorporate 2D spatial information into the representation, whereas the I3D features incorporate 3D spatial information (through the RGB stream), as well as explicitly modelling temporal information (through the optical flow stream). This shows that separately modelling spatial / appearance and temporal / motion information in videos is useful.. We use a simple averaging approach whereby the RGB and optical flow outputs of the sigmoid regression network are simply averaged. This simple scheme is likely a suboptimal way to fuse the information from the two streams and more complex information fusion methods can likely yield higher performance. The ConvLSTM I3D model improves upon the previous, standard ConvNet model on all metrics (RGB, optical flow, and combined).. We attribute this performance increase to the encoder-decoder model, which is able to effectively capture the most salient information from the I3D representations, and remove any noise from the core signal that could potentially be detrimental in the sigmoid network's learning process.. Thus, the change points supplied by using the I3D features clearly demonstrate its superiority over using simple 2D spatial features, such as those from ResNet." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparison of video summarisation methods using F-score (%) on TvSum50 dataset. These results clearly show the advantage of using spatio-temporal features.'\nProblem: 'What is the combined RGB and optical flow accuracy of the ConvLSTM method using I3D features?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/122.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of video summarisation methods using F-score (%) on TvSum50 dataset. These results clearly show the advantage of using spatio-temporal features." ], "reasoning": "The table shows the combined RGB and optical flow accuracy for different methods using I3D features. The ConvLSTM method has a combined accuracy of 69.10%.", "question": "What is the combined RGB and optical flow accuracy of the ConvLSTM method using I3D features?", "answer": "So, the answer is {\"answer\": \"69.10\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ConvNet method using I3D features has a higher combined accuracy than the Baseline method using I3D features.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/122.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of video summarisation methods using F-score (%) on TvSum50 dataset. These results clearly show the advantage of using spatio-temporal features." ], "reasoning": "The table shows that the ConvNet method using I3D features has a combined accuracy of 68.40%, while the Baseline method using I3D features has a combined accuracy of 68.64%. Therefore, the statement is refuted.", "statement": "The ConvNet method using I3D features has a higher combined accuracy than the Baseline method using I3D features.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison of video summarisation methods using F-score (%) on SumMe dataset. These results clearly show the advantage of using spatio-temporal features.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/123.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of video summarisation methods using F-score (%) on SumMe dataset. These results clearly show the advantage of using spatio-temporal features." ], "reasoning": "The I3D-based models outperform the ResNet model, with SummaryNet performing the best due to the combination of RGB and Optical Flow features.", "description": "In Table 3, we see a similar trend to that of Table 2. The baseline I3D model outperforms the baseline ResNet model, due to the incorporation of 3D spatial and temporal information into the representations. The trends follows that of the TvSum50 dataset for the other I3D-based models as well, with the combined SummaryNet model performing best." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Comparison of video summarisation methods using F-score (%) on SumMe dataset. These results clearly show the advantage of using spatio-temporal features.'\nProblem: 'Which model performs best when combining RGB and Optical Flow features?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/123.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of video summarisation methods using F-score (%) on SumMe dataset. These results clearly show the advantage of using spatio-temporal features." ], "reasoning": "The table shows the performance of different models with various features. The highest value in the RGB+Opt. Flow column indicates the best performing model.", "question": "Which model performs best when combining RGB and Optical Flow features?", "answer": "So, the answer is {\"answer\": \"SummaryNet\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ConvNet model outperforms the Baseline I3D model when using Optical Flow features.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/123.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of video summarisation methods using F-score (%) on SumMe dataset. These results clearly show the advantage of using spatio-temporal features." ], "reasoning": "The table shows the performance of the ConvNet and Baseline I3D models with Optical Flow features. ConvNet has a score of 41.77, while Baseline I3D has a score of 41.45.", "statement": "The ConvNet model outperforms the Baseline I3D model when using Optical Flow features.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Comparison of video summarisation methods using F-score (%). The best results are shown in dark blue and second best is shown in light blue.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/124.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of video summarisation methods using F-score (%). The best results are shown in dark blue and second best is shown in light blue." ], "reasoning": "SummaryNet significantly outperforms other methods on the TvSum50 dataset with a score of 72.02, while achieving competitive results on the SumMe dataset with a score of 44.6.", "description": "In Table 4, we compare video summarisation methods. All the methods shown in this table use deep learning, often leveraging deep spatial features (e.g. GoogLeNet features). For the TvSum50 dataset, SummaryNet outperforms the state-of-the-art by more than 11%. This significant increase in accuracy is attractive since it demonstrates the power of SummaryNet. For the SumMe dataset we achieve competitive results, where the state-of-the-art is around 6.7% better than SummaryNet. However, SummaryNet outperforms on TvSum50 dataset by a large margin of more than 13%.. Overall, SummaryNet achieves the state-of-the-art performance on TvSum50 dataset, and is competitive with state-of-the-art for the SumMe dataset." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Comparison of video summarisation methods using F-score (%). The best results are shown in dark blue and second best is shown in light blue.'\nProblem: 'What is the performance difference between SummaryNet and the second-best method on the TvSum50 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/124.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of video summarisation methods using F-score (%). The best results are shown in dark blue and second best is shown in light blue." ], "reasoning": "SummaryNet achieves a score of 72.02 on the TvSum50 dataset. The second-best method on the TvSum50 dataset has a score of 61. The performance difference is 72.02 - 61.", "question": "What is the performance difference between SummaryNet and the second-best method on the TvSum50 dataset?", "answer": { "answer": "11.02" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSummaryNet achieves the highest score on the SumMe dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/124.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of video summarisation methods using F-score (%). The best results are shown in dark blue and second best is shown in light blue." ], "reasoning": "The highest score on the SumMe dataset is 51.3, achieved by another method. SummaryNet achieves a score of 44.6, which is not the highest.", "statement": "SummaryNet achieves the highest score on the SumMe dataset.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: RMSE Analysis'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/125.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: RMSE Analysis" ], "reasoning": "The Y-coordinate RMSE column shows the error in the Y-coordinate between simulated and true trajectories.", "description": "The Y -coordinate RMSE column in Table II denotes the displacement in Y -coordinate between simulated trajectories and their corresponding true trajectories." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: RMSE Analysis'\nProblem: 'Which model has the lowest Y-coordinate RMSE at 10 seconds?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/125.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: RMSE Analysis" ], "reasoning": "To find the model with the lowest Y-coordinate RMSE at 10 seconds, we need to look at the Y RMSE column and identify the smallest value. The smallest value is 1.40, which corresponds to the EGCN model.", "question": "Which model has the lowest Y-coordinate RMSE at 10 seconds?", "answer": "So, the answer is {\"answer\": \"EGCN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe GAT model has the highest Y-coordinate RMSE at 10 seconds.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/125.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: RMSE Analysis" ], "reasoning": "To verify this statement, we need to compare the Y-coordinate RMSE values for all models. The highest Y-coordinate RMSE value is 4.13, which corresponds to the GAT model.", "statement": "The GAT model has the highest Y-coordinate RMSE at 10 seconds.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Jerk Sign Inversions Per Trajectory'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/126.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Jerk Sign Inversions Per Trajectory" ], "reasoning": "The table shows that original GCN models often produce poor acceleration predictions, leading to unrealistic states, as evidenced by higher negative headway occurrences.", "description": "Table III shows the number of negative headway occurrences over number of simulated trajectories for all models.. Consistent with RMSE analysis, the results. from Table III demonstrates that original GCN models often produce poor acceleration predictions which lead to unrealistic states." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Jerk Sign Inversions Per Trajectory'\nProblem: 'Which model has the highest negative headway occurrence rate?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/126.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Jerk Sign Inversions Per Trajectory" ], "reasoning": "By examining the 'Negative Headway Occurrence Rate' column, the GAT model has the highest rate at 0.27.", "question": "Which model has the highest negative headway occurrence rate?", "answer": "So, the answer is {\"answer\": \"GAT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe EGCN model has a negative headway occurrence rate of 0.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/126.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Jerk Sign Inversions Per Trajectory" ], "reasoning": "The table shows that the EGCN model has a negative headway occurrence rate of 0, which supports the statement.", "statement": "The EGCN model has a negative headway occurrence rate of 0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Quantitative performance of different single image point cloud generation methods on 13 major categories of ShapeNet. “↑” indicates higher is better. “↓” specifies the opposition. Best performance is highlighted in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/127.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Quantitative performance of different single image point cloud generation methods on 13 major categories of ShapeNet. “↑” indicates higher is better. “↓” specifies the opposition. Best performance is highlighted in bold." ], "reasoning": "PCDNet variants outperform other methods significantly, with the simplest model (FC) achieving twice better CD scores than the state of the art, and UpResGraphX achieving the best performance overall.", "description": "The metric scores of PCDNet versus others are tabulated in Table 1. As anticipated, all PCDNet variants outrun all the competing methods by a huge gap. Specifically, the average CD scores from our simplest model (FC) is already twice better than the state of the art. For IoU, our method still tops the table and raises the performance bar which was previ. ously set by GAL. Also, among all the variants of PCDNet, the GraphX family obtains better CD scores than the baseline whose deformation network is made of only FC layers.. Still and all, the gain in CD comes at the cost of lower IoU.. To our surprise, the best performance is achieved by the model using UpResGraphX." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Quantitative performance of different single image point cloud generation methods on 13 major categories of ShapeNet. “↑” indicates higher is better. “↓” specifies the opposition. Best performance is highlighted in bold.'\nProblem: 'Which model achieves the best mean CD score and what is the value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/127.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Quantitative performance of different single image point cloud generation methods on 13 major categories of ShapeNet. “↑” indicates higher is better. “↓” specifies the opposition. Best performance is highlighted in bold." ], "reasoning": "The table shows that the model 'Ours (UpResGraphX)' achieves the best mean CD score with a value of 0.252.", "question": "Which model achieves the best mean CD score and what is the value?", "answer": "So, the answer is {\"answer\": \"0.252\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model 'Ours (UpResGraphX)' achieves the best mean IoU score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/127.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Quantitative performance of different single image point cloud generation methods on 13 major categories of ShapeNet. “↑” indicates higher is better. “↓” specifies the opposition. Best performance is highlighted in bold." ], "reasoning": "The table shows that the model 'Ours (UpResGraphX)' does not achieve the best mean IoU score; instead, 'Ours (ResFC)' achieves the best mean IoU score with a value of 0.750.", "statement": "The model 'Ours (UpResGraphX)' achieves the best mean IoU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Quantitative performance of PCDNet when different features are ablated.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/128.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Quantitative performance of PCDNet when different features are ablated." ], "reasoning": "The models that incorporate only one feature (projection or AdaIN) achieve roughly the same performance, but combining both features significantly boosts both CD and IoU scores, validating the design of PCDNet.", "description": "Table 2 and Figure 9 demonstrate the quantitative and qualitative results of the ablation study, respectively.. As can be seen from the table, the models that incorporate only one feature (projection or AdaIN) achieves roughly the same performance. The projection feature helps PCDNet in CD score and AdaIN improves IoU.. When the two are combined, both the two scores are significantly boosted, which validates our design of PCDNet." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Quantitative performance of PCDNet when different features are ablated.'\nProblem: 'What is the mean IoU score for the model that uses both projection and AdaIN features?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/128.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Quantitative performance of PCDNet when different features are ablated." ], "reasoning": "The table shows the IoU scores for different models. The model that uses both projection and AdaIN features is labeled 'Ours (full)'. The mean IoU score for this model is 0.716.", "question": "What is the mean IoU score for the model that uses both projection and AdaIN features?", "answer": "So, the answer is {\"answer\": \"0.716\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model that uses only the projection feature has a higher mean IoU score than the model that uses only the AdaIN feature.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/128.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Quantitative performance of PCDNet when different features are ablated." ], "reasoning": "The table shows the mean IoU scores for the models. The model that uses only the projection feature has a mean IoU score of 0.644, while the model that uses only the AdaIN feature has a mean IoU score of 0.651. Therefore, the statement is refuted.", "statement": "The model that uses only the projection feature has a higher mean IoU score than the model that uses only the AdaIN feature.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Results on optimization of ground state energy of SK model for different system size N and instances I. [27] only reported results of system size up to N=1024. In the implementation of simulated annealing, the program failed to finish within 96 hours for N=8192. We also present the comparison of time consumption.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/129.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on optimization of ground state energy of SK model for different system size N and instances I. [27] only reported results of system size up to N=1024. In the implementation of simulated annealing, the program failed to finish within 96 hours for N=8192. We also present the comparison of time consumption." ], "reasoning": "Extremal optimization (EO) achieves better results but is limited to system sizes up to 1024 and is very time-consuming.", "description": "Although extremal optimization (EO) has obtained better results, this method is limited to system size up to 1024 and is extremely time-consuming." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Results on optimization of ground state energy of SK model for different system size N and instances I. [27] only reported results of system size up to N=1024. In the implementation of simulated annealing, the program failed to finish within 96 hours for N=8192. We also present the comparison of time consumption.'\nProblem: 'What is the EO E0 value for the largest system size where EO is applicable?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/129.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on optimization of ground state energy of SK model for different system size N and instances I. [27] only reported results of system size up to N=1024. In the implementation of simulated annealing, the program failed to finish within 96 hours for N=8192. We also present the comparison of time consumption." ], "reasoning": "The largest system size where EO is applicable is 1024. The EO E0 value for this size is -0.7563.", "question": "What is the EO E0 value for the largest system size where EO is applicable?", "answer": "So, the answer is {\"answer\": \"-0.7563\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nEO is applicable for system sizes up to 2048.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/129.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on optimization of ground state energy of SK model for different system size N and instances I. [27] only reported results of system size up to N=1024. In the implementation of simulated annealing, the program failed to finish within 96 hours for N=8192. We also present the comparison of time consumption." ], "reasoning": "The table shows EO values only for system sizes up to 1024, and no EO values are provided for sizes 2048 and above.", "statement": "EO is applicable for system sizes up to 2048.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results on optimization of ground state energy of SK model for different system size N and instances I. We use different optimizer in gradient descent (GD). We also present the results of parallel version of our proposed method where we choose Nbs=128.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/130.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on optimization of ground state energy of SK model for different system size N and instances I. We use different optimizer in gradient descent (GD). We also present the results of parallel version of our proposed method where we choose Nbs=128." ], "reasoning": "Gradient descent with Adam or L-BFGS optimizers shows unsatisfactory results due to many local minima, but parallel implementation significantly improves the results.", "description": "We can optimize this objective function through gradient descent (GD) with different optimizer, e.g. Adam or L-BFGS but the results are not satisfying.. are not satisfying. The reason is that there are too many frustrations and local mi With the implementation of the parallel version, the results can be improved greatly." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results on optimization of ground state energy of SK model for different system size N and instances I. We use different optimizer in gradient descent (GD). We also present the results of parallel version of our proposed method where we choose Nbs=128.'\nProblem: 'What is the time taken by GD (L-BFGS) for N=4096 and I=50?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/130.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on optimization of ground state energy of SK model for different system size N and instances I. We use different optimizer in gradient descent (GD). We also present the results of parallel version of our proposed method where we choose Nbs=128." ], "reasoning": "To find the time taken by GD (L-BFGS) for N=4096 and I=200, locate the row where N=4096 and the column for GD (L-BFGS) time (s). The value is 3.55 seconds.", "question": "What is the time taken by GD (L-BFGS) for N=4096 and I=200?", "answer": "So, the answer is {\"answer\": \"3.55\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe GD (Adam) optimizer takes more time than GD (L-BFGS) for N=1024 and I=1250.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/130.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on optimization of ground state energy of SK model for different system size N and instances I. We use different optimizer in gradient descent (GD). We also present the results of parallel version of our proposed method where we choose Nbs=128." ], "reasoning": "For N=1024 and I=1250, the time taken by GD (Adam) is 3.22 seconds, and the time taken by GD (L-BFGS) is 2.73 seconds. Since 3.22 is greater than 2.73, the statement is supported.", "statement": "The GD (Adam) optimizer takes more time than GD (L-BFGS) for N=1024 and I=1250.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results on MIS problems.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/131.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on MIS problems." ], "reasoning": "Our proposed method outperformed S2V-DQN on all three datasets and found the global optimal solution on the Cora dataset.", "description": "Table 3 presents the performance of MIS problem on three citation networks. Our proposed method has successfully found the global optimal solution on Cora dataset and obtained much better results compared to the sophisticated S2V-DQN on all three dataset. Although our results are not competitive with ," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Results on MIS problems.'\nProblem: 'Which method obtained the highest score on the Citeseer dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/131.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on MIS problems." ], "reasoning": "By comparing the scores for each method on the Citeseer dataset, GCNs obtained the highest score of 1867.", "question": "Which method obtained the highest score on the Citeseer dataset?", "answer": "So, the answer is {\"answer\": \"GCNs\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe GSO method obtained the highest score on the PubMed dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/131.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on MIS problems." ], "reasoning": "By comparing the scores for each method on the PubMed dataset, GCNs and Greedy both obtained the highest score of 15912, not GSO.", "statement": "The GSO method obtained the highest score on the PubMed dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Results on modularity optimization. We report the maximum modularity and the corresponding number of communities.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/132.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on modularity optimization. We report the maximum modularity and the corresponding number of communities." ], "reasoning": "Our method achieved higher modularity (Q) on all datasets compared to hierarchical agglomeration.", "description": "On the task of modularity optimization, our method also obtained satisfying results, as shown in Table 4. Comparing to hierarchical agglomeration, our proposed method has achieved much higher modularity on all datasets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Results on modularity optimization. We report the maximum modularity and the corresponding number of communities.'\nProblem: 'Which dataset has the highest modularity (Q) using the GSO method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/132.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on modularity optimization. We report the maximum modularity and the corresponding number of communities." ], "reasoning": "To find the dataset with the highest modularity using the GSO method, compare the GSO Q values for all datasets. The highest value is 0.5275 for the E-mail dataset.", "question": "Which dataset has the highest modularity (Q) using the GSO method?", "answer": "So, the answer is {\"answer\": \"E-mail\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Jazz dataset has a higher modularity (Q) using the EO method than the GSO method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/132.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on modularity optimization. We report the maximum modularity and the corresponding number of communities." ], "reasoning": "Compare the EO Q and GSO Q values for the Jazz dataset. The EO Q value is 0.4452, and the GSO Q value is 0.4451. Since 0.4452 is higher than 0.4451, the statement is supported.", "statement": "The Jazz dataset has a higher modularity (Q) using the EO method than the GSO method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results on structural optimization.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/133.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on structural optimization." ], "reasoning": "The GSO method achieved high reconstruction accuracy for both 10 and 30 node graphs, with better performance on chaotic dynamics compared to non-chaotic dynamics.", "description": "Here we present our results on the task of structural optimization in Table 5.. For a random 4-regular graph with 10 and 30 nodes, our GSO method has obtained very good reconstruction accuracy and the performance obtained on chaotic dynamics is better than that on non-chaotic dynamics." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Results on structural optimization.'\nProblem: 'What is the reconstruction accuracy for a 30-node graph with s, λ values of 0.2, 3.5?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/133.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on structural optimization." ], "reasoning": "To find the reconstruction accuracy for a 30-node graph with s, λ values of 0.2, 3.5, locate the row where N is 30 and s, λ is 0.2, 3.5. The corresponding accuracy is 93.11%.", "question": "What is the reconstruction accuracy for a 30-node graph with s, λ values of 0.2, 3.5?", "answer": { "answer": "93.11" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe GSO method achieved 100% accuracy for a 10-node graph with s, λ values of 0.2, 3.8.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/133.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on structural optimization." ], "reasoning": "To verify this statement, locate the row where N is 10 and s, λ is 0.2, 3.8. The corresponding accuracy is 100%, which supports the statement.", "statement": "The GSO method achieved 100% accuracy for a 10-node graph with s, λ values of 0.2, 3.8.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Exemplary boundary conditions for the test dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/134.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Exemplary boundary conditions for the test dataset" ], "reasoning": "Tables 2 and 3 provide statistical summaries of key thermodynamic properties, channel geometries, and wall temperatures for both training and test datasets.", "description": "Table 2 and Table 3 give an overview of mean value, standard deviation and different percentiles of most relevant thermodynamic properties of the coolant, channel geometries and the resulting wall temperature at the hot-gas wall for the training and test datasets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Exemplary boundary conditions for the test dataset'\nProblem: 'What is the highest value of the heat flux (q) in the test cases?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/134.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Exemplary boundary conditions for the test dataset" ], "reasoning": "To find the highest value of the heat flux (q), we need to compare the values in the q column across all test cases. The highest value is 81 MW/m2 in Test Case 2.", "question": "What is the highest value of the heat flux (q) in the test cases?", "answer": "So, the answer is {\"answer\": \"81\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe highest value of the area (A) is 7.4 mm2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/134.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Exemplary boundary conditions for the test dataset" ], "reasoning": "To verify this statement, we need to check the values in the A column across all test cases. The highest value of A is indeed 7.4 mm2, which appears in Test Cases 3 and 6.", "statement": "The highest value of the area (A) is 7.4 mm2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Mean value, standard deviation and percentiles of the training data'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/135.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Mean value, standard deviation and percentiles of the training data" ], "reasoning": "The tables provide statistical summaries of key thermodynamic properties, channel geometries, and wall temperatures for both training and test datasets.", "description": "Table 2 and Table 3 give an overview of mean value, standard deviation and different percentiles of most relevant thermodynamic properties of the coolant, channel geometries and the resulting wall temperature at the hot-gas wall for the training and test datasets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Mean value, standard deviation and percentiles of the training data'\nProblem: 'What is the mean value of the wall temperature at the hot-gas wall (Tw) for the training and test datasets?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/135.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Mean value, standard deviation and percentiles of the training data" ], "reasoning": "The mean value of the wall temperature at the hot-gas wall (Tw) is directly listed in the table under the 'Mean' row for the 'Tw' column.", "question": "What is the mean value of the wall temperature at the hot-gas wall (Tw) for the training and test datasets?", "answer": "So, the answer is {\"answer\": \"669 K\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe standard deviation of the coolant's enthalpy (hb) is 317 kJ/kg.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/135.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Mean value, standard deviation and percentiles of the training data" ], "reasoning": "The table lists the standard deviation of the coolant's enthalpy (hb) as 317 kJ/kg in the 'Std' row under the 'hb' column.", "statement": "The standard deviation of the coolant's enthalpy (hb) is 317 kJ/kg.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Investigations of WDB with different growth rates on Set5 with scaling factor 4.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/136.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Investigations of WDB with different growth rates on Set5 with scaling factor 4." ], "reasoning": "WDB consistently achieves higher PSNR scores than DB across different growth rates.", "description": "which weights are fixed and equal to 1. As shown in Table 1, by adopting a group of trainable weights, WDB can consistently achieve higher scores than DB when setting different growth rates. It achieves more apparent PSNR promotion with growth rates increased." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Investigations of WDB with different growth rates on Set5 with scaling factor 4.'\nProblem: 'What is the PSNR score for WDB-24?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/136.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Investigations of WDB with different growth rates on Set5 with scaling factor 4." ], "reasoning": "The table shows the PSNR scores for different growth rates. For WDB-24, the PSNR score is listed in the corresponding column.", "question": "What is the PSNR score for WDB-24?", "answer": { "answer": "31.71" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nWDB-48 has a higher SSIM score than DB-48.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/136.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Investigations of WDB with different growth rates on Set5 with scaling factor 4." ], "reasoning": "The table shows the SSIM scores for different growth rates. Comparing WDB-48 and DB-48, WDB-48 has a higher SSIM score (0.8975) than DB-48 (0.8970).", "statement": "WDB-48 has a higher SSIM score than DB-48.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Evaluation of SA with different growth rates on Set14 with 4× up-scaling factor.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/137.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation of SA with different growth rates on Set14 with 4× up-scaling factor." ], "reasoning": "SA improves PSNR by more than 0.1db for each growth rate and increases RCIR significantly, leading to clearer high-frequency image details. Despite similar parameters, SA (G = 20) achieves higher RCIR and SSIM than noSA (G = 32).", "description": "As shown in Table 2, SA improves. PSNR more than 0.1db in each growth rate, and it increases output RCIR with a large extent, thus high-frequency details of an image tend to be recovered more clearly. Besides, compared SA (G = 20) with noSA (G = 32), they have the almost same parameters (1.52M and 1.54M), but SA still achieves higher RCIR and SSIM than no SA. Notably, increasing G" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Evaluation of SA with different growth rates on Set14 with 4× up-scaling factor.'\nProblem: 'How does the PSNR of SA-32 compare to noSA-32, and what does this indicate about the performance of SA?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/137.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation of SA with different growth rates on Set14 with 4× up-scaling factor." ], "reasoning": "The PSNR of SA-32 is 28.55, while the PSNR of noSA-32 is 28.43. ", "question": "Does SA-32 or noSA-32 have a higher PSNR?", "answer": "So, the answer is {\"answer\": \"SA-32\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSA-16 has a higher SSIM than noSA-16.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/137.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation of SA with different growth rates on Set14 with 4× up-scaling factor." ], "reasoning": "The SSIM of SA-16 is 0.7820, while the SSIM of noSA-16 is 0.7784. Therefore, SA-16 has a higher SSIM than noSA-16.", "statement": "SA-16 has a higher SSIM than noSA-16.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparisons with the state-of-the-art methods by PSNR and SSIM (4×). Scores in bold denote the highest values (⋆ indicates that the input is divided into four parts and calculated due to computation limitation of large size images).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/138.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparisons with the state-of-the-art methods by PSNR and SSIM (4×). Scores in bold denote the highest values (⋆ indicates that the input is divided into four parts and calculated due to computation limitation of large size images)." ], "reasoning": "ADRD achieves the highest SSIM among all methods and outperforms D-DBPN by nearly 0.2db PSNR on the Urban100 dataset.", "description": "We compare ADRD with state-of-the-art methods, as shown in Table 3. Here, the bicubic interpolation is viewed as a baseline for comparisons. A+ [Timofte et al., 2013] is introduced as a conventional machine learning approach. Some CNN based methods, i.e. SRCNN [Dong et al., 2016a], VDSR [Kim et al., 2016a], LapSRN [Lai et al., 2017], and D-DBPN [Haris et al., 2018] are introduced. SRDenseNet [Tong et al., 2017] (denotes as SRDense), SR-DDNet [Zhu et al., 2018], and RDN [Zhang et al., 2018] are three different sizes of dense block based networks are also cited in the comparison list. ADRD achieves the highest SSIM among all methods, it tends to have better quality in human perception [Wang et al., 2004]. Because ADRD is adept at recovering high-frequency information. Additionally, ADRD also outperforms D-DBPN nearly 0.2db PSNR on Urban100 dataset that contains many large-size real-world images.. Despite D-DBPN surpasses ADRD on Set5 in PSNR as shown in Table 3," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Comparisons with the state-of-the-art methods by PSNR and SSIM (4×). Scores in bold denote the highest values (⋆ indicates that the input is divided into four parts and calculated due to computation limitation of large size images).'\nProblem: 'Which method achieves the highest SSIM on the Set14 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/138.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparisons with the state-of-the-art methods by PSNR and SSIM (4×). Scores in bold denote the highest values (⋆ indicates that the input is divided into four parts and calculated due to computation limitation of large size images)." ], "reasoning": "To find the method with the highest SSIM on the Set14 dataset, look at the SSIM values in the Set14 row. ADRD has the highest SSIM value of 0.7923.", "question": "Which method achieves the highest SSIM on the Set14 dataset?", "answer": { "answer": "ADRD" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nADRD achieves the highest PSNR on the Set5 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/138.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparisons with the state-of-the-art methods by PSNR and SSIM (4×). Scores in bold denote the highest values (⋆ indicates that the input is divided into four parts and calculated due to computation limitation of large size images)." ], "reasoning": "To verify this statement, compare the PSNR values for all methods on the Set5 dataset. D-DBPN has the highest PSNR value of 32.47, not ADRD.", "statement": "ADRD achieves the highest PSNR on the Set5 dataset.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: PSNR results of different noise levels on Set5.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/139.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: PSNR results of different noise levels on Set5." ], "reasoning": "ADRD consistently outperforms other methods across all noise levels, as shown in Table 4.", "description": "et al., 2018, RDN Zhang et al., 2018, LapSRN Lai et al., 2017] are introduced for comparisons. The detailed results are shown in Table 4.. ADRD outperforms all other methods in each noise level. Though RDN is also a dense block based network, it is easy" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: PSNR results of different noise levels on Set5.'\nProblem: 'Which method achieves the highest score at the noise level of 5×10⁻⁵?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/139.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: PSNR results of different noise levels on Set5." ], "reasoning": "By examining the table, the highest score at the noise level of 5×10⁻⁵ is achieved by ADRD with a score of 31.90.", "question": "Which method achieves the highest score at the noise level of 5×10⁻⁵?", "answer": "So, the answer is {\"answer\": \"ADRD\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRDN achieves the highest score at the noise level of 2×10⁻⁴.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/139.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: PSNR results of different noise levels on Set5." ], "reasoning": "The table shows that at the noise level of 2×10⁻⁴, ADRD achieves the highest score of 30.86, not RDN.", "statement": "RDN achieves the highest score at the noise level of 2×10⁻⁴.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Recognition accuracy on Pairs & Oxford.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/140.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Recognition accuracy on Pairs & Oxford." ], "reasoning": "ADRD improves Top-1 accuracy by 2.3%, while RDN improves it by 1.2%.", "description": "As shown in Table 5, ADRD promotes 2.3% Top-1 accuracy, while the RDN only promotes 1.2%. The results" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Recognition accuracy on Pairs & Oxford.'\nProblem: 'What is the difference in Top-1 accuracy improvement between ADRD and RDN?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/140.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Recognition accuracy on Pairs & Oxford." ], "reasoning": "ADRD promotes 2.3% Top-1 accuracy, while RDN promotes 1.2%. The difference is 2.3% - 1.2% = 1.1%.", "question": "What is the difference in Top-1 accuracy improvement between ADRD and RDN?", "answer": { "answer": "1.1%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nADRD has the highest Top-1 accuracy among the methods listed.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/140.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Recognition accuracy on Pairs & Oxford." ], "reasoning": "The table shows that ADRD has a Top-1 accuracy of 55.7%, which is higher than Bicubic (53.4%), LapSRN (52.1%), RDN (54.6%), and D-DBPN (55.1%).", "statement": "ADRD has the highest Top-1 accuracy among the methods listed.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Average error rates of different personalization approaches. The percentage of user inputs required by the semi-supervised approach in parentheses.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/141.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Average error rates of different personalization approaches. The percentage of user inputs required by the semi-supervised approach in parentheses." ], "reasoning": "The semi-supervised approach significantly reduces error rates compared to the non-supervised approach, with an average error rate less than 2%-units higher than the supervised approach.", "description": "Table 1 show error rates from the balanced accuracies averaged over all nine study subjects.. Model update clearly benefits from user inputs as the error rates using semisupervised approach (4.2/4.3%) are much lower than non-supervised (9.5%),. On average, the error rate using the semi-supervised approach is less than 2%-units higher than using the supervised approach.. (26%. the error rates of non-supervised models were much higher than the one's obtained using the semi-supervised approach (semi-supervised 4.2/4.4% vs. non-supervised 9.5%).. the average error rate using the semisupervised approach instead of supervised was less than 2%-units.. al" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Average error rates of different personalization approaches. The percentage of user inputs required by the semi-supervised approach in parentheses.'\nProblem: 'What is the average error rate for the semi-supervised approach with th=90?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/141.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Average error rates of different personalization approaches. The percentage of user inputs required by the semi-supervised approach in parentheses." ], "reasoning": "The table shows the error rates for different approaches. The average error rate for the semi-supervised approach with th=90 is listed in the 'Mean' row under the 'Semi-super, th=90' column.", "question": "What is the average error rate for the semi-supervised approach with th=90?", "answer": "{\"answer\": \"4.3%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe error rate for the non-supervised approach is higher than the semi-supervised approach with th=95.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/141.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Average error rates of different personalization approaches. The percentage of user inputs required by the semi-supervised approach in parentheses." ], "reasoning": "The table shows that the error rate for the non-supervised approach is 9.5%, while the error rate for the semi-supervised approach with th=95 is 4.2%. Therefore, the statement is supported.", "statement": "The error rate for the non-supervised approach is higher than the semi-supervised approach with th=95.", "answer": "{\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Performance of baseline (single model) for several metrics and percent reduction in error for other methods. All methods except baseline use T-scaling calibration. “T-scaling” is a single calibrated model.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/142.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of baseline (single model) for several metrics and percent reduction in error for other methods. All methods except baseline use T-scaling calibration. “T-scaling” is a single calibrated model." ], "reasoning": "The baseline classifier shows significantly higher error rates for novel samples compared to familiar ones across all tasks, with the ensemble of T-scaled models consistently achieving the lowest errors.", "description": "Our main table of results is shown in Table 1.. Looking at baseline performance in Table 1, we see much higher error rates for novel samples, compared to familiar, for all tasks. The label error and calibration error are both higher, leading to much higher NLL and Brier error. This means the baseline classifier is less accurate and has poor ability to detect its own inaccuracy on novel samples — it does not know what it does not know. For example, in gender recognition, the label error increases from 2.8% for novel to 14.7%; the calibration error ECE increases from 0.013 to 0.109; and the NLL increases from 0.083 to 0.542.. The differences between novel and familiar for object presence classification are substantial but smaller than other tasks,. In Table 1, \"T-scaling\" refers to the T-scaled baseline, and T-scaling is used for all other non-baseline methods as well.. Finally, considering Table 1, we see that the ensemble of T-scaled models dominates, consistently achieving the lowest label error, calibration error, NLL, and Brier error." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Performance of baseline (single model) for several metrics and percent reduction in error for other methods. All methods except baseline use T-scaling calibration. “T-scaling” is a single calibrated model.'\nProblem: 'What is the NLL for familiar samples in the Animals task using the baseline method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/142.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of baseline (single model) for several metrics and percent reduction in error for other methods. All methods except baseline use T-scaling calibration. “T-scaling” is a single calibrated model." ], "reasoning": "To find the NLL for familiar samples in the Animals task using the baseline method, locate the Animals task row and the NLL fam. column. The value is 0.326.", "question": "What is the NLL for familiar samples in the Animals task using the baseline method?", "answer": "So, the answer is {\"answer\": \"0.326\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ensemble method achieves the lowest label error for the Gender task on novel samples.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/142.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of baseline (single model) for several metrics and percent reduction in error for other methods. All methods except baseline use T-scaling calibration. “T-scaling” is a single calibrated model." ], "reasoning": "The table shows that for the Gender task on novel samples, the ensemble method has a label error of 24%, which is not the lowest. The lowest label error for novel samples in the Gender task is achieved by the Distill method with -7%.", "statement": "The ensemble method achieves the lowest label error for the Gender task on novel samples.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: T-scaling calibration effectively reduces likelihood error (NLL, Brier) and calibration error (ECE) for many models across tasks for familiar and unfamiliar samples. Without calibration, using an ensemble reduces these errors, but an ensemble of calibrated models (“Ens. T-scale”) performs best. Applying T-scaling to an ensemble of uncalibrated classifiers, and creating an ensemble of calibrated classifiers produces nearly identical results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/143.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: T-scaling calibration effectively reduces likelihood error (NLL, Brier) and calibration error (ECE) for many models across tasks for familiar and unfamiliar samples. Without calibration, using an ensemble reduces these errors, but an ensemble of calibrated models (“Ens. T-scale”) performs best. Applying T-scaling to an ensemble of uncalibrated classifiers, and creating an ensemble of calibrated classifiers produces nearly identical results." ], "reasoning": "Calibrated models and ensembles of calibrated models outperform their uncalibrated counterparts in tasks like cat vs. dog recognition, reducing NLL significantly.", "description": "els outperform uncalibrated models, and ensembles of calibrated models outperform ensembles of uncalibrated models. For example, in cat vs. dog recognition, the baseline NLL drops from 0.423 to 0.295, a 30% reduction; and the ensemble NLL drops from 0.286 to 0.229, a 20% reduction. Table 2 compares performance of the baseline and ensemble methods, both without and with T-scale calibration. Calibrated single mod. For the object presence task, there is little effect of calibration. We also found calibration to improve the Bayesian method . Calibration has little effect for distillation and G-distillation, likely because distillation's fitting to soft labels makes it less confident." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: T-scaling calibration effectively reduces likelihood error (NLL, Brier) and calibration error (ECE) for many models across tasks for familiar and unfamiliar samples. Without calibration, using an ensemble reduces these errors, but an ensemble of calibrated models (“Ens. T-scale”) performs best. Applying T-scaling to an ensemble of uncalibrated classifiers, and creating an ensemble of calibrated classifiers produces nearly identical results.'\nProblem: 'What is the NLL for a single calibrated model in the cat vs. dog recognition task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/143.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: T-scaling calibration effectively reduces likelihood error (NLL, Brier) and calibration error (ECE) for many models across tasks for familiar and unfamiliar samples. Without calibration, using an ensemble reduces these errors, but an ensemble of calibrated models (“Ens. T-scale”) performs best. Applying T-scaling to an ensemble of uncalibrated classifiers, and creating an ensemble of calibrated classifiers produces nearly identical results." ], "reasoning": "To find the NLL for a single calibrated model in the cat vs. dog recognition task, look at the row labeled 'Sin. T-scale' under 'Cat vs. Dog' and find the value under the 'NLL fam.' column.", "question": "What is the NLL for a single calibrated model in the cat vs. dog recognition task?", "answer": "So, the answer is {\"answer\": \"0.041\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCalibration improves the performance of single models in the cat vs. dog recognition task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/143.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: T-scaling calibration effectively reduces likelihood error (NLL, Brier) and calibration error (ECE) for many models across tasks for familiar and unfamiliar samples. Without calibration, using an ensemble reduces these errors, but an ensemble of calibrated models (“Ens. T-scale”) performs best. Applying T-scaling to an ensemble of uncalibrated classifiers, and creating an ensemble of calibrated classifiers produces nearly identical results." ], "reasoning": "The table shows that the NLL for a single model in the cat vs. dog recognition task drops from 0.423 to 0.295 with calibration, indicating improved performance.", "statement": "Calibration improves the performance of single models in the cat vs. dog recognition task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: MIG score of disentanglement.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/144.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: MIG score of disentanglement." ], "reasoning": "ICP achieves the highest MIG scores on both the dSprites and 3D Faces datasets, indicating state-of-the-art disentanglement performance.", "description": "We evaluate the disentanglement performance quantitatively by the Mutual Information Gap (MIG) score with the 2D shapes (dSprites) dataset and 3D Faces dataset. MIG is a classifier-free information-theoretic disentanglement metric and is meaningful for any factorized latent distribution. As shown in Table 3, ICP achieves the state-of-the-art performance on the quantitative evaluation of disentanglement. We also conduct ablation studies as what we do in the supervised setting.. From the results of ICP-ALL and ICP-COM, we find disentanglement performance decreases without the information diversifying and competing process." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: MIG score of disentanglement.'\nProblem: 'Which model achieves the highest MIG score on the 3D Faces dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/144.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: MIG score of disentanglement." ], "reasoning": "By comparing the MIG scores for the 3D Faces dataset in the table, ICP has the highest score of 0.73.", "question": "Which model achieves the highest MIG score on the 3D Faces dataset?", "answer": "So, the answer is {\"answer\": \"ICP\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nICP-COM achieves a higher MIG score than β-TCVAE on the dSprites dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/144.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: MIG score of disentanglement." ], "reasoning": "Comparing the MIG scores for the dSprites dataset, ICP-COM has a score of 0.20, which is lower than β-TCVAE's score of 0.38.", "statement": "ICP-COM achieves a higher MIG score than β-TCVAE on the dSprites dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison with the state-of-the-art methods on CARS196, CUB-200-2011 and SOP in terms of Recall@K (%). All the compared methods use GoogLeNet V2 as the backbone architecture. ‘–’ means the results which are not reported in the original paper. The best results in the first block using single embedding are bolded.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/145.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison with the state-of-the-art methods on CARS196, CUB-200-2011 and SOP in terms of Recall@K (%). All the compared methods use GoogLeNet V2 as the backbone architecture. ‘–’ means the results which are not reported in the original paper. The best results in the first block using single embedding are bolded." ], "reasoning": "ICE achieves the best Recall@1 performance on all benchmarks, except for SOP where it is 0.9% lower than Wang et al. (2019c).", "description": "Results. Table 3 compares the results of our ICE and those of the state-of-the-art DML losses. ICE achieves the best Recall@1 performance on all benchmarks. We observe that only RLL achieves comparable performance in a few terms.. We note that (Wang et al., 2019c) is also complex in designing weighting schemes and contains four control hyper-parameters. However, our Recall@1 on SOP is 77.3%, which is only 0.9% lower than 78.2% of (Wang et al., 2019c). It is also worth mentioning that among these approaches, except fine-tuned models with CCE, only our method has a clear probability interpretation and aims to maximise the joint instance-level matching probability. As observed, apart from being unscalable, CCE's performance is much worse than the state-of-the-art methods." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Comparison with the state-of-the-art methods on CARS196, CUB-200-2011 and SOP in terms of Recall@K (%). All the compared methods use GoogLeNet V2 as the backbone architecture. ‘–’ means the results which are not reported in the original paper. The best results in the first block using single embedding are bolded.'\nProblem: 'What is the Recall@1 performance of ICE on the CUB-200-2011 4 benchmark?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/145.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison with the state-of-the-art methods on CARS196, CUB-200-2011 and SOP in terms of Recall@K (%). All the compared methods use GoogLeNet V2 as the backbone architecture. ‘–’ means the results which are not reported in the original paper. The best results in the first block using single embedding are bolded." ], "reasoning": "From the table, locate the row for ICE and the column for CUB-200-2011 4. The value is 79.4%.", "question": "What is the Recall@1 performance of ICE on the CUB-200-2011 4 benchmark?", "answer": { "answer": "79.4%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nICE achieves the best Recall@1 performance on the CARS196 8 benchmark.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/145.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison with the state-of-the-art methods on CARS196, CUB-200-2011 and SOP in terms of Recall@K (%). All the compared methods use GoogLeNet V2 as the backbone architecture. ‘–’ means the results which are not reported in the original paper. The best results in the first block using single embedding are bolded." ], "reasoning": "From the table, ICE has a Recall@1 performance of 94.8% on the CARS196 8 benchmark, which is the highest compared to other methods.", "statement": "ICE achieves the best Recall@1 performance on the CARS196 8 benchmark.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: The impact of batch content C×k on SOP in terms of Recall@K (%). The batch size is N=180 and the scaling parameter is s=64.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/146.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The impact of batch content C×k on SOP in terms of Recall@K (%). The batch size is N=180 and the scaling parameter is s=64." ], "reasoning": "Increasing the number of classes in the mini-batch improves performance, as shown by higher R@1, R@10, and R@100 values when the number of classes increases from 36 to 90.", "description": "We evaluate the impact of batch content which consists of. classes and k images per class, i.e., ∀c, Nc = k. The batch size. =. × k is set to 180. In our experiments, we change the number of classes. from 36 to 90, and the number of images k from 2 to 5, while keeping the batch size unchanged. Table 4 shows the results on SOP dataset. We observe that when there are more classes in the mini-batch, the performance is better. We conjecture that as the number of classes increases, the mini-batch training becomes more difficult and helps the model to generalise better." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: The impact of batch content C×k on SOP in terms of Recall@K (%). The batch size is N=180 and the scaling parameter is s=64.'\nProblem: 'What is the R@1 value when the number of classes is 90 and the number of images per class is 2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/146.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The impact of batch content C×k on SOP in terms of Recall@K (%). The batch size is N=180 and the scaling parameter is s=64." ], "reasoning": "The table shows the R@1 value for different combinations of classes and images per class. For C×k=90×2, the R@1 value is 77.3.", "question": "What is the R@1 value when the number of classes is 90 and the number of images per class is 2?", "answer": { "answer": "77.3" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe performance improves as the number of classes increases from 36 to 90.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/146.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The impact of batch content C×k on SOP in terms of Recall@K (%). The batch size is N=180 and the scaling parameter is s=64." ], "reasoning": "The table shows that as the number of classes increases from 36 to 90, the R@1, R@10, and R@100 values generally increase, indicating improved performance.", "statement": "The performance improves as the number of classes increases from 36 to 90.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: The results of different batch size N on SOP in terms of Recall@K (%). While changing C, we fix k=2 and s=64. Therefore, N=C×2.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/147.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The results of different batch size N on SOP in terms of Recall@K (%). While changing C, we fix k=2 and s=64. Therefore, N=C×2." ], "reasoning": "As the number of classes increases from 100 to 180, the performance improves, indicating better generalization with more classes.", "description": "To explore different batch size. , we fix k = 2 and only change C. In this case,. =. × 2. Table 5 shows that as the number of classes increases, the performance grows. In detail, when the number of classes increases from 50 to 90, the performance raises from 74.4% to 77.3% accordingly. One reason may be that as the number of classes increases, it fits the global structure of the test set better, where there are a large number of classes but only a few positive examples. In addition, the increasing difficulty of mini-batch training can help the model to generalise better." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: The results of different batch size N on SOP in terms of Recall@K (%). While changing C, we fix k=2 and s=64. Therefore, N=C×2.'\nProblem: 'What is the performance at R@1 when the number of classes is 160?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/147.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The results of different batch size N on SOP in terms of Recall@K (%). While changing C, we fix k=2 and s=64. Therefore, N=C×2." ], "reasoning": "To find the performance at R@1 for 160 classes, locate the row where N=160 and find the corresponding value under the R@1 column.", "question": "What is the performance at R@1 when the number of classes is 160?", "answer": "So, the answer is {\"answer\": \"75.4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe performance at R@10 is highest when the number of classes is 180.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/147.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The results of different batch size N on SOP in terms of Recall@K (%). While changing C, we fix k=2 and s=64. Therefore, N=C×2." ], "reasoning": "To verify this statement, compare the values in the R@10 column for all rows. The highest value is 90.0, which corresponds to N=180.", "statement": "The performance at R@10 is highest when the number of classes is 180.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: The results of different embedding size on SOP in terms of Recall@K (%). In all experiments: s=64, C=90,k=2. N=C×k=90×2.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/148.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The results of different embedding size on SOP in terms of Recall@K (%). In all experiments: s=64, C=90,k=2. N=C×k=90×2." ], "reasoning": "The performance of DML methods on SOP improves as the embedding size increases, with smaller sizes like 64 performing worse.", "description": "The dimension of feature representations is an important factor in many DML methods. We conduct experiments on SOP to see the influence of different embedding size. The results are presented in Table 6. We observe that when the embedding size is very small, e.g., 64, the performance is much worse. The performance increases gradually as the embedding size grows." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: The results of different embedding size on SOP in terms of Recall@K (%). In all experiments: s=64, C=90,k=2. N=C×k=90×2.'\nProblem: 'What is the R@1 performance when the embedding size is 256?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/148.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The results of different embedding size on SOP in terms of Recall@K (%). In all experiments: s=64, C=90,k=2. N=C×k=90×2." ], "reasoning": "To find the R@1 performance for an embedding size of 256, locate the row with 256 in the first column and read the corresponding value in the R@1 column.", "question": "What is the R@1 performance when the embedding size is 256?", "answer": "So, the answer is {\"answer\": \"75.2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe R@10 performance is highest when the embedding size is 512.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/148.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The results of different embedding size on SOP in terms of Recall@K (%). In all experiments: s=64, C=90,k=2. N=C×k=90×2." ], "reasoning": "To verify this statement, compare the R@10 values for all embedding sizes. The highest R@10 value is 90.0, which corresponds to an embedding size of 512.", "statement": "The R@10 performance is highest when the embedding size is 512.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Validation of our auto-annotation for video instance segmentation on KITTI MOTS validation set (car class).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/149.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Validation of our auto-annotation for video instance segmentation on KITTI MOTS validation set (car class)." ], "reasoning": "The proposed method achieves state-of-the-art performance in single view depth prediction with unsupervised training, as shown by the highest sMOTSA score of 77.4 in Table 2.", "description": "Table 2 shows the results on the KITTI Eigen split test, where the proposed method achieves state-of-the-art performance in the single view depth prediction task with unsupervised training." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Validation of our auto-annotation for video instance segmentation on KITTI MOTS validation set (car class).'\nProblem: 'Which method achieved the highest sMOTSA score and what was the score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/149.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Validation of our auto-annotation for video instance segmentation on KITTI MOTS validation set (car class)." ], "reasoning": "To find the method with the highest sMOTSA score, look at the sMOTSA column and identify the highest value, which is 77.4, achieved by the method labeled 'Ours' with the Mask R-CNN/ResNet-101 backbone pretrained on COCO + MOTS.", "question": "Which method achieved the highest sMOTSA score and what was the score?", "answer": "So, the answer is {\"answer\": \"Ours with 77.4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe method with the highest sMOTSA score also has the lowest FN.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/149.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Validation of our auto-annotation for video instance segmentation on KITTI MOTS validation set (car class)." ], "reasoning": "The method with the highest sMOTSA score is 'Ours' with 77.4. Checking the FN column, 'Ours' with the highest sMOTSA score has an FN of 691, which is the lowest FN in the table.", "statement": "The method with the highest sMOTSA score also has the lowest FN.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Compared with various state-of-the-art efficient models on ImageNet, HURRICANE is the only NAS method that achieves high accuracy and low latency on all the target hardware. Results suggest FLOPs is not the accurate metric that indicates actual inference latency. As the hardware measurement settings in related NAS works are different, we measure the latency on our hardware platforms. ‡: CPU latency is measured on a single CPU core with float32 precision. ⋆: For fair comparison, we use the block search model instead of the block search+ channel search. †: In the form ”x(y)”, where ”x” means the training time reduction and ”y” means the accuracy achieved.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/150.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Compared with various state-of-the-art efficient models on ImageNet, HURRICANE is the only NAS method that achieves high accuracy and low latency on all the target hardware. Results suggest FLOPs is not the accurate metric that indicates actual inference latency. As the hardware measurement settings in related NAS works are different, we measure the latency on our hardware platforms. ‡: CPU latency is measured on a single CPU core with float32 precision. ⋆: For fair comparison, we use the block search model instead of the block search+ channel search. †: In the form ”x(y)”, where ”x” means the training time reduction and ”y” means the accuracy achieved." ], "reasoning": "HURRICANE achieves the lowest inference latency on DSP, CPU, and VPU with better or comparable accuracy compared to state-of-the-art models, and it significantly reduces supernet training time.", "description": "Ta. Com. CANE improves the accuracy by 2.59% to 4.03% on all target hardware platforms. Compared to state-of-the-art models searched by NAS, HURRICANE achieves the lowest inference latency on DSP, CPU, VPU, with better or comparable accuracy. It demonstrates that it's essential to leverage hardware diversity in NAS to achieve the best efficiency on different hardware platforms.. Finally, Table 3 shows none of existing efficient models are efficient on all the three target hardware.. Compared with Singlepath-Oneshot, HURRICANE (Stage1 + Stage2) reduces 30.4% supernet training time and finds models with better performance. Furthermore, HURRICANE (Stage1) already achieves better classification accuracy than other NAS methods (loss ≤ 0.5% compared with our final result) but it could reduce an average 54.7% time, which is almost a 2X training time speedup. It demonstrates the effectiveness of exploring more architecture selections in the later layers." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Compared with various state-of-the-art efficient models on ImageNet, HURRICANE is the only NAS method that achieves high accuracy and low latency on all the target hardware. Results suggest FLOPs is not the accurate metric that indicates actual inference latency. As the hardware measurement settings in related NAS works are different, we measure the latency on our hardware platforms. ‡: CPU latency is measured on a single CPU core with float32 precision. ⋆: For fair comparison, we use the block search model instead of the block search+ channel search. †: In the form ”x(y)”, where ”x” means the training time reduction and ”y” means the accuracy achieved.'\nProblem: 'Which model achieves the lowest inference latency on DSP, CPU, and VPU while maintaining better or comparable accuracy?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/150.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Compared with various state-of-the-art efficient models on ImageNet, HURRICANE is the only NAS method that achieves high accuracy and low latency on all the target hardware. Results suggest FLOPs is not the accurate metric that indicates actual inference latency. As the hardware measurement settings in related NAS works are different, we measure the latency on our hardware platforms. ‡: CPU latency is measured on a single CPU core with float32 precision. ⋆: For fair comparison, we use the block search model instead of the block search+ channel search. †: In the form ”x(y)”, where ”x” means the training time reduction and ”y” means the accuracy achieved." ], "reasoning": "The table shows that HURRICANE achieves the lowest inference latency on DSP (16.5 ms), CPU (301.3 ms), and VPU (364.5 ms) while maintaining accuracy of 76.67%, 74.59%, and 75.13% respectively, which is better or comparable to other models.", "question": "Which model achieves the lowest inference latency on DSP, CPU, and VPU while maintaining better or comparable accuracy?", "answer": "So, the answer is {\"answer\": \"HURRICANE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nHURRICANE achieves the lowest inference latency on DSP, CPU, and VPU.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/150.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Compared with various state-of-the-art efficient models on ImageNet, HURRICANE is the only NAS method that achieves high accuracy and low latency on all the target hardware. Results suggest FLOPs is not the accurate metric that indicates actual inference latency. As the hardware measurement settings in related NAS works are different, we measure the latency on our hardware platforms. ‡: CPU latency is measured on a single CPU core with float32 precision. ⋆: For fair comparison, we use the block search model instead of the block search+ channel search. †: In the form ”x(y)”, where ”x” means the training time reduction and ”y” means the accuracy achieved." ], "reasoning": "The table shows that HURRICANE achieves the lowest inference latency on DSP (16.5 ms), CPU (301.3 ms), and VPU (364.5 ms) compared to other models listed.", "statement": "HURRICANE achieves the lowest inference latency on DSP, CPU, and VPU.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Compared to Singlepath-Oneshot [9], our two-stage search method achieves higher accuracy with much less search cost (26.7%-73.6%) on both manually-designed and hardware-aware search spaces. We list out the training iterations on ImageNet (batchsize=1,024) and OUI (batchsize=64) for search cost comparison.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/151.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Compared to Singlepath-Oneshot [9], our two-stage search method achieves higher accuracy with much less search cost (26.7%-73.6%) on both manually-designed and hardware-aware search spaces. We list out the training iterations on ImageNet (batchsize=1,024) and OUI (batchsize=64) for search cost comparison." ], "reasoning": "The two-stage search method, HURRICANE, achieves higher accuracy and lower search cost compared to Singlepath-Oneshot, with significant reductions in training iterations.", "description": "To further demonstrate the effectiveness of two-stage search method, we compare it with Singlepath-Oneshot on a sequence of tasks in Table 5.. Table 5 summarizes experiment results. The searched models outperform the manual designed light-weight models, such as MobileNetV2 (top-1 acc: 72.00% on ImageNet, 85.67% on OUI-Adience-Age). For each task, our proposed method could achieve not only higher accuracy but also less search cost.. As shown in Table 5, in most tasks, only one step search (Stage1) of HURRICANE could achieve a comparable top1 accuracy (or even better in some tasks), but the number of training iterations is significantly reduced (50%-77.5%).. If the computation budget (e.g. training time) allows, HURRICANE can benefit from the second step search (Stage2). The accuracy is improved by 0.15%-1.06% with an additional cost of only 4.0%-23.3% of training iterations." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Compared to Singlepath-Oneshot [9], our two-stage search method achieves higher accuracy with much less search cost (26.7%-73.6%) on both manually-designed and hardware-aware search spaces. We list out the training iterations on ImageNet (batchsize=1,024) and OUI (batchsize=64) for search cost comparison.'\nProblem: 'What is the accuracy improvement of HURRICANE (Stage1+Stage2) over Singlepath-Oneshot for the OUI dataset with a hardware-aware (DSP) search space?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/151.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Compared to Singlepath-Oneshot [9], our two-stage search method achieves higher accuracy with much less search cost (26.7%-73.6%) on both manually-designed and hardware-aware search spaces. We list out the training iterations on ImageNet (batchsize=1,024) and OUI (batchsize=64) for search cost comparison." ], "reasoning": "For the OUI dataset with a hardware-aware (DSP) search space, Singlepath-Oneshot has an accuracy of 87.22%, while HURRICANE (Stage1+Stage2) has an accuracy of 87.62%. The improvement is 87.62% - 87.22% = 0.40%.", "question": "What is the accuracy improvement of HURRICANE (Stage1+Stage2) over Singlepath-Oneshot for the OUI dataset with a hardware-aware (DSP) search space?", "answer": { "answer": "0.40%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nHURRICANE (Stage1) achieves higher accuracy than Singlepath-Oneshot for the ImageNet dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/151.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Compared to Singlepath-Oneshot [9], our two-stage search method achieves higher accuracy with much less search cost (26.7%-73.6%) on both manually-designed and hardware-aware search spaces. We list out the training iterations on ImageNet (batchsize=1,024) and OUI (batchsize=64) for search cost comparison." ], "reasoning": "For the ImageNet dataset, Singlepath-Oneshot has an accuracy of 73.72%, while HURRICANE (Stage1) has an accuracy of 74.01%. Therefore, HURRICANE (Stage1) achieves higher accuracy.", "statement": "HURRICANE (Stage1) achieves higher accuracy than Singlepath-Oneshot for the ImageNet dataset.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: MNIST Arithmetic Generalization Results'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/152.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: MNIST Arithmetic Generalization Results" ], "reasoning": "The SEQL network maintains higher accuracy for pairs y ≥ 15 compared to the ReLU network, which drops to below 1%.", "description": "Generalization results of the network are shown in Table II.. First, the most significant result is the difference between the accuracy evaluated on pairs y < 15 and pairs y ≥ 15. For the architecture with the EQL network, the accuracy drops by a few percentage points. However, for. the architecture where the SEQL network is replaced by the commonly used fully-connected network with ReLU activation functions (which we label as \"ReLU\"), the accuracy drops to below 1% showing that the results of the SEQL is able to generalize reasonably well in a regime where the ReLU cannot generalize at all." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: MNIST Arithmetic Generalization Results'\nProblem: 'What is the accuracy of the SEQL network on the MNIST test dataset for pairs y < 15?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/152.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: MNIST Arithmetic Generalization Results" ], "reasoning": "The table shows the accuracy of different networks on the MNIST test dataset. For the SEQL network, the accuracy for pairs y < 15 is listed.", "question": "What is the accuracy of the SEQL network on the MNIST test dataset for pairs y < 15?", "answer": "So, the answer is {\"answer\": \"91\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ReLU network has an accuracy of 0.8% for pairs y ≥ 15 on the MNIST training dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/152.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: MNIST Arithmetic Generalization Results" ], "reasoning": "The table shows the accuracy of the ReLU network on the MNIST training dataset for pairs y ≥ 15 as 0.8%.", "statement": "The ReLU network has an accuracy of 0.8% for pairs y ≥ 15 on the MNIST training dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I:'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/153.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I:" ], "reasoning": "Algorithm 1 converges faster than both DGD and APC, requiring fewer iterations to reach a relative estimation error of 10^-4.", "description": "the convergence speed of our proposed algorithm is faster than the accelerated projection-based consensus (APC) method proposed in .. ∗(cid:107) ) of 10 We compare the number of iterations by DGD, APC and Algorithm 1 to reach a relative estimation error (defined as (cid:107)x(t ) − x −4 (ref. Table I). (cid:107)x∗(cid:107). The tuning parameter β has been set at 5 for Algorithm 1. The rest of the algorithm parameters have been set the respective algorithms will have their smallest possible convergence rates. Specifically, (α∗ = 3.17 ∗ 10 −7, δ∗ = 1.95) for Algorithm 1, (γ∗ = 1.08, η∗ = 12.03) for APC and δ∗ = 3.17 ∗ 10 −7 for DGD. such that. Algorithm 1 performs the fastest among these algorithms.. test the algorithm on a real data-set with a significant condition number (cond (AT A) = 5.8 ∗ 107) and get much better performance compared to the classical distributed gradient algorithm as well as the recently proposed accelerated projection-consensus algorithm (APC), regarding the number of iterations needed for convergence to the true solution." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I:'\nProblem: 'How many iterations does Algorithm 1 need to reach a relative estimation error of 10^-4?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/153.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I:" ], "reasoning": "The table shows that Algorithm 1 requires 2.11*10^4 iterations to reach a relative estimation error of 10^-4.", "question": "How many iterations does Algorithm 1 need to reach a relative estimation error of 10^-4?", "answer": "So, the answer is {\"answer\": \"2.11*10^4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAlgorithm 1 has the highest rate of decrease among the three algorithms.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/153.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I:" ], "reasoning": "The table shows that the rate of decrease for Algorithm 1 is 0.9583 + 4.3 * 10^8 * (0.9999 / (t + 1)), which is not the highest compared to DGD's rate of 0.9999.", "statement": "Algorithm 1 has the highest rate of decrease among the three algorithms.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Laconic energy efficiency relative to BASE2K.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/154.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Laconic energy efficiency relative to BASE2K." ], "reasoning": "LAC configurations LAC128, LAC256, LAC512, and LAC1K are more energy efficient than BASE2K by factors of 2.13×, 2.55×, 3.05×, and 1.95× respectively.", "description": "Table 2 summarizes the energy efficiency of various LAC configurations over BASE2K. On average over all networks LAC128, LAC256, LAC512, and LAC1K are 2.13×, 2.55×, 3.05×, and 1.95× more energy efficient than BASE2K." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Laconic energy efficiency relative to BASE2K.'\nProblem: 'Which LAC configuration is the most energy efficient on average over all networks?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/154.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Laconic energy efficiency relative to BASE2K." ], "reasoning": "By comparing the average energy efficiency factors of LAC128, LAC256, LAC512, and LAC1K, we see that LAC512 has the highest average energy efficiency factor of 3.05×.", "question": "Which LAC configuration is the most energy efficient on average over all networks?", "answer": "So, the answer is {\"answer\": \"LAC512\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nLAC256 is more energy efficient than LAC1K for all networks.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/154.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Laconic energy efficiency relative to BASE2K." ], "reasoning": "By comparing the energy efficiency factors of LAC256 and LAC1K for each network, we see that LAC256 has higher values than LAC1K for all networks listed.", "statement": "LAC256 is more energy efficient than LAC1K for all networks.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: target test set accuracy comparison between standard transfer learning, Temporal Ensembling (TE), Mk-MMD, Lautum regularization and both TE and Lautum regularization for different amounts of labeled training target samples, MNIST → notMNIST.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/155.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: target test set accuracy comparison between standard transfer learning, Temporal Ensembling (TE), Mk-MMD, Lautum regularization and both TE and Lautum regularization for different amounts of labeled training target samples, MNIST → notMNIST." ], "reasoning": "Lautum regularization consistently outperforms other methods in all target training set splits, and the combination of Lautum regularization and Temporal Ensembling provides the best accuracy.", "description": "We examine five different methods of transfer learning: (1) standard supervised transfer which uses the labeled samples only. (2) Temporal Ensembling semi-supervised learning as outlined in , applied in a transfer learning setting. Temporal Ensembling is applied in the post-transfer training stage. (3) Mk-MMD , which is based on 19 different Gaussian kernels with different standard deviations. Mk-MMD is applied in the pre-transfer training stage. (4) Lautum regularization our technique as described in Section IV. (5) Both Temporal Ensembling and Lautum regularization. Note that Temporal Ensembling is applied in the post-transfer training stage, whereas Lautum regularization is applied in the pre-transfer training stage.. For each of the five transfer learning methods we examined three different splits of the notMNIST training dataset which consists of 200,000 samples to an unlabeled part and a labeled part: (1) unlabeled part of 199,950 samples and a labeled part of 50 samples; (2) unlabeled part of 199,900 samples and a labeled part of 100 samples; (3) unlabeled part of 199,800 samples and a labeled part of 200 samples.. Using the settings outlined above we obtained the results shown in Table. for the MNIST → notMNIST case.. The advantage of using Lautum regularization is evident from the results, as it outperforms the other compared methods in all the examined target training set splits.. In general, the Temporal Ensembling method by itself does not yield very competitive results compared to standard transfer learning. However, the combination of Lautum regularization and Temporal Ensembling provides the best target test set accuracy over all the examined target training set splits." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: target test set accuracy comparison between standard transfer learning, Temporal Ensembling (TE), Mk-MMD, Lautum regularization and both TE and Lautum regularization for different amounts of labeled training target samples, MNIST → notMNIST.'\nProblem: 'What is the accuracy of the Lautum + TE method when the labeled part consists of 100 samples?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/155.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: target test set accuracy comparison between standard transfer learning, Temporal Ensembling (TE), Mk-MMD, Lautum regularization and both TE and Lautum regularization for different amounts of labeled training target samples, MNIST → notMNIST." ], "reasoning": "From the table, locate the row where the method is 'Lautum + TE' and the '# labeled' is 100. The corresponding accuracy is 77.32%.", "question": "What is the accuracy of the Lautum + TE method when the labeled part consists of 100 samples?", "answer": "So, the answer is {\"answer\": \"77.32%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Mk-MMD method achieves an accuracy of over 80% when the labeled part consists of 200 samples.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/155.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: target test set accuracy comparison between standard transfer learning, Temporal Ensembling (TE), Mk-MMD, Lautum regularization and both TE and Lautum regularization for different amounts of labeled training target samples, MNIST → notMNIST." ], "reasoning": "From the table, locate the row where the method is 'Mk-MMD' and the '# labeled' is 200. The corresponding accuracy is 80.35%, which is over 80%.", "statement": "The Mk-MMD method achieves an accuracy of over 80% when the labeled part consists of 200 samples.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: target test set accuracy comparison between standard transfer learning, Temporal Ensembling (TE), Mk-MMD, Lautum regularization and both TE and Lautum regularization for different amounts of labeled training target samples, CIFAR-10 → CIFAR-100 (10 classes).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/156.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: target test set accuracy comparison between standard transfer learning, Temporal Ensembling (TE), Mk-MMD, Lautum regularization and both TE and Lautum regularization for different amounts of labeled training target samples, CIFAR-10 → CIFAR-100 (10 classes)." ], "reasoning": "Using Lautum regularization improves the post-transfer performance on the target test set and outperforms Temporal Ensembling and Mk-MMD, especially in the case of 200 and 500 labeled samples.", "description": "We examined the same five transfer learning techniques as in the MNIST → notMNIST case, where for each we examined three different splits of the CIFAR-100 (10 classes) training dataset to an unlabeled part and a labeled part: (1) unlabeled part of 4,900 samples and a labeled part of 100 samples; (2) unlabeled part of 4,800 samples and a labeled part of 200 samples; (3) unlabeled part of 4,500 samples and a labeled part of 500 samples.. Using the settings outlined above we obtained the results shown in Table II for the CIFAR-10 → CIFAR-100 (10 classes) case.. It is evident from the results that in the CIFAR10 → CIFAR-100 (10 classes) case as well, using Lautum regularization improves the post-transfer performance on the target test set and outperforms Temporal Ensembling and Mk-MMD.. Note, however, that in the case of 200 and 500 labeled samples, applying Lautum regularization only, without Temporal Ensembling, slightly outperforms the combination of the two methods." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: target test set accuracy comparison between standard transfer learning, Temporal Ensembling (TE), Mk-MMD, Lautum regularization and both TE and Lautum regularization for different amounts of labeled training target samples, CIFAR-10 → CIFAR-100 (10 classes).'\nProblem: 'What is the accuracy of the Lautum method with 200 labeled samples?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/156.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: target test set accuracy comparison between standard transfer learning, Temporal Ensembling (TE), Mk-MMD, Lautum regularization and both TE and Lautum regularization for different amounts of labeled training target samples, CIFAR-10 → CIFAR-100 (10 classes)." ], "reasoning": "To find the accuracy of the Lautum method with 200 labeled samples, locate the row where the method is Lautum and the # labeled is 200. The corresponding accuracy is 60.90%.", "question": "What is the accuracy of the Lautum method with 200 labeled samples?", "answer": "So, the answer is {\"answer\": \"60.90%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Lautum method achieves the highest accuracy with 500 labeled samples.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/156.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: target test set accuracy comparison between standard transfer learning, Temporal Ensembling (TE), Mk-MMD, Lautum regularization and both TE and Lautum regularization for different amounts of labeled training target samples, CIFAR-10 → CIFAR-100 (10 classes)." ], "reasoning": "To verify this statement, compare the accuracy of the Lautum method with 500 labeled samples to other methods with the same number of labeled samples. Lautum achieves 70.80%, which is higher than the other methods.", "statement": "The Lautum method achieves the highest accuracy with 500 labeled samples.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: The average results of 10 experiments on traffic control tasks. For models named as Gated-*, dynamic thresholds with β=0.8 are used. The “delay” indicates the timesteps to complete the simulation. The “# C” indicates the number of collisions.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/157.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The average results of 10 experiments on traffic control tasks. For models named as Gated-*, dynamic thresholds with β=0.8 are used. The “delay” indicates the timesteps to complete the simulation. The “# C” indicates the number of collisions." ], "reasoning": "ACML achieves smaller delays and collisions compared to CommNet and AMP, while Gated-ACML outperforms ACML in terms of rewards and message efficiency.", "description": "We notice that ACML drives the cars with a relatively larger speed to complete the simulation with the smallest delay (i.e., 31.2, 43.1 and 66.4 for three scenarios, respectively). Besides, although its collision is not the smallest, ACML manages to achieve relatively smaller collisions (i.e., 1.9, 1.7 and 7.2 for three scenarios), since avoiding collision is also in favor of the total reward.. For traffic control, ACML outperforms CommNet and AMP as shown by the upper part of Table 1. The reason is that ACML has found better tradeoffs between small delay and small collision.. Both ACML-mean and ACML-attention improve ACML by a clear margin, and they perform much better than CommNet and AMP as shown in Table 1.. For traffic control scenarios, as shown at the lower part of Table 1, Gated-ACML emits much fewer messages than ACML, which is expected. What surprises us is that GatedACML even obtains more rewards than ACML.. Consequently, the delay of ACML is larger than that of Gated-ACML as shown in Table 1.. We also modify ATOC to make it suitable for continuous action and heterogeneous agents. The results are shown at the last row of Table 1 and 2. ATOC works well in traffic control tasks, but performs badly in routing and wifi tasks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: The average results of 10 experiments on traffic control tasks. For models named as Gated-*, dynamic thresholds with β=0.8 are used. The “delay” indicates the timesteps to complete the simulation. The “# C” indicates the number of collisions.'\nProblem: 'Which method achieves the smallest delay in the Complex Traffic Control scenario with 16 cars?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/157.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The average results of 10 experiments on traffic control tasks. For models named as Gated-*, dynamic thresholds with β=0.8 are used. The “delay” indicates the timesteps to complete the simulation. The “# C” indicates the number of collisions." ], "reasoning": "To find the method with the smallest delay in the Complex Traffic Control scenario with 16 cars, we look at the 'Complex Traffic Control (16 cars) delay' column and identify the smallest value.", "question": "Which method achieves the smallest delay in the Complex Traffic Control scenario with 16 cars?", "answer": { "answer": "Gated-ACML" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nACML-mean has a smaller delay than ACML in the Simple Traffic Control scenario with 4 cars.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/157.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The average results of 10 experiments on traffic control tasks. For models named as Gated-*, dynamic thresholds with β=0.8 are used. The “delay” indicates the timesteps to complete the simulation. The “# C” indicates the number of collisions." ], "reasoning": "Comparing the delays of ACML-mean and ACML in the Simple Traffic Control scenario with 4 cars, ACML-mean has a delay of 29.3 while ACML has a delay of 31.2, indicating that ACML-mean has a smaller delay.", "statement": "ACML-mean has a smaller delay than ACML in the Simple Traffic Control scenario with 4 cars.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: The average results of 10 experiments on packet routing and wifi access point configuration tasks. For models named as Gated-*, we adopt dynamic thresholds with β=0.8. The “WAPC.” is the abbreviation of Wifi Access Point Configuration.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/158.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The average results of 10 experiments on packet routing and wifi access point configuration tasks. For models named as Gated-*, we adopt dynamic thresholds with β=0.8. The “WAPC.” is the abbreviation of Wifi Access Point Configuration." ], "reasoning": "The performance of ACML is consistent across various multi-agent scenarios, and Gated-ACML can significantly reduce messages with minimal impact on rewards.", "description": "In addition, the results of packet routing and wifi access point configuration shown at the upper part of Table 2 can be analyzed similarly to demonstrate that the performance of ACML is consistent in several multi-agent scenarios.. For packet routing and access point configuration scenarios, as shown at the lower part of Table 2, Gated-ACML can prune a lot of messages (e.g., about 70% ≈ 1 − 33.6%) with little damage to the reward (e.g., about 10% ≈ 1 − 0.288/0.317)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: The average results of 10 experiments on packet routing and wifi access point configuration tasks. For models named as Gated-*, we adopt dynamic thresholds with β=0.8. The “WAPC.” is the abbreviation of Wifi Access Point Configuration.'\nProblem: 'How much does Gated-ACML reduce messages in the Simple Routing scenario compared to ACML?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/158.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The average results of 10 experiments on packet routing and wifi access point configuration tasks. For models named as Gated-*, we adopt dynamic thresholds with β=0.8. The “WAPC.” is the abbreviation of Wifi Access Point Configuration." ], "reasoning": "Gated-ACML reduces messages to 33.6% in the Simple Routing scenario, while ACML uses 100% of messages. The reduction is 100% - 33.6% = 66.4%.", "question": "How much does Gated-ACML reduce messages in the Simple Routing scenario compared to ACML?", "answer": "So, the answer is {\"answer\": \"66.4%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGated-ACML reduces messages by about 70% in the Simple Routing scenario.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/158.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The average results of 10 experiments on packet routing and wifi access point configuration tasks. For models named as Gated-*, we adopt dynamic thresholds with β=0.8. The “WAPC.” is the abbreviation of Wifi Access Point Configuration." ], "reasoning": "The table shows that Gated-ACML reduces messages to 33.6% in the Simple Routing scenario, which is approximately a 66.4% reduction. This is close to 70%, supporting the statement.", "statement": "Gated-ACML reduces messages by about 70% in the Simple Routing scenario.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: The results of Gated-ACML in packet routing scenarios. We adopt a fixed threshold T=LΔQ(oi)[K×Tm%].'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/159.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The results of Gated-ACML in packet routing scenarios. We adopt a fixed threshold T=LΔQ(oi)[K×Tm%]." ], "reasoning": "When the quantity of pruned messages is smaller than 30%, the reward actually increases, but as more messages are pruned, the reward decrease becomes larger.", "description": "The results are shown in Table 3. It can be noticed that the number of pruned messages is close to the desired Tm% for both routing scenarios.. In addition, as shown at the upper part of the table, when the quantity of pruned messages is smaller than 30%, the reward decrease is a negative value, which means that the reward is increased actually.. However, as we are pruning more messages, the reward decrease is becoming larger and larger as shown at the lower part of the table, especially when all messages are pruned." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: The results of Gated-ACML in packet routing scenarios. We adopt a fixed threshold T=LΔQ(oi)[K×Tm%].'\nProblem: 'At what Tm% does the Simple Routing reward decrease become positive?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/159.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The results of Gated-ACML in packet routing scenarios. We adopt a fixed threshold T=LΔQ(oi)[K×Tm%]." ], "reasoning": "By examining the Simple Routing reward decrease column, we see that the reward decrease becomes positive at 80.0% Tm%.", "question": "At what Tm% does the Simple Routing reward decrease become positive?", "answer": "So, the answer is {\"answer\": \"80.0%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe reward decrease for Moderate Routing is always negative.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/159.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The results of Gated-ACML in packet routing scenarios. We adopt a fixed threshold T=LΔQ(oi)[K×Tm%]." ], "reasoning": "By examining the Moderate Routing reward decrease column, we see that the reward decrease is negative for Tm% values up to 30.0%, but becomes positive at 70.0% and higher.", "statement": "The reward decrease for Moderate Routing is always negative.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: L2-norm distances between the center of samples for which an attribute is true and the center of samples for which it is not true (cmp. Fig. 8). The first 3 components of the PCA are used.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/160.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: L2-norm distances between the center of samples for which an attribute is true and the center of samples for which it is not true (cmp. Fig. 8). The first 3 components of the PCA are used." ], "reasoning": "The highest value per label is set bold, while the lowest is set italics. Most information about the hair is found in the BG+hair subspace and for pale skin it is in the face.", "description": "The labels considered in this experiment can be found in Tab. 1.. In Tab. 1, we show the distances based on the L2-norm between the mean 3-dimensional PCA-vectors of all samples in which a label is true, and those in which the label is false. We do that for all previously mentioned labels. The highest value per label is set bold, while the lowest is set italics.. most information about the hair is found in the BG+hair subspace and for pale skin it is in the face.. the Bushy Eyebrows label seems to have the most information in the BG+hair subspace." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: L2-norm distances between the center of samples for which an attribute is true and the center of samples for which it is not true (cmp. Fig. 8). The first 3 components of the PCA are used.'\nProblem: 'Which label has the highest L2-norm distance in the BG+Hair subspace?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/160.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: L2-norm distances between the center of samples for which an attribute is true and the center of samples for which it is not true (cmp. Fig. 8). The first 3 components of the PCA are used." ], "reasoning": "To find the label with the highest L2-norm distance in the BG+Hair subspace, look for the bold value in the BG+Hair column. The highest value is 5.155 for the Blo. Hair label.", "question": "Which label has the highest L2-norm distance in the BG+Hair subspace?", "answer": "So, the answer is {\"answer\": \"Blo. Hair\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe label with the highest L2-norm distance in the Face subspace is Male.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/160.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: L2-norm distances between the center of samples for which an attribute is true and the center of samples for which it is not true (cmp. Fig. 8). The first 3 components of the PCA are used." ], "reasoning": "To verify this statement, look for the bold value in the Face column. The highest value is 3.623 for the Pale Skin label, not Male.", "statement": "The label with the highest L2-norm distance in the Face subspace is Male.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Averaged total returns using VI policy trained using inferred reward from optimal demonstrations.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/161.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Averaged total returns using VI policy trained using inferred reward from optimal demonstrations." ], "reasoning": "The table shows that GP-ME-IRL has the highest average total return, followed closely by GP-PTR-IRL, while GP-Increasing-IRL has the lowest average total return.", "description": "Table. shows the averaged total returns obtained for trials in environments when rewards are inferred from optimal demonstrations using the probabilistic temporal ranking3 (GPPTR-IRL), a Gaussian process maximum entropy approach (GP-ME-IRL) and an increasing linear model assumption. In the optimal demonstration case, policies obtained using both the maximum entropy and probabilistic temporal ranking approach perform equally well," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Averaged total returns using VI policy trained using inferred reward from optimal demonstrations.'\nProblem: 'Which method has the lowest average total return and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/161.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Averaged total returns using VI policy trained using inferred reward from optimal demonstrations." ], "reasoning": "By comparing the average total returns in the table, GP-Increasing-IRL has the lowest average total return with a value of 7.39.", "question": "Which method has the lowest average total return and what is its value?", "answer": "So, the answer is {\"answer\": \"GP-Increasing-IRL with 7.39\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGP-PTR-IRL has a higher average total return than GP-ME-IRL.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/161.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Averaged total returns using VI policy trained using inferred reward from optimal demonstrations." ], "reasoning": "The table shows that GP-PTR-IRL has an average total return of 9.51, while GP-ME-IRL has an average total return of 9.58. Therefore, GP-PTR-IRL does not have a higher average total return than GP-ME-IRL.", "statement": "GP-PTR-IRL has a higher average total return than GP-ME-IRL.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Averaged total returns using VI policy trained using inferred reward from sub-optimal demonstrations.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/162.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Averaged total returns using VI policy trained using inferred reward from sub-optimal demonstrations." ], "reasoning": "The probabilistic temporal ranking approach performs better than the maximum entropy and increasing linear model approaches in sub-optimal demonstration cases.", "description": "Table II shows the averaged total returns obtained for trials in environments when rewards are inferred from sub-optimal demonstrations using the probabilistic temporal ranking, the Gaussian process maximum entropy approach and the increasing linear model assumption.. In this sub-optimal demonstration case, policies obtained using the maximum entropy approach regularly fail, while the probabilistic temporal ranking continues to perform relatively well." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Averaged total returns using VI policy trained using inferred reward from sub-optimal demonstrations.'\nProblem: 'Which approach yields the highest average total return in sub-optimal demonstration cases?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/162.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Averaged total returns using VI policy trained using inferred reward from sub-optimal demonstrations." ], "reasoning": "The table shows the average total returns for different approaches. The GP-PTR-IRL approach has the highest average total return of 7.42.", "question": "Which approach yields the highest average total return in sub-optimal demonstration cases?", "answer": "So, the answer is {\"answer\": \"GP-PTR-IRL\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe GP-ME-IRL approach yields the highest average total return.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/162.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Averaged total returns using VI policy trained using inferred reward from sub-optimal demonstrations." ], "reasoning": "The table shows that the GP-ME-IRL approach has an average total return of 3.31, which is lower than the GP-PTR-IRL approach's average total return of 7.42.", "statement": "The GP-ME-IRL approach yields the highest average total return.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Cross-validation experiments for analyzing how part knowledge transfers across category boundaries.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/163.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Cross-validation experiments for analyzing how part knowledge transfers across category boundaries." ], "reasoning": "The table shows the results of training on one category and testing on another, demonstrating the transferability of part knowledge across chairs, tables, and lamps.", "description": "We analyze the capability of transferring part knowledge across category boundaries under our framework. Table 2 presents experimental results of doing cross-validation using chairs, tables and lamps by training on one category and testing on another." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Cross-validation experiments for analyzing how part knowledge transfers across category boundaries.'\nProblem: 'What is the highest score achieved when training on one category and testing on another?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/163.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Cross-validation experiments for analyzing how part knowledge transfers across category boundaries." ], "reasoning": "To find the highest score, we need to look at all the values in the table and identify the maximum value. The highest score in the table is 37.1.", "question": "What is the highest score achieved when training on one category and testing on another?", "answer": "So, the answer is {\"answer\": \"37.1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe lowest score achieved when training on one category and testing on another is 8.3.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/163.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Cross-validation experiments for analyzing how part knowledge transfers across category boundaries." ], "reasoning": "To verify this statement, we need to look at all the values in the table and identify the minimum value. The lowest score in the table is indeed 8.3.", "statement": "The lowest score achieved when training on one category and testing on another is 8.3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Quantitative evaluation of involving more context. w/ and w/o denote making decision with and without involving more context, respectively. Note that we only introduce more context in the late grouping process and the involved context is restricted in a very local region. The number is the mean recall of segmentation results. The L1, L2 and L3 refer to the three levels of segmentation defined in PartNet. Avg is the average among mean recall of three levels segmentation results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/164.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Quantitative evaluation of involving more context. w/ and w/o denote making decision with and without involving more context, respectively. Note that we only introduce more context in the late grouping process and the involved context is restricted in a very local region. The number is the mean recall of segmentation results. The L1, L2 and L3 refer to the three levels of segmentation defined in PartNet. Avg is the average among mean recall of three levels segmentation results." ], "reasoning": "The context improves performance on seen categories but negatively affects most unseen categories.", "description": "From the results listed in Table 3, we can point out that the involved context helps to consistently improve the performance on seen categories, but has negative effects on most unseen categories." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Quantitative evaluation of involving more context. w/ and w/o denote making decision with and without involving more context, respectively. Note that we only introduce more context in the late grouping process and the involved context is restricted in a very local region. The number is the mean recall of segmentation results. The L1, L2 and L3 refer to the three levels of segmentation defined in PartNet. Avg is the average among mean recall of three levels segmentation results.'\nProblem: 'What is the average performance on unseen categories with context?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/164.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Quantitative evaluation of involving more context. w/ and w/o denote making decision with and without involving more context, respectively. Note that we only introduce more context in the late grouping process and the involved context is restricted in a very local region. The number is the mean recall of segmentation results. The L1, L2 and L3 refer to the three levels of segmentation defined in PartNet. Avg is the average among mean recall of three levels segmentation results." ], "reasoning": "To find the average performance on unseen categories with context, look at the 'Avg' row under the 'w/' context column for unseen categories.", "question": "What is the average performance on unseen categories with context?", "answer": { "answer": "38.1" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe context has a positive effect on the performance of unseen categories.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/164.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Quantitative evaluation of involving more context. w/ and w/o denote making decision with and without involving more context, respectively. Note that we only introduce more context in the late grouping process and the involved context is restricted in a very local region. The number is the mean recall of segmentation results. The L1, L2 and L3 refer to the three levels of segmentation defined in PartNet. Avg is the average among mean recall of three levels segmentation results." ], "reasoning": "The table shows that the average performance on unseen categories with context (38.1) is lower than without context (41.6), indicating a negative effect.", "statement": "The context has a positive effect on the performance of unseen categories.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Quantitative results of the components analysis. We train the models on the Chair, Lamp, Storage Furniture of level-3 annotations and test on the listed categories. The number is the mean recall of the most fine-grained annotations of each category.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/165.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Quantitative results of the components analysis. We train the models on the Chair, Lamp, Storage Furniture of level-3 annotations and test on the listed categories. The number is the mean recall of the most fine-grained annotations of each category." ], "reasoning": "The table shows the quantitative results of training the model on Chair, Lamp, and Storage Furniture with different components, validating their effectiveness.", "description": "Here, we show the quantitative results and validate the effectiveness of these components. The results are listed in Table 4, where we train the model on the Chair, Lamp, Storage Furniture of level-3 annotations." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Quantitative results of the components analysis. We train the models on the Chair, Lamp, Storage Furniture of level-3 annotations and test on the listed categories. The number is the mean recall of the most fine-grained annotations of each category.'\nProblem: 'What is the highest score achieved in the Unseen Category for the full-model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/165.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Quantitative results of the components analysis. We train the models on the Chair, Lamp, Storage Furniture of level-3 annotations and test on the listed categories. The number is the mean recall of the most fine-grained annotations of each category." ], "reasoning": "To find the highest score in the Unseen Category for the full-model, look at the 'Unseen Category' columns in the 'full-model' row. The highest score is 66.5.", "question": "What is the highest score achieved in the Unseen Category for the full-model?", "answer": "So, the answer is {\"answer\": \"66.5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe no purity model achieved a higher score in the Seen Category than the no rectification model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/165.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Quantitative results of the components analysis. We train the models on the Chair, Lamp, Storage Furniture of level-3 annotations and test on the listed categories. The number is the mean recall of the most fine-grained annotations of each category." ], "reasoning": "To verify this statement, compare the scores in the 'Seen Category' columns for 'no purity' and 'no rectification'. The 'no purity' model has scores of 38.6, 36.8, and 5.6, while the 'no rectification' model has scores of 38.4, 36.4, and 5.5. The 'no purity' model has higher scores in all Seen Category columns.", "statement": "The no purity model achieved a higher score in the Seen Category than the no rectification model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Quantitative evaluation of the sub-part proposal module. PosAcc and NegAcc refer to positive accuracy and negative accuracy of the binary segmentation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/166.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Quantitative evaluation of the sub-part proposal module. PosAcc and NegAcc refer to positive accuracy and negative accuracy of the binary segmentation." ], "reasoning": "The module was trained on Chair, Storage Furniture, and Lamp categories and tested on all categories using the most fine-grained level annotations.", "description": "To validate the transferring performance of this module, we train the module on Chair, Storage Furniture, and Lamp of level-3 annotations and test on all categories with evaluating by the most fine-grained level annotations of each category. The results are listed in Table 5." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Quantitative evaluation of the sub-part proposal module. PosAcc and NegAcc refer to positive accuracy and negative accuracy of the binary segmentation.'\nProblem: 'What is the highest PosAcc score for the Seen Category?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/166.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Quantitative evaluation of the sub-part proposal module. PosAcc and NegAcc refer to positive accuracy and negative accuracy of the binary segmentation." ], "reasoning": "To find the highest PosAcc score for the Seen Category, look at all the PosAcc scores under the Seen Category columns and identify the maximum value.", "question": "What is the highest PosAcc score for the Seen Category?", "answer": { "answer": "98.6" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe lowest NegAcc score for the Unseen Category is 3.8.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/166.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Quantitative evaluation of the sub-part proposal module. PosAcc and NegAcc refer to positive accuracy and negative accuracy of the binary segmentation." ], "reasoning": "To verify this statement, look at all the NegAcc scores under the Unseen Category columns and identify the minimum value. The lowest NegAcc score for the Unseen Category is 3.8.", "statement": "The lowest NegAcc score for the Unseen Category is 3.8.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: AUC of link prediction.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/167.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: AUC of link prediction." ], "reasoning": "HSRL significantly outperforms all baselines on all datasets, with the largest improvements observed on large-scale networks such as DBLP, Yelp, and Douban.", "description": "Each experiment is independently implemented for 20 times and the average performances on the testing set are reported in Table II.. HSRL significantly outperforms all baselines on all datasets. For the small and sparse network movielens, the improvements of HSRL(DW), HSRL(N2V), and HSRL(LINE) are 3.6%, 2.5%, and 16.6% respectively. For the dense network MIT, HSRL(DW), HSRL(N2V), and HSRL(LINE) outperform the baselines by 2.9%, 8.5%, and 0.5%. For three large networks, DBLP, Yelp, and Douban, the improvement of HSRL is striking: the improvements of HSRL(DW), HSRL(N2V), and HSRL(LINE) are 6.3%, 19.9%, 3.5% for DBLP, 6.5%, 16.8%, 5.9% for Yelp, and 18.4%, 30.5%, 17.5% for Douban.. The results of HARP on movielens, DBLP, Yelp, and Douban are worse than the original NRL methods. Moreover, the performance of HARP(LINE) is drastically worse than LINE. It only works better than DeepWalk, node2vec on MIT which is a small and dense network.. The compressed networks generated by HARP on a network could not reveal its global topologies. Hence, using node embeddings of compressed networks as initialization could mislead the NRL methods to a bad local minimum. Such an issue could occur especially when the input network is large-scale and the objective function of the NRL method is highly non-convex, e.g., LINE.. The improvements of HSRL on DBLP, Yelp, and Duban are larger than that on Movielens and MIT. It demonstrates that HSRL works much better than baselines on large-scale networks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: AUC of link prediction.'\nProblem: 'What is the performance of HSRL(DW) on the MIT dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/167.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: AUC of link prediction." ], "reasoning": "To find the performance of HSRL(DW) on the MIT dataset, locate the row for HSRL(DW) and the column for the MIT dataset. The value is 0.926.", "question": "What is the performance of HSRL(DW) on the MIT dataset?", "answer": { "answer": "0.926" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nHSRL(LINE) performs better than HSRL(N2V) on the Movielens dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/167.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: AUC of link prediction." ], "reasoning": "To verify this statement, compare the performance values of HSRL(LINE) and HSRL(N2V) on the Movielens dataset. HSRL(LINE) has a value of 0.735, while HSRL(N2V) has a value of 0.865. Therefore, HSRL(LINE) does not perform better than HSRL(N2V) on the Movielens dataset.", "statement": "HSRL(LINE) performs better than HSRL(N2V) on the Movielens dataset.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Maximal rejectable impulses for the various learning algorithms without taking steps.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/168.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Maximal rejectable impulses for the various learning algorithms without taking steps." ], "reasoning": "The learned policy is independent of the learning algorithm, with TRPO achieving higher rewards and withstanding higher impulses compared to PPO and DDPG.", "description": "Due to the structure and choice of our framework, the learned policy is independent of the type of learning algorithm. We trained a policy for maintaining balance via TRPO, PPO, and DDPG, and found similar resulting behavior (cf. Table I). All four balancing strategies (Fig. 1) emerges regardless of the DRL algorithm used. However, from our simulations, TRPO is able to achieve higher rewards and is able to withstand higher impulses. Figure 5 shows the learning curves for the policies learned in Table I." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Maximal rejectable impulses for the various learning algorithms without taking steps.'\nProblem: 'Which learning algorithm achieved the highest maximal disturbance in the sagittal direction?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/168.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Maximal rejectable impulses for the various learning algorithms without taking steps." ], "reasoning": "The table shows the maximal disturbances in Ns for different learning algorithms. TRPO has the highest value in the sagittal direction with 240 Ns.", "question": "Which learning algorithm achieved the highest maximal disturbance in the sagittal direction?", "answer": { "answer": "TRPO" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTRPO achieved the highest maximal disturbance in the lateral direction.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/168.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Maximal rejectable impulses for the various learning algorithms without taking steps." ], "reasoning": "The table shows that DDPG achieved the highest maximal disturbance in the lateral direction with 160 Ns, not TRPO.", "statement": "TRPO achieved the highest maximal disturbance in the lateral direction.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Push disturbance from various push recovery studies'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/169.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Push disturbance from various push recovery studies" ], "reasoning": "The learned policy performs similarly to other controllers in strategies not taking a step and better in strategies taking a step, with results obtained from simulations.", "description": "The normalized impulse is used for comparison between the controllers of other works , , , and the learned policy (Table IV). We compared sagittal and lateral pushes. For the sagittal push, two impulses are chosen such that foot stepping occurs for the larger impulse (1.73N s/kg), while the smaller impulse (0.57N s/kg) will result in a strategy without stepping. By comparing the rejectable normalized impulse of the strategies not taking a step (A, B, E, G), it can be seen that our policy performs similar (E: 0.57N s/kg, G: 0.56N s/kg) to the other controllers (A: 0.6N s/kg, B: 0.52N s/kg). For the strategies taking a step (C, D, F), our policy is able to perform better (F: 1.73N s/kg) than the stepping controllers. (0.52N s/kg) and D (0.6N s/kg). Albeit our results are obtained from simulation whereas. and D are obtained from real experiments, we show in the next section that the generated motions are realistic and within the real physical constraints." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Push disturbance from various push recovery studies'\nProblem: 'Which controller has the highest normalized impulse for strategies taking a step?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/169.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Push disturbance from various push recovery studies" ], "reasoning": "By comparing the normalized impulses for strategies taking a step (C, D, F), the highest value is found in column F with a normalized impulse of 1.73 N s/kg.", "question": "Which controller has the highest normalized impulse for strategies taking a step?", "answer": "So, the answer is {\"answer\": \"F\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe learned policy performs better than other controllers in strategies taking a step.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/169.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Push disturbance from various push recovery studies" ], "reasoning": "The table shows that the learned policy (F: 1.73 N s/kg) has a higher normalized impulse compared to other controllers (C: 0.52 N s/kg, D: 0.6 N s/kg) for strategies taking a step.", "statement": "The learned policy performs better than other controllers in strategies taking a step.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: Peak torques and velocities for different scenarios.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/170.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Peak torques and velocities for different scenarios." ], "reasoning": "Table V shows the peak torques and velocities for different scenarios, highlighting the highest joint torque for dropping and the highest joint velocities for taking multiple steps due to large pushes at the pelvis.", "description": "Table V compares the peak torques and velocities for different scenarios. The chosen scenarios require the largest joint torque for dropping and the largest joint velocities for taking multiple steps due to large pushes at the pelvis. All other presented test cases required less joint torque and velocity than the the ones presented in Table V." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: Peak torques and velocities for different scenarios.'\nProblem: 'Which scenario requires the highest peak joint torque for the knee pitch?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/170.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Peak torques and velocities for different scenarios." ], "reasoning": "By examining the 'Peak joint torque [N] Knee pitch' column, the highest value is 350 N, which appears in the '0.55m Drop' and '240 Ns Pelvis (sagittal)' scenarios.", "question": "Which scenario requires the highest peak joint torque for the knee pitch?", "answer": "So, the answer is {\"answer\": \"0.55m Drop and 240 Ns Pelvis (sagittal)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 0.55m Drop scenario requires the highest peak joint velocity for the hip pitch.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/170.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Peak torques and velocities for different scenarios." ], "reasoning": "By examining the 'Peak joint velocity [rad/s] Hip pitch' column, the highest value is 6.11 rad/s, which appears in the 'Joint limit' scenario, not the '0.55m Drop' scenario.", "statement": "The 0.55m Drop scenario requires the highest peak joint velocity for the hip pitch.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Reported results assume observation length of Tobs=1.6sec and prediction lengths of Tpred=3.2sec and Tpred=4.8sec. First 4 rows correspond to the Average Displacement Error and last 4 to the final displacement error. Each result represents an average obtained from 5 independent runs with different random seeds.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/171.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Reported results assume observation length of Tobs=1.6sec and prediction lengths of Tpred=3.2sec and Tpred=4.8sec. First 4 rows correspond to the Average Displacement Error and last 4 to the final displacement error. Each result represents an average obtained from 5 independent runs with different random seeds." ], "reasoning": "The proposed solution, RDB, often outperforms alternatives, especially in error metrics, but building an environmental model for UCY Zara01 and Zara02 datasets is challenging.", "description": "The proposed solution outperforms the alternatives.. Table 1 shows the results obtained on this task.. it can be observed that the variability of the predicted trajectories is affected by the uncertainty of the global knowledge of the environment.. a well optimised vanilla LSTM with stochastic output often outperforms alternative approaches.. we observed results proportional to the quality of the learned global model of the environment. Building an environmental model for the UCY Zara01 and Zara02 datasets is particularly challenging and thus the results are not substantially better than alternatives.. importance of environmental or spatial information when making predictions." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Reported results assume observation length of Tobs=1.6sec and prediction lengths of Tpred=3.2sec and Tpred=4.8sec. First 4 rows correspond to the Average Displacement Error and last 4 to the final displacement error. Each result represents an average obtained from 5 independent runs with different random seeds.'\nProblem: 'Which model has the lowest average error for Tpred=3.2s?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/171.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Reported results assume observation length of Tobs=1.6sec and prediction lengths of Tpred=3.2sec and Tpred=4.8sec. First 4 rows correspond to the Average Displacement Error and last 4 to the final displacement error. Each result represents an average obtained from 5 independent runs with different random seeds." ], "reasoning": "To find the model with the lowest average error for Tpred=3.2s, look at the 'Error' row under Tpred=3.2s and compare the average values. RDB has the lowest average error of 0.048 ± 0.001.", "question": "Which model has the lowest average error for Tpred=3.2s?", "answer": "So, the answer is {\"answer\": \"RDB\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LSTM model has the lowest average error for Tpred=4.8s.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/171.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Reported results assume observation length of Tobs=1.6sec and prediction lengths of Tpred=3.2sec and Tpred=4.8sec. First 4 rows correspond to the Average Displacement Error and last 4 to the final displacement error. Each result represents an average obtained from 5 independent runs with different random seeds." ], "reasoning": "To verify this statement, look at the 'Error' row under Tpred=4.8s and compare the average values. The RDB model has the lowest average error of 0.091 ± 0.006, not the LSTM model.", "statement": "The LSTM model has the lowest average error for Tpred=4.8s.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Ablation Table'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/172.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation Table" ], "reasoning": "Combining global dynamics and static representation results in more accurate predictions than considering them individually.", "description": "Table 2 shows that a combination of both global dynamics and static representation are useful.. considered individually, less accurate predictions are made, but combining these outperforms alternatives.. contains longer, more complex trajectorThe less accurate model of the world in this scenario has not resulted in improved performance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Ablation Table'\nProblem: 'Which dataset and model combination has the lowest prediction error?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/172.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation Table" ], "reasoning": "By examining the table, the highest prediction error is found in the ETH Univ dataset with the R+D+B model, which has an error of 0.113.", "question": "Which dataset and model combination has the highest prediction error?", "answer": "So, the answer is {\"answer\": \"ETH Univ with R+D+B\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe UCY Zara01 dataset with the R+B model has a higher prediction error than the ETH Univ dataset with the B model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/172.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation Table" ], "reasoning": "The table shows that the UCY Zara01 dataset with the R+B model has a prediction error of 0.031, while the ETH Univ dataset with the B model has a prediction error of 0.146. Therefore, the statement is refuted.", "statement": "The UCY Zara01 dataset with the R+B model has a higher prediction error than the ETH Univ dataset with the B model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Average test accuracy (%) of backpropagation (BP), DNI [8], critic training [3], and proposed local critic training (LC). The numbers of local networks used are shown in the parentheses. The standard deviation values are also shown.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/173.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Average test accuracy (%) of backpropagation (BP), DNI [8], critic training [3], and proposed local critic training (LC). The numbers of local networks used are shown in the parentheses. The standard deviation values are also shown." ], "reasoning": "The proposed local critic training approach decouples the training of layer groups with a small decrease in accuracy, showing similar or slightly better performance compared to the critic training method.", "description": "When more local critic networks are used, the accuracy tends to decrease more due to higher reliance on predicted gradients rather than true gradients, while more layer groups can be trained independently.. The classification performance of the proposed local critic training approach is evaluated in Table I. For comparison, the performance of the regular backpropagation, DNI , and critic training is also evaluated.. When compared to the result of backpropagation, the proposed approach successfully decouples training of the layer groups at a small expense of accuracy decrease. The degradation of the accuracy of our method is larger for CIFAR-100,. The DNI method shows poor performance as in . The proposed method shows similar (or even slightly better) performance to the critic training method, which shows the efficacy of the cascaded learning scheme of the local networks in our method." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Average test accuracy (%) of backpropagation (BP), DNI [8], critic training [3], and proposed local critic training (LC). The numbers of local networks used are shown in the parentheses. The standard deviation values are also shown.'\nProblem: 'What is the accuracy of the proposed local critic training approach with 3 local critic networks on the CIFAR-10 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/173.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Average test accuracy (%) of backpropagation (BP), DNI [8], critic training [3], and proposed local critic training (LC). The numbers of local networks used are shown in the parentheses. The standard deviation values are also shown." ], "reasoning": "The table shows the accuracy for different methods on CIFAR-10. For the proposed local critic training approach with 3 local critic networks (LC (3)), the accuracy is 92.39.", "question": "What is the accuracy of the proposed local critic training approach with 3 local critic networks on the CIFAR-10 dataset?", "answer": "So, the answer is {\"answer\": \"92.39\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed local critic training approach with 1 local critic network has higher accuracy on CIFAR-100 than the Critic method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/173.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Average test accuracy (%) of backpropagation (BP), DNI [8], critic training [3], and proposed local critic training (LC). The numbers of local networks used are shown in the parentheses. The standard deviation values are also shown." ], "reasoning": "The table shows the accuracy for different methods on CIFAR-100. The proposed local critic training approach with 1 local critic network (LC (1)) has an accuracy of 73.61, while the Critic method has an accuracy of 69.07. Therefore, the statement is supported.", "statement": "The proposed local critic training approach with 1 local critic network has higher accuracy on CIFAR-100 than the Critic method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Average test accuracy (%) with respect to the number of layers in the local critic networks. [a,b,c] means that the numbers of convolutional layers in LC1, LC2, and LC3 are a, b, and c, respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/174.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Average test accuracy (%) with respect to the number of layers in the local critic networks. [a,b,c] means that the numbers of convolutional layers in LC1, LC2, and LC3 are a, b, and c, respectively." ], "reasoning": "As the number of convolutional layers increases for all local networks, the accuracy for CIFAR-100 slightly increases, whereas for CIFAR-10 the accuracy slightly decreases when five convolutional layers are used.", "description": "The results for various structure combinations of the three local critic networks are shown in Table II.. As the number of convolutional layers increase for all local networks (the first three cases in the table), the accuracy for CIFAR-100 slightly increases from 69.91% (with one convolutional layer) to 70.02% (three convolutional layers) and 70.34% (five convolutional layers), whereas for CIFAR-10 the accuracy slightly decreases when five convolutional layers are used.. The results are shown in the last four columns of Table II." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Average test accuracy (%) with respect to the number of layers in the local critic networks. [a,b,c] means that the numbers of convolutional layers in LC1, LC2, and LC3 are a, b, and c, respectively.'\nProblem: 'What is the accuracy for CIFAR-100 when three convolutional layers are used?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/174.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Average test accuracy (%) with respect to the number of layers in the local critic networks. [a,b,c] means that the numbers of convolutional layers in LC1, LC2, and LC3 are a, b, and c, respectively." ], "reasoning": "The table shows the accuracy for CIFAR-100 with different numbers of convolutional layers. For three convolutional layers, the accuracy is 70.02%.", "question": "What is the accuracy for CIFAR-100 when three convolutional layers are used?", "answer": "So, the answer is {\"answer\": \"70.02%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe accuracy for CIFAR-10 decreases when five convolutional layers are used.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/174.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Average test accuracy (%) with respect to the number of layers in the local critic networks. [a,b,c] means that the numbers of convolutional layers in LC1, LC2, and LC3 are a, b, and c, respectively." ], "reasoning": "The table shows that the accuracy for CIFAR-10 is 92.39% with one convolutional layer and decreases to 91.72% with five convolutional layers.", "statement": "The accuracy for CIFAR-10 decreases when five convolutional layers are used.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: FLOPs required for a feedforward pass and numbers of model parameters in the sub-models and main model for CIFAR-10. Note that sub-model 2 has less FLOPs and parameters than sub-model 1 due to the pooling operation in sub-model 2.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/175.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: FLOPs required for a feedforward pass and numbers of model parameters in the sub-models and main model for CIFAR-10. Note that sub-model 2 has less FLOPs and parameters than sub-model 1 due to the pooling operation in sub-model 2." ], "reasoning": "The computational and memory complexities of sub-models are significantly reduced compared to the main model, with Sub-model 3 showing a reduction to about 30% of the main model's FLOPs and parameters.", "description": "Table V shows the complexities of the sub-models in terms of the amount of computation for a feedforward pass and the number of weight parameters.. For CIFAR-10, the computational complexity in terms of the number of floating-point operations (FLOPs) and the memory complexity are reduced to only about 30% (15.72 to 4.52 million FLOPs, and 7.87 to 2.26 million parameters), as shown in Table V." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: FLOPs required for a feedforward pass and numbers of model parameters in the sub-models and main model for CIFAR-10. Note that sub-model 2 has less FLOPs and parameters than sub-model 1 due to the pooling operation in sub-model 2.'\nProblem: 'Which sub-model has the lowest computational complexity in terms of FLOPs?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/175.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: FLOPs required for a feedforward pass and numbers of model parameters in the sub-models and main model for CIFAR-10. Note that sub-model 2 has less FLOPs and parameters than sub-model 1 due to the pooling operation in sub-model 2." ], "reasoning": "To find the sub-model with the lowest computational complexity, compare the FLOPs of all sub-models. Sub-model 2 has the lowest FLOPs at 1.76M.", "question": "Which sub-model has the lowest computational complexity in terms of FLOPs?", "answer": "So, the answer is {\"answer\": \"Sub-model 2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSub-model 1 has fewer parameters than Sub-model 3.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/175.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: FLOPs required for a feedforward pass and numbers of model parameters in the sub-models and main model for CIFAR-10. Note that sub-model 2 has less FLOPs and parameters than sub-model 1 due to the pooling operation in sub-model 2." ], "reasoning": "According to the table, Sub-model 1 has 1.42M parameters, while Sub-model 3 has 2.26M parameters. Therefore, Sub-model 1 has fewer parameters than Sub-model 3.", "statement": "Sub-model 1 has fewer parameters than Sub-model 3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Average test accuracy (%) with respect to the update frequency of local critic networks.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/176.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Average test accuracy (%) with respect to the update frequency of local critic networks." ], "reasoning": "When the update frequency is halved, the accuracy drops by 0.48% and 1.92% for CIFAR-10 and CIFAR-100, respectively. Further decreasing the update frequency to 1/5 results in an additional accuracy drop of 0.56% for CIFAR-10 and 1.60% for CIFAR-100.", "description": "we compare different update frequency in Table III.. When the update frequency is a half of that for the main network (i.e., 1/2), the accuracy drops by 0.48% and 1.92% for the two datasets, respectively. Then, the decrease of the accuracy is only 0.56% for CIFAR-10 and 1.60% for CIFAR100 when the update frequency decreases from 1/2 to 1/5." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Average test accuracy (%) with respect to the update frequency of local critic networks.'\nProblem: 'What is the accuracy drop for CIFAR-100 when the update frequency decreases from 1/1 to 1/5?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/176.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Average test accuracy (%) with respect to the update frequency of local critic networks." ], "reasoning": "The accuracy for CIFAR-100 at 1/1 update frequency is 69.91%. At 1/5 update frequency, the accuracy is 66.39%. The drop in accuracy is 69.91% - 66.39% = 3.52%.", "question": "What is the accuracy drop for CIFAR-100 when the update frequency decreases from 1/1 to 1/5?", "answer": "So, the answer is {\"answer\": \"3.52%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe accuracy for CIFAR-10 decreases by 1.04% when the update frequency changes from 1/1 to 1/3.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/176.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Average test accuracy (%) with respect to the update frequency of local critic networks." ], "reasoning": "The accuracy for CIFAR-10 at 1/1 update frequency is 92.39%. At 1/3 update frequency, the accuracy is 91.78%. The drop in accuracy is 92.39% - 91.78% = 0.61%, not 1.04%.", "statement": "The accuracy for CIFAR-10 decreases by 1.04% when the update frequency changes from 1/1 to 1/3.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Average test accuracy (%) of the sub-models produced by local critic training and the networks trained by regular backpropagation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/177.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Average test accuracy (%) of the sub-models produced by local critic training and the networks trained by regular backpropagation." ], "reasoning": "Sub-model 3 shows similar accuracy to the main model for both CIFAR-10 and CIFAR-100 datasets.", "description": "Table IV compares the performance of the sub-models,. The largest sub-model (sub-model 3) shows similar accuracy to the main model (92.29% vs. 92.39% for CIFAR-10 and 67.54% vs. 69.91% for CIFAR-100),. the table also shows the accuracy of the networks that have the same structures with the sub-models but are trained using regular backpropagation." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Average test accuracy (%) of the sub-models produced by local critic training and the networks trained by regular backpropagation.'\nProblem: 'What is the accuracy difference between BP sub 3 and LC sub 3 for CIFAR-100?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/177.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Average test accuracy (%) of the sub-models produced by local critic training and the networks trained by regular backpropagation." ], "reasoning": "The accuracy for BP sub 3 on CIFAR-100 is 67.81%, and for LC sub 3 it is 67.54%. The difference is 67.81% - 67.54% = 0.27%.", "question": "What is the accuracy difference between BP sub 3 and LC sub 3 for CIFAR-100?", "answer": { "answer": "0.27%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nLC sub 2 has higher accuracy than BP sub 2 for CIFAR-10.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/177.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Average test accuracy (%) of the sub-models produced by local critic training and the networks trained by regular backpropagation." ], "reasoning": "The accuracy for LC sub 2 on CIFAR-10 is 90.53%, while for BP sub 2 it is 88.03%. Since 90.53% is greater than 88.03%, the statement is supported.", "statement": "LC sub 2 has higher accuracy than BP sub 2 for CIFAR-10.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VI: Average FLOPs and accuracy of progressive inference for test data of CIFAR-10 when the threshold is set to 0.9 or 0.95.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/178.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Average FLOPs and accuracy of progressive inference for test data of CIFAR-10 when the threshold is set to 0.9 or 0.95." ], "reasoning": "Increasing the threshold from 0.9 to 0.95 results in a smaller accuracy loss while maintaining a similar reduction in computational complexity.", "description": "The results are shown in Table VI.. When the threshold is 0.9, with only a slight loss of accuracy (92.39% to 91.18%), the computational complexity is reduced significantly, which is only 18.45% of that of the main model. When the threshold increases to 0.95, the accuracy loss becomes smaller (only 0.64%), while the complexity reduction remains almost the same (19.40% of the main model's complexity)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VI: Average FLOPs and accuracy of progressive inference for test data of CIFAR-10 when the threshold is set to 0.9 or 0.95.'\nProblem: 'What is the accuracy of the progressive inference model when the threshold is 0.95?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/178.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Average FLOPs and accuracy of progressive inference for test data of CIFAR-10 when the threshold is set to 0.9 or 0.95." ], "reasoning": "The table shows the accuracy for different thresholds. For a threshold of 0.95, the accuracy is listed as 91.75%.", "question": "What is the accuracy of the progressive inference model when the threshold is 0.95?", "answer": { "answer": "91.75%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe main model has a higher computational complexity than the progressive inference models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/178.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Average FLOPs and accuracy of progressive inference for test data of CIFAR-10 when the threshold is set to 0.9 or 0.95." ], "reasoning": "The table shows that the FLOP for the main model is 15.72M, which is higher than the FLOP for both progressive inference models (2.90M and 3.05M).", "statement": "The main model has a higher computational complexity than the progressive inference models.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Estimated in-sample MSE (13) of estimating the treatment effect on subsampled welfare data, averaged over 200 runs at each subsample size. We show estimated error from local linear causal forests (LLCF) and standard causal forests (CF). Tuning parameters were selected via cross-validation using the R-learner objective.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/179.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Estimated in-sample MSE (13) of estimating the treatment effect on subsampled welfare data, averaged over 200 runs at each subsample size. We show estimated error from local linear causal forests (LLCF) and standard causal forests (CF). Tuning parameters were selected via cross-validation using the R-learner objective." ], "reasoning": "The table shows that using local linear correction (LLCF) consistently reduces error estimates compared to the standard method (CF) across various subsample sizes.", "description": "Table 1 has error estimates for both types of forests using (13), and verifies that using the local linear correction improves empirical performance across different subsample sizes." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Estimated in-sample MSE (13) of estimating the treatment effect on subsampled welfare data, averaged over 200 runs at each subsample size. We show estimated error from local linear causal forests (LLCF) and standard causal forests (CF). Tuning parameters were selected via cross-validation using the R-learner objective.'\nProblem: 'What is the error estimate for LLCF at a subsample size of 800, and how does it compare to CF at the same subsample size?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/179.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Estimated in-sample MSE (13) of estimating the treatment effect on subsampled welfare data, averaged over 200 runs at each subsample size. We show estimated error from local linear causal forests (LLCF) and standard causal forests (CF). Tuning parameters were selected via cross-validation using the R-learner objective." ], "reasoning": "The error estimate for LLCF at a subsample size of 800 is 0.013, while for CF it is 0.015. Therefore, LLCF has a lower error estimate than CF at this subsample size.", "question": "What is the error estimate for LLCF at a subsample size of 800, and how does it compare to CF at the same subsample size?", "answer": "So, the answer is {\"answer\": \"0.013\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe error estimate for CF is higher than LLCF for all subsample sizes.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/179.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Estimated in-sample MSE (13) of estimating the treatment effect on subsampled welfare data, averaged over 200 runs at each subsample size. We show estimated error from local linear causal forests (LLCF) and standard causal forests (CF). Tuning parameters were selected via cross-validation using the R-learner objective." ], "reasoning": "The table shows that for every subsample size (200, 400, 800, 1200, 1500, 2000), the error estimate for CF is higher than that for LLCF.", "statement": "The error estimate for CF is higher than LLCF for all subsample sizes.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: RMSE from simulations on equation 1 on random forests, lasso-random forest, local linear forests, BART, and boosting. We vary sample size n, error variance σ, and ambient dimension d, and report test error on 1000 test points. We estimate Var[E[Y∣X]] as 3.52 over 10,000 Monte Carlo repetitions, so that signal-to-noise ratio ranges from 352 at σ=0.1 to 0.88 at σ=2. All errors are averaged over 50 runs, and minimizing errors are in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/180.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: RMSE from simulations on equation 1 on random forests, lasso-random forest, local linear forests, BART, and boosting. We vary sample size n, error variance σ, and ambient dimension d, and report test error on 1000 test points. We estimate Var[E[Y∣X]] as 3.52 over 10,000 Monte Carlo repetitions, so that signal-to-noise ratio ranges from 352 at σ=0.1 to 0.88 at σ=2. All errors are averaged over 50 runs, and minimizing errors are in bold." ], "reasoning": "Local linear forests (LLF) perform well in detecting strong linear signals, especially in the tail.", "description": "Local linear forests do quite well here; they detect the strong linear signal in the tail" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: RMSE from simulations on equation 1 on random forests, lasso-random forest, local linear forests, BART, and boosting. We vary sample size n, error variance σ, and ambient dimension d, and report test error on 1000 test points. We estimate Var[E[Y∣X]] as 3.52 over 10,000 Monte Carlo repetitions, so that signal-to-noise ratio ranges from 352 at σ=0.1 to 0.88 at σ=2. All errors are averaged over 50 runs, and minimizing errors are in bold.'\nProblem: 'What is the LLF value when d=5, n=1000, and σ=0.10?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/180.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: RMSE from simulations on equation 1 on random forests, lasso-random forest, local linear forests, BART, and boosting. We vary sample size n, error variance σ, and ambient dimension d, and report test error on 1000 test points. We estimate Var[E[Y∣X]] as 3.52 over 10,000 Monte Carlo repetitions, so that signal-to-noise ratio ranges from 352 at σ=0.1 to 0.88 at σ=2. All errors are averaged over 50 runs, and minimizing errors are in bold." ], "reasoning": "To find the LLF value, locate the row where d=5, n=1000, and σ=0.10. The LLF value in this row is 0.02.", "question": "What is the LLF value when d=5, n=1000, and σ=0.10?", "answer": "So, the answer is {\"answer\": \"0.02\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nLLF has the lowest value when d=50, n=5000, and σ=0.10.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/180.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: RMSE from simulations on equation 1 on random forests, lasso-random forest, local linear forests, BART, and boosting. We vary sample size n, error variance σ, and ambient dimension d, and report test error on 1000 test points. We estimate Var[E[Y∣X]] as 3.52 over 10,000 Monte Carlo repetitions, so that signal-to-noise ratio ranges from 352 at σ=0.1 to 0.88 at σ=2. All errors are averaged over 50 runs, and minimizing errors are in bold." ], "reasoning": "To verify this statement, locate the row where d=50, n=5000, and σ=0.10. The LLF value in this row is 0.07. Comparing it with other values in the same row, LLF does not have the lowest value; XGBoost has a lower value of 0.06.", "statement": "LLF has the lowest value when d=50, n=5000, and σ=0.10.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Global Graph Recovery and Mask Recovery Performances'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/181.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Global Graph Recovery and Mask Recovery Performances" ], "reasoning": "GL-conv has a high difference between recall and precision, leading to a poor F-score compared to other methods. ML and GL-conv have better MSE scores than GL-informed.", "description": "The findings are summarized in Table I.. GL-conv yields a high difference between the recall and the precision rate. which leads to poor F-score compared to other methods.. The MSE score of ML and GL-conv is better than the one of GL-informed.. ML achieves good rates on the" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Global Graph Recovery and Mask Recovery Performances'\nProblem: 'Which method has the highest recall rate in Global Graph Recovery?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/181.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Global Graph Recovery and Mask Recovery Performances" ], "reasoning": "To find the method with the highest recall rate in Global Graph Recovery, compare the recall rates of ML, GL-informed, and GL-conv. GL-conv has the highest recall rate at 100%.", "question": "Which method has the highest recall rate in Global Graph Recovery?", "answer": "So, the answer is {\"answer\": \"GL-conv\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nML has the highest precision rate in Mask Recovery.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/181.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Global Graph Recovery and Mask Recovery Performances" ], "reasoning": "To verify if ML has the highest precision rate in Mask Recovery, check the precision rate for Mask Recovery. ML has a precision rate of 92.57%, which is the only precision rate listed for Mask Recovery.", "statement": "ML has the highest precision rate in Mask Recovery.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Contribution of layers on the structure of different measurements'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/182.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Contribution of layers on the structure of different measurements" ], "reasoning": "Temperature has a higher percentage contribution from the altitude layer compared to the GPS layer, indicating a strong coherence with altitude similarity.", "description": "In Table II, the percentage of the connections that ML draws from the GPS and the altitude layer is given for different types of measurements that are used as signals. To begin with temperature, its structure seems to be highly coherent with the altitude similarity considering the percentage contribution of each layer." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Contribution of layers on the structure of different measurements'\nProblem: 'Which measurement has the highest percentage contribution from the GPS layer?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/182.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Contribution of layers on the structure of different measurements" ], "reasoning": "By examining the table, we see that 'Cloudy days' has the highest percentage contribution from the GPS layer at 65%.", "question": "Which measurement has the highest percentage contribution from the GPS layer?", "answer": "So, the answer is {\"answer\": \"Cloudy days\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nHumidity has a higher percentage contribution from the altitude layer than the GPS layer.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/182.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Contribution of layers on the structure of different measurements" ], "reasoning": "The table shows that humidity has a 51% contribution from the GPS layer and a 49% contribution from the altitude layer, indicating that the statement is incorrect.", "statement": "Humidity has a higher percentage contribution from the altitude layer than the GPS layer.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Mean inference time of the 3-nearest neighbor classifier in each dataset. The first column specifies the dataset. The second column shows the inference time of the 3-nearest neighbor classifier using the weighted pq-gram distance. The third column shows that the inference time of the 3-nearest neighbor classifier using the tree edit distance.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/183.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Mean inference time of the 3-nearest neighbor classifier in each dataset. The first column specifies the dataset. The second column shows the inference time of the 3-nearest neighbor classifier using the weighted pq-gram distance. The third column shows that the inference time of the 3-nearest neighbor classifier using the tree edit distance." ], "reasoning": "The proposed method significantly reduces inference times compared to the tree edit distance, especially in the Sentiment dataset where it is over 5000 times faster.", "description": "Table 3 shows the mean running time of 3-nearest neighbor inference for 5-fold cross-validation.. In all datasets, the weighted pqgram distance yields much shorter inference times than the tree edit distance. Especially in the Sentiment dataset, our proposed method yields over 5000 times shorter inference time than that using the tree edit distance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Mean inference time of the 3-nearest neighbor classifier in each dataset. The first column specifies the dataset. The second column shows the inference time of the 3-nearest neighbor classifier using the weighted pq-gram distance. The third column shows that the inference time of the 3-nearest neighbor classifier using the tree edit distance.'\nProblem: 'How much faster is the proposed method compared to the tree edit distance in the Glycan_EL dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/183.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Mean inference time of the 3-nearest neighbor classifier in each dataset. The first column specifies the dataset. The second column shows the inference time of the 3-nearest neighbor classifier using the weighted pq-gram distance. The third column shows that the inference time of the 3-nearest neighbor classifier using the tree edit distance." ], "reasoning": "The proposed method has a mean running time of 0.249 seconds, while the tree edit distance has a mean running time of 182.9 seconds. To find how much faster the proposed method is, divide 182.9 by 0.249.", "question": "How much faster is the proposed method compared to the tree edit distance in the Glycan_EL dataset?", "answer": "So, the answer is {\"answer\": \"734.54\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed method is faster than the tree edit distance in all datasets.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/183.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Mean inference time of the 3-nearest neighbor classifier in each dataset. The first column specifies the dataset. The second column shows the inference time of the 3-nearest neighbor classifier using the weighted pq-gram distance. The third column shows that the inference time of the 3-nearest neighbor classifier using the tree edit distance." ], "reasoning": "The table shows that the proposed method has shorter inference times compared to the tree edit distance in all listed datasets: Strings, Glycan_SP, Glycan_EL, Words, and Sentiment.", "statement": "The proposed method is faster than the tree edit distance in all datasets.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Rerank evaluation on real-world dataset with random splitting.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/184.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Rerank evaluation on real-world dataset with random splitting." ], "reasoning": "IRecGAN achieved the highest P@10 improvement, while PGIS performed worse than PG and PGU performed better than PG but worse than IRecGAN.", "description": "IRecGAN achieved encouraging P@10 improvement against all baselines.. PGIS did not perform as well as PG. PGU was able to fit the given data more accurately than PG. PGU performed worse than IRecGAN. LSTMD outperformed LSTM," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Rerank evaluation on real-world dataset with random splitting.'\nProblem: 'Which model achieved the highest P@10 improvement?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/184.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Rerank evaluation on real-world dataset with random splitting." ], "reasoning": "By comparing the P@10 values in the table, IRecGAN has the highest P@10 value of 35.06±0.48.", "question": "Which model achieved the highest P@10 improvement?", "answer": "So, the answer is {\"answer\": \"IRecGAN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPGU performed better than PGIS.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/184.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Rerank evaluation on real-world dataset with random splitting." ], "reasoning": "The table shows that PGU has a P@10 value of 34.12±0.52, which is higher than PGIS's P@10 value of 28.13±0.45.", "statement": "PGU performed better than PGIS in P@10.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Rerank evaluation on real-world recommendation dataset when split by session ID.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/185.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Rerank evaluation on real-world recommendation dataset when split by session ID." ], "reasoning": "LSTM and LSTMD performed better than RL agents in P@1, while RL agents had better P@10 results. LSTMD outperformed LSTM, and IRecGAN outperformed all RL methods in both P@1 and P@10.", "description": "To compare results under different data separation strategies, we evaluated models when splitting the dataset in the order of session ID or time, as shown in Table 2 and 3, respectively.. LSTM and LSTMD performed better than the RL agents in P@1. And the RL agents (IRecGAN and other RL baselines) had advantages in capturing users' overall interests, which led to better P@10 results.. LSTMD outperformed LSTM and IRecGAN outperformed all other RL-based methods in both P@1 and P@10.. By comparing the P@1 and P@1 (all), we observed that the differences between these two metrics in user behavior models (LSTM and LSTMD) were smaller than those between most RL agents. More specifically, most of RL agents performed better under the P@1 (all) metric than P@1, where the former included evaluations with less than 10 ranking candidates." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Rerank evaluation on real-world recommendation dataset when split by session ID.'\nProblem: 'Which model had the highest P@10 (%) score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/185.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Rerank evaluation on real-world recommendation dataset when split by session ID." ], "reasoning": "By comparing the P@10 (%) scores in the table, IRecGAN had the highest score of 33.45±0.71.", "question": "Which model had the highest P@10 (%) score?", "answer": "So, the answer is {\"answer\": \"IRecGAN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nLSTMD had a higher P@1 (%) score than PG.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/185.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Rerank evaluation on real-world recommendation dataset when split by session ID." ], "reasoning": "The table shows that LSTMD had a P@1 (%) score of 11.06±0.23, while PG had a score of 6.83±0.38, indicating that LSTMD had a higher score.", "statement": "LSTMD had a higher P@1 (%) score than PG.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparisons between fPCA, SLI and CSI under different values of ρ and μ.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/186.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparisons between fPCA, SLI and CSI under different values of ρ and μ." ], "reasoning": "CSI consistently outperforms SLI and fPCA across different treatment effects and observation rates, with SLI performing better than fPCA.", "description": "The results are presented in Table 1 and Figure 1. From Table 1 and the left plot of Figure 1, we have the following findings:. . CSI achieves better performance than SLI and fPCA, regardless of the treatment effect µ and observation rate ρ. Meanwhile SLI performs better than fPCA. 2. All three methods give comparable errors for smaller values of µ. In particular, our introduc tion of treatment effect µ does not over-fit the model in the case of µ = 0. 3. As the treatment effect µ increases, the performance of CSI remains the same whereas the performances of SLI and fPCA deteriorate rapidly. As a result, CSI outperforms SLI and fPCA by a significant margin for large values of µ. For example, when ρ = 0.1, the MSE( ˆY ) of CSI decreases from 72.3% of SLI and 59.6% of fPCA at µ = 1 to 12.4% of SLI and 5.8% of fPCA at µ = 5. 4. All three algorithms suffer a higher MSE( ˆY ) with smaller observation rate ρ. The biggest decay comes from SLI with an average 118% increase in test error from ρ = 0.5 to ρ = 0.1. The performances of fPCA and CSI remains comparatively stable among different observation rate with a 6% and 12% increase respectively. This implies that our algorithm is tolerant to low observation rate." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparisons between fPCA, SLI and CSI under different values of ρ and μ.'\nProblem: 'What is the MSE(Y) of CSI when the observation rate ρ is 0.1 and the treatment effect µ is 1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/186.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparisons between fPCA, SLI and CSI under different values of ρ and μ." ], "reasoning": "From the table, locate the row for CSI and the column where the observation rate ρ is 0.1 and the treatment effect µ is 1. The value is 0.311.", "question": "What is the MSE(Y) of CSI when the observation rate ρ is 0.1 and the treatment effect µ is 1?", "answer": "So, the answer is {\"answer\": \"0.311\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCSI has a lower MSE(Y) than SLI and fPCA when the observation rate ρ is 0.5 and the treatment effect µ is 1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/186.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparisons between fPCA, SLI and CSI under different values of ρ and μ." ], "reasoning": "From the table, for ρ = 0.5 and µ = 1, the MSE(Y) values are 0.294 for CSI, 0.341 for SLI, and 0.525 for fPCA. CSI has the lowest value.", "statement": "CSI has a lower MSE(Y) than SLI and fPCA when the observation rate ρ is 0.5 and the treatment effect µ is 1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Deployment Phase Results (Average over 200 runs)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/187.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Deployment Phase Results (Average over 200 runs)" ], "reasoning": "The QL-AMC configurations with cardinality 30 and 60 show the best spectral efficiency results.", "description": "Table III summarizes the results in the deployment phase in terms of average values for each configuration of the QL-AMC and baseline solution.. we see that the two QL-AMC configurations presenting the best results in terms of spectral efficiency are those with cardinality 30 and 60," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Deployment Phase Results (Average over 200 runs)'\nProblem: 'Which QL-AMC configuration has the highest spectral efficiency?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/187.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Deployment Phase Results (Average over 200 runs)" ], "reasoning": "By examining the table, the QL-AMC configuration with cardinality 60 and reward SE has the highest spectral efficiency of 4.1553.", "question": "Which QL-AMC configuration has the highest spectral efficiency?", "answer": "So, the answer is {\"answer\": \"QL-AMC with cardinality 60 and reward SE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe QL-AMC configuration with cardinality 15 and reward SE has a spectral efficiency higher than 3.9.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/187.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Deployment Phase Results (Average over 200 runs)" ], "reasoning": "The table shows that the QL-AMC configuration with cardinality 15 and reward SE has a spectral efficiency of 3.8207, which is less than 3.9.", "statement": "The QL-AMC configuration with cardinality 15 and reward SE has a spectral efficiency higher than 3.9.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Figure 2: Test error on GDI dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/188.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 2: Test error on GDI dataset" ], "reasoning": "CSI achieves the lowest mean test error and the smallest standard deviation, improving the result of SLI by 7%.", "description": "We run all five methods on the same training/validation/test set for 40 times and compare the mean and sd of test-errors. The results are presented in Table 2 and Figure 2. Compared with the null model pMean (Column 2 of Table 2), fPCA gives roughly the same order of error; CSI, SLI and rowMean provide better predictions, achieving 62%, 66% and 73% of the test errors respectively. In particular, our algorithm CSI improves the result of vanilla model SLI by 7%, it also provide a stable estimation with the smallest sd across multiple selections of test sets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Figure 2: Test error on GDI dataset'\nProblem: 'Which method provides the most stable estimation across multiple selections of test sets?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/188.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 2: Test error on GDI dataset" ], "reasoning": "The table shows the standard deviation (sd) for each method. The method with the smallest sd is the most stable. CSI has the smallest sd of 8.90.", "question": "Which method provides the most stable estimation across multiple selections of test sets?", "answer": { "answer": "CSI" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe fPCA method has a higher mean test error than the pMean method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/188.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 2: Test error on GDI dataset" ], "reasoning": "The table shows that the mean test error for fPCA is 127.73, while for pMean it is 119.80. Since 127.73 is higher than 119.80, the statement is correct.", "statement": "The fPCA method has a higher mean test error than the pMean method.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Accuracy of pairwise verification and KNN identification tasks for point embeddings, and our hedged embeddings with a single Gaussian component (MoG-1) and two components (MoG-2). We report results for images with N digits and using D embedding dimensions.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/189.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy of pairwise verification and KNN identification tasks for point embeddings, and our hedged embeddings with a single Gaussian component (MoG-1) and two components (MoG-2). We report results for images with N digits and using D embedding dimensions." ], "reasoning": "HIB shows improved performance over point embeddings, especially for corrupted test images, in both the verification AP and KNN identification tasks.", "description": "The average | precision (AP) of this task is reported in the top half of Table 1. HIB shows improved performance, especially for corrupted test images. For example, in the. = 2 digit case, when using D = 2 dimensions, point embeddings achieve 88.0% AP on corrupt test images, while hedged instance embeddings improves to 90.7% with. = 1 Gaussian, and 91.2% with. = 2 Gaussians.. We next measure performance on the KNN identification task. The bottom half of Table 1 reports the results. In the KNN experiment, each clean image is identified by comparing to the test gallery which is either comprised of clean or corrupted images based on its K = 5 nearest neighbors. The identification is considered correct only if a majority of the neighbors have the correct class. Again, the proposed stochastic embeddings generally outperform point embeddings, with the greatest advantage for the corrupted input samples. For example, in the. = 2 digit case, when using D = 2 dimensions, point embeddings achieve 58.3% AP on corrupt test images, while HIB improves to. 6.0% with. = 1 Gaussian, and 75.7% with. = 2 Gaussians." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Accuracy of pairwise verification and KNN identification tasks for point embeddings, and our hedged embeddings with a single Gaussian component (MoG-1) and two components (MoG-2). We report results for images with N digits and using D embedding dimensions.'\nProblem: 'What is the KNN accuracy for clean images when N=2, D=3, and using MoG=2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/189.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy of pairwise verification and KNN identification tasks for point embeddings, and our hedged embeddings with a single Gaussian component (MoG-1) and two components (MoG-2). We report results for images with N digits and using D embedding dimensions." ], "reasoning": "To find the KNN accuracy for clean images with N=2, D=3, and MoG=2, locate the corresponding cell in the table under the 'KNN Accuracy' row and the specified column.", "question": "What is the KNN accuracy for clean images when N=2, D=3, and using MoG=2?", "answer": "So, the answer is {\"answer\": \"0.939\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe verification AP for corrupt images is higher when using N=2, D=2, and MoG=2 compared to N=2, D=2, and point embeddings.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/189.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy of pairwise verification and KNN identification tasks for point embeddings, and our hedged embeddings with a single Gaussian component (MoG-1) and two components (MoG-2). We report results for images with N digits and using D embedding dimensions." ], "reasoning": "Compare the verification AP for corrupt images under N=2, D=2, MoG=2 (0.912) with N=2, D=2, point embeddings (0.880). The value for MoG=2 is higher.", "statement": "The verification AP for corrupt images is higher when using N=2, D=2, and MoG=2 compared to N=2, D=2, and point embeddings.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Accuracy of pairwise verification and KNN identification tasks for point embeddings, and our hedged embeddings with a single Gaussian component (MoG-1) and two components (MoG-2). We report results for images with N digits and using D embedding dimensions.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/190.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy of pairwise verification and KNN identification tasks for point embeddings, and our hedged embeddings with a single Gaussian component (MoG-1) and two components (MoG-2). We report results for images with N digits and using D embedding dimensions." ], "reasoning": "HIB shows improved performance over point embeddings, especially for corrupted test images, in both AP and KNN tasks.", "description": "The average precision (AP) of this task is reported in the top half of Table 1. HIB shows improved performance, especially for corrupted test images. For example, in the. = 2 digit case, when using D = 2 dimensions, point embeddings achieve 88.0% AP on corrupt test images, while hedged instance embeddings improves to 90.7% with. = 1 Gaussian, and 91.2% with. = 2 Gaussians. We next measure performance on the KNN identification task. The bottom half of Table 1 reports the results. In the KNN experiment, each clean image is identified by comparing to the test gallery which is either comprised of clean or corrupted images based on its K = 5 nearest neighbors. The identification is considered correct only if a majority of the neighbors have the correct class. Again, the proposed stochastic embeddings generally outperform point embeddings, with the greatest advantage for the corrupted input samples. For example, in the. = 2 digit case, when using D = 2 dimensions, point embeddings achieve 58.3% AP on corrupt test images, while HIB improves to 76.0% with. = 1 Gaussian, and 75.7% with. = 2 Gaussians." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Accuracy of pairwise verification and KNN identification tasks for point embeddings, and our hedged embeddings with a single Gaussian component (MoG-1) and two components (MoG-2). We report results for images with N digits and using D embedding dimensions.'\nProblem: 'What is the AP correlation for clean images when N=2, D=3, and MoG=1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/190.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy of pairwise verification and KNN identification tasks for point embeddings, and our hedged embeddings with a single Gaussian component (MoG-1) and two components (MoG-2). We report results for images with N digits and using D embedding dimensions." ], "reasoning": "To find the AP correlation for clean images when N=2, D=3, and MoG=1, locate the corresponding cell in the table under the AP Correlation row for clean images.", "question": "What is the AP correlation for clean images when N=2, D=3, and MoG=1?", "answer": "So, the answer is {\"answer\": \"0.68\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe KNN correlation for corrupted images is higher when N=3, D=2, and MoG=1 compared to N=2, D=2, and MoG=1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/190.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy of pairwise verification and KNN identification tasks for point embeddings, and our hedged embeddings with a single Gaussian component (MoG-1) and two components (MoG-2). We report results for images with N digits and using D embedding dimensions." ], "reasoning": "The KNN correlation for corrupted images when N=3, D=2, and MoG=1 is 0.50, while for N=2, D=2, and MoG=1 it is 0.47. Therefore, the statement is supported.", "statement": "The KNN correlation for corrupted images is higher when N=3, D=2, and MoG=1 compared to N=2, D=2, and MoG=1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Task performance and uncertainty measure correlations with task performance for 4D embeddings with 3-digit MNIST. (a) Task accuracies for pairwise verification and KNN identification.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/191.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Task performance and uncertainty measure correlations with task performance for 4D embeddings with 3-digit MNIST. (a) Task accuracies for pairwise verification and KNN identification." ], "reasoning": "The table shows that 4D embeddings with 3-digit MNIST achieve high verification accuracy on clean images, and HIB improves performance on corrupt images.", "description": "In Table 4, we report the results of 4D embeddings with 3-digit MNIST. Here, the task performance begins to saturate, with verification accuracy on clean input images above 0.99. However, we again observe that HIB provides a slight performance improvement over point embeddings for corrupt images, and task performance correlates with uncertainty." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Task performance and uncertainty measure correlations with task performance for 4D embeddings with 3-digit MNIST. (a) Task accuracies for pairwise verification and KNN identification.'\nProblem: 'What is the KNN accuracy for clean images using N=3, D=4 MoG-2 embeddings?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/191.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Task performance and uncertainty measure correlations with task performance for 4D embeddings with 3-digit MNIST. (a) Task accuracies for pairwise verification and KNN identification." ], "reasoning": "To find the KNN accuracy for clean images using N=3, D=4 MoG-2 embeddings, look at the KNN Accuracy row for clean images under the N=3, D=4 MoG-2 column.", "question": "What is the KNN accuracy for clean images using N=3, D=4 MoG-2 embeddings?", "answer": "So, the answer is {\"answer\": \"0.922\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe verification accuracy for corrupt images is highest with N=3, D=4 MoG-2 embeddings.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/191.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Task performance and uncertainty measure correlations with task performance for 4D embeddings with 3-digit MNIST. (a) Task accuracies for pairwise verification and KNN identification." ], "reasoning": "The table shows the verification accuracy for corrupt images under different embeddings. The highest value is 0.948, which is under the N=3, D=4 MoG-2 column.", "statement": "The verification accuracy for corrupt images is highest with N=3, D=4 MoG-2 embeddings.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Task performance and uncertainty measure correlations with task performance for 4D embeddings with 3-digit MNIST. (b) Correlations between uncertainty, η(x), and task performances.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/192.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Task performance and uncertainty measure correlations with task performance for 4D embeddings with 3-digit MNIST. (b) Correlations between uncertainty, η(x), and task performances." ], "reasoning": "The table shows that HIB embeddings slightly improve performance over point embeddings for corrupt images, with verification accuracy above 0.99 for clean images.", "description": "In Table 4, we report the results of 4D embeddings with 3-digit MNIST. Here, the task performance begins to saturate, with verification accuracy on clean input images above 0.99. However, we again observe that HIB provides a slight performance improvement over point embeddings for corrupt images, and task performance correlates with uncertainty." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Task performance and uncertainty measure correlations with task performance for 4D embeddings with 3-digit MNIST. (b) Correlations between uncertainty, η(x), and task performances.'\nProblem: 'What is the AP correlation for corrupt images in the N=3, D=4 MoG-1 setting?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/192.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Task performance and uncertainty measure correlations with task performance for 4D embeddings with 3-digit MNIST. (b) Correlations between uncertainty, η(x), and task performances." ], "reasoning": "To find the AP correlation for corrupt images in the N=3, D=4 MoG-1 setting, look at the row labeled 'corrupt' under 'AP Correlation' and the column labeled 'N=3, D=4 MoG-1'.", "question": "What is the AP correlation for corrupt images in the N=3, D=4 MoG-1 setting?", "answer": "So, the answer is {\"answer\": \"0.79\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe KNN correlation for clean images is higher in the N=3, D=4 MoG-2 setting than in the N=3, D=4 MoG-1 setting.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/192.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Task performance and uncertainty measure correlations with task performance for 4D embeddings with 3-digit MNIST. (b) Correlations between uncertainty, η(x), and task performances." ], "reasoning": "To verify this statement, compare the KNN correlation values for clean images in both settings. The value for N=3, D=4 MoG-2 is 0.32, which is higher than the value for N=3, D=4 MoG-1, which is 0.31.", "statement": "The KNN correlation for clean images is higher in the N=3, D=4 MoG-2 setting than in the N=3, D=4 MoG-1 setting.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results for single Gaussian embeddings with and without KL divergence term. We report results for images with N digits and using D embedding dimensions. (a) Task performances.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/193.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results for single Gaussian embeddings with and without KL divergence term. We report results for images with N digits and using D embedding dimensions. (a) Task performances." ], "reasoning": "Including the KL term (β = 10−4) improves both main task performance and uncertainty quality, as seen in the increased KNN accuracy and better Kendall’s tau correlation for clean and corrupt inputs.", "description": "For quantitative analysis on the impact of β on main task performance and uncertainty quality, see Table 5 where we compare β = 0 and β = 10−4cases. We observe mild improvements in main task performances when the KL divergence regularization is used. For example, KNN accuracy improves from 0.685 to 0.730 by including the KL term for. = 3, D = 3 case. Improvement in uncertainty quality, measured in terms of Kendall’s tau correlation, is more pronounced. For example, KNN accuracy and uncertainty are nearly uncorrelated when β = 0 under the. = 3, D = 3 setting (-0.080 for clean and 0.183 for corrupt inputs), while they are well-correlated when β = 10−4 (0.685 for clean and 0.549 for corrupt inputs)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Results for single Gaussian embeddings with and without KL divergence term. We report results for images with N digits and using D embedding dimensions. (a) Task performances.'\nProblem: 'What is the KNN accuracy for clean inputs when β = 10−4 and N=3, D=3?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/193.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results for single Gaussian embeddings with and without KL divergence term. We report results for images with N digits and using D embedding dimensions. (a) Task performances." ], "reasoning": "To find the KNN accuracy for clean inputs when β = 10−4 and N=3, D=3, locate the corresponding cell in the table. The value is 0.861.", "question": "What is the KNN accuracy for clean inputs when β = 10−4 and N=3, D=3?", "answer": "So, the answer is {\"answer\": \"0.861\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIncluding the KL term (β = 10−4) improves KNN accuracy for corrupt inputs when N=2, D=2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/193.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results for single Gaussian embeddings with and without KL divergence term. We report results for images with N digits and using D embedding dimensions. (a) Task performances." ], "reasoning": "The table shows that KNN accuracy for corrupt inputs improves from 0.729 to 0.781 when β changes from 0 to 10−4 for N=2, D=2.", "statement": "Including the KL term (β = 10−4) improves KNN accuracy for corrupt inputs when N=2, D=2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results for single Gaussian embeddings with and without KL divergence term. We report results for images with N digits and using D embedding dimensions. (b) Uncertainty correlations.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/194.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results for single Gaussian embeddings with and without KL divergence term. We report results for images with N digits and using D embedding dimensions. (b) Uncertainty correlations." ], "reasoning": "Including the KL term (β = 10−4) improves both main task performance and uncertainty quality compared to β = 0, with notable improvements in KNN accuracy and Kendall’s tau correlation.", "description": "For quantitative analysis on the impact of β on main task performance and uncertainty quality, see Table 5 where we compare β = 0 and β = 10−4cases. We observe mild improvements in main task performances when the KL divergence regularization is used. For example, KNN accuracy improves from 0.685 to 0.730 by including the KL term for. = 3, D = 3 case. Improvement in uncertainty quality, measured in terms of Kendall’s tau correlation, is more pronounced. For example, KNN accuracy and uncertainty are nearly uncorrelated when β = 0 under the. = 3, D = 3 setting (-0.080 for clean and 0.183 for corrupt inputs), while they are well-correlated when β = 10−4 (0.685 for clean and 0.549 for corrupt inputs)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Results for single Gaussian embeddings with and without KL divergence term. We report results for images with N digits and using D embedding dimensions. (b) Uncertainty correlations.'\nProblem: 'What is the KNN correlation for clean inputs when β = 10−4 and N=2, D=2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/194.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results for single Gaussian embeddings with and without KL divergence term. We report results for images with N digits and using D embedding dimensions. (b) Uncertainty correlations." ], "reasoning": "To find the KNN correlation for clean inputs when β = 10−4 and N=2, D=2, look at the table under the column labeled 'N=2, D=2, 10−4' and the row labeled 'KNN Correlation' and 'clean'. The value is 0.800.", "question": "What is the KNN correlation for clean inputs when β = 10−4 and N=2, D=2?", "answer": "So, the answer is {\"answer\": \"0.800\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe KNN correlation for corrupt inputs when β = 0 and N=3, D=3 is 0.183.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/194.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results for single Gaussian embeddings with and without KL divergence term. We report results for images with N digits and using D embedding dimensions. (b) Uncertainty correlations." ], "reasoning": "To verify this statement, look at the table under the column labeled 'N=3, D=3, 0' and the row labeled 'KNN Correlation' and 'corrupt'. The value is indeed 0.183.", "statement": "The KNN correlation for corrupt inputs when β = 0 and N=3, D=3 is 0.183.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Comparison of all the methods on the UCF-Crime Dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/195.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Comparison of all the methods on the UCF-Crime Dataset." ], "reasoning": "The proposed method with the new ranking loss function achieves the highest AUC score compared to other state-of-the-art methods, demonstrating its effectiveness.", "description": "As shown in Figures 2, 3 and in Table I, ROC-Curve and UC methods show the quantitative comparisons of our algorithm with other state-of-art methods.. from the Table. and Fig. 3, we can say that our proposed method gives the highest AUC score in all. Additionally, the effectiveness of our proposed ranking loss function is also shown. From these comparisons, it is shown that our proposed algorithm along with the proposed ranking loss shows the best performance in other state-of-art methods." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Comparison of all the methods on the UCF-Crime Dataset.'\nProblem: 'What is the AUC score of the proposed method with the new ranking loss function?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/195.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Comparison of all the methods on the UCF-Crime Dataset." ], "reasoning": "The table shows the AUC scores of various algorithms, and the proposed method with the new ranking loss function has the highest AUC score.", "question": "What is the AUC score of the proposed method with the new ranking loss function?", "answer": "So, the answer is {\"answer\": \"76.67\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed method with the new ranking loss function has an AUC score higher than 75.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/195.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Comparison of all the methods on the UCF-Crime Dataset." ], "reasoning": "The table shows that the AUC score of the proposed method with the new ranking loss function is 76.67, which is higher than 75.", "statement": "The proposed method with the new ranking loss function has an AUC score higher than 75.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison of failure prediction methods on various datasets. All methods share the same classification network. Note that for MCDropout, test accuracy is averaged over random sampling. All values are percentages.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/196.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of failure prediction methods on various datasets. All methods share the same classification network. Note that for MCDropout, test accuracy is averaged over random sampling. All values are percentages." ], "reasoning": "The table compares failure prediction methods on various datasets, showing metrics like FPR, AUPR-Error, AUPR-Success, and AUC for each method.", "description": "Table 1: Comparison of failure prediction methods on various datasets. All methods share the same classification network. Note that for MCDropout, test accuracy is averaged over random sampling. All values are percentages." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison of failure prediction methods on various datasets. All methods share the same classification network. Note that for MCDropout, test accuracy is averaged over random sampling. All values are percentages.'\nProblem: 'Which method has the lowest FPR-95%-TPR on the MNIST Small ConvNet dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/196.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of failure prediction methods on various datasets. All methods share the same classification network. Note that for MCDropout, test accuracy is averaged over random sampling. All values are percentages." ], "reasoning": "To find the method with the lowest FPR-95%-TPR on the MNIST Small ConvNet dataset, look at the FPR-95%-TPR column for the MNIST Small ConvNet rows. The lowest value is 3.33, which corresponds to the ConfidNet (Ours) method.", "question": "Which method has the lowest FPR-95%-TPR on the MNIST Small ConvNet dataset?", "answer": "So, the answer is {\"answer\": \"ConfidNet (Ours)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTrustScore has the highest AUC on the CIFAR-10 VGG16 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/196.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of failure prediction methods on various datasets. All methods share the same classification network. Note that for MCDropout, test accuracy is averaged over random sampling. All values are percentages." ], "reasoning": "To verify this statement, check the AUC values for the CIFAR-10 VGG16 dataset. TrustScore has an AUC of 88.47, while ConfidNet (Ours) has a higher AUC of 92.12.", "statement": "TrustScore has the highest AUC on the CIFAR-10 VGG16 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Accuracy evaluation with mPCKh@0.5.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/197.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy evaluation with mPCKh@0.5." ], "reasoning": "The NADS-Net model scored an average mPCKh of 84% over all key points, outperforming the baseline model which scored 82%. NADS-Net performs better on Asian populations and white populations compared to people with darker skin tones.", "description": "We compared mPCKh scores of NADS-Net model and the baseline model for each individual key point location. As reported at the bottom of Table 2, the baseline model scored the average mPCKh of 82% over all key points while NADS-Net model scored 84%. Unlike the baseline model, NADS-Net does not have refining stages and have fewer parameters, but PCK-wise, shows a similar or slightly better performance.. Race-wise, the model performance was slightly better on Asian populations followed by white populations. Per- formance on people with darker skin tone was noticeably lower," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Accuracy evaluation with mPCKh@0.5.'\nProblem: 'What is the average mPCKh score of the NADS-Net model trained with both data sets combined?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/197.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy evaluation with mPCKh@0.5." ], "reasoning": "The table shows that the NADS-Net model trained with both data sets combined has an overall mPCKh score of 89%.", "question": "What is the average mPCKh score of the NADS-Net model trained with both data sets combined?", "answer": "So, the answer is {\"answer\": \"89%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe NADS-Net model trained with the new driving data set only has a higher overall mPCKh score than the baseline model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/197.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy evaluation with mPCKh@0.5." ], "reasoning": "The table shows that the NADS-Net model trained with the new driving data set only has an overall mPCKh score of 77%, which is lower than the baseline model's score of 82%.", "statement": "The NADS-Net model trained with the new driving data set only has a higher overall mPCKh score than the baseline model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Mean absolute error of formation energy predictions for V-RF, SchNet and the proposed model. For QM9 the error is in meV and for the Materials Project and OQMD the numbers are in meV/atom. The lowest error is highlighted in bold. We have obtained the V-RF results by running the implementation provided by the authors (Ward et al., 2017), while SchNet results have been obtained by running our own SchNet implementation. The numbers in parenthesis are the estimated 95th percentile, which have been obtained by sampling the test set (with replacement) 1×106 times.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/198.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Mean absolute error of formation energy predictions for V-RF, SchNet and the proposed model. For QM9 the error is in meV and for the Materials Project and OQMD the numbers are in meV/atom. The lowest error is highlighted in bold. We have obtained the V-RF results by running the implementation provided by the authors (Ward et al., 2017), while SchNet results have been obtained by running our own SchNet implementation. The numbers in parenthesis are the estimated 95th percentile, which have been obtained by sampling the test set (with replacement) 1×106 times." ], "reasoning": "The proposed model shows the lowest mean absolute error in formation energy predictions across all three benchmarks (QM9, Materials Project, and OQMD) compared to V-RF and SchNet.", "description": "Table 2: Mean absolute error of formation energy predictions for V-RF, SchNet and the proposed model. For QM9 the error is in meV and for the Materials Project and OQMD the numbers are in meV/atom. The lowest error is highlighted in bold. We have obtained the V-RF results by running the implementation provided by the authors (Ward et al., 2017), while SchNet results have been obtained by running our own SchNet implementation. The numbers in parenthesis are the estimated 95th percentile, which have been obtained by sampling the test set (with replacement) 1 × 106 times.. The mean absolute error for the test set predictions are shown in Table 2. In all three benchmarks we observe a big improvement in prediction accuracy when using the edge update network." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Mean absolute error of formation energy predictions for V-RF, SchNet and the proposed model. For QM9 the error is in meV and for the Materials Project and OQMD the numbers are in meV/atom. The lowest error is highlighted in bold. We have obtained the V-RF results by running the implementation provided by the authors (Ward et al., 2017), while SchNet results have been obtained by running our own SchNet implementation. The numbers in parenthesis are the estimated 95th percentile, which have been obtained by sampling the test set (with replacement) 1×106 times.'\nProblem: 'Which model has the lowest mean absolute error for the Materials Project benchmark?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/198.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Mean absolute error of formation energy predictions for V-RF, SchNet and the proposed model. For QM9 the error is in meV and for the Materials Project and OQMD the numbers are in meV/atom. The lowest error is highlighted in bold. We have obtained the V-RF results by running the implementation provided by the authors (Ward et al., 2017), while SchNet results have been obtained by running our own SchNet implementation. The numbers in parenthesis are the estimated 95th percentile, which have been obtained by sampling the test set (with replacement) 1×106 times." ], "reasoning": "By comparing the mean absolute errors for the Materials Project benchmark, the proposed model has the lowest error at 22.7 meV/atom.", "question": "Which model has the lowest mean absolute error for the Materials Project benchmark?", "answer": { "answer": "Proposed Model" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSchNet has the lowest mean absolute error for the QM9 benchmark.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/198.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Mean absolute error of formation energy predictions for V-RF, SchNet and the proposed model. For QM9 the error is in meV and for the Materials Project and OQMD the numbers are in meV/atom. The lowest error is highlighted in bold. We have obtained the V-RF results by running the implementation provided by the authors (Ward et al., 2017), while SchNet results have been obtained by running our own SchNet implementation. The numbers in parenthesis are the estimated 95th percentile, which have been obtained by sampling the test set (with replacement) 1×106 times." ], "reasoning": "The table shows that the proposed model has a lower mean absolute error (10.5 meV) for the QM9 benchmark compared to SchNet (13.6 meV).", "statement": "SchNet has the lowest mean absolute error for the QM9 benchmark.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Mean absolute error of predictions for different target properties of the QM9 dataset using 110k training examples. The lowest error is highlighted in bold. SchNet and enn-s2s results are from (Schütt et al., 2017c) and (Gilmer et al., 2017) respectively. The numbers in parenthesis are the estimated 95th percentile, which have been obtained by sampling the test set (with replacement) 1×106 times.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/199.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Mean absolute error of predictions for different target properties of the QM9 dataset using 110k training examples. The lowest error is highlighted in bold. SchNet and enn-s2s results are from (Schütt et al., 2017c) and (Gilmer et al., 2017) respectively. The numbers in parenthesis are the estimated 95th percentile, which have been obtained by sampling the test set (with replacement) 1×106 times." ], "reasoning": "The table compares the mean absolute error of predictions for different target properties of the QM9 dataset using 110k training examples, highlighting the lowest error for each property.", "description": "Mean absolute error of predictions for different target properties of the QM9 dataset using 110k training examples. The lowest error is highlighted in bold. SchNet and enn-s2s results are from (Schütt et al., 2017c) and (Gilmer et al., 2017) respectively. The numbers in parenthesis are the estimated 95th percentile, which have been obtained by sampling the test set (with replacement) 1 × 106 times." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Mean absolute error of predictions for different target properties of the QM9 dataset using 110k training examples. The lowest error is highlighted in bold. SchNet and enn-s2s results are from (Schütt et al., 2017c) and (Gilmer et al., 2017) respectively. The numbers in parenthesis are the estimated 95th percentile, which have been obtained by sampling the test set (with replacement) 1×106 times.'\nProblem: 'Which method has the lowest mean absolute error for the target property ZPVE?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/199.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Mean absolute error of predictions for different target properties of the QM9 dataset using 110k training examples. The lowest error is highlighted in bold. SchNet and enn-s2s results are from (Schütt et al., 2017c) and (Gilmer et al., 2017) respectively. The numbers in parenthesis are the estimated 95th percentile, which have been obtained by sampling the test set (with replacement) 1×106 times." ], "reasoning": "To find the method with the lowest mean absolute error for ZPVE, look at the ZPVE row and compare the values under SchNet, enn-s2s, and Proposed. The lowest value is highlighted in bold.", "question": "Which method has the lowest mean absolute error for the target property ZPVE?", "answer": { "answer": "Proposed" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed method has the lowest mean absolute error for the target property α.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/199.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Mean absolute error of predictions for different target properties of the QM9 dataset using 110k training examples. The lowest error is highlighted in bold. SchNet and enn-s2s results are from (Schütt et al., 2017c) and (Gilmer et al., 2017) respectively. The numbers in parenthesis are the estimated 95th percentile, which have been obtained by sampling the test set (with replacement) 1×106 times." ], "reasoning": "To verify this statement, look at the α row and compare the values under SchNet, enn-s2s, and Proposed. The lowest value is highlighted in bold.", "statement": "The proposed method has the lowest mean absolute error for the target property α.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Training Choices: Unconditional Case'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/200.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Training Choices: Unconditional Case" ], "reasoning": "The table compares the training choices for WaveNet and LSTM architectures, highlighting differences in parameters, initialization, and regularization.", "description": "It was found that the SGD works better and faster than the full gradient descent optimization with 20000 iterations from In the . Brief hyperparameter tuning was performed, notably for learning rate, mini batch size and number of epochs selection in the absence of a validation set.. Table 1 presents the training choices for each architecture. 6. The difference in number of parameters between a WaveNet and an LSTM architecture is noticeable.. The loss function is the median absolute error producing the one step ahead median estimate J(θ) = |ˆxt+1 − xt+1| (12) 1. T −1 t=0 where. is the size of the training set. In the convolutional neural network case the objective includes the term corresponding to the l2 penalty on the weights as well. The Xavier initialization gives uni). Conversely, the He initialization form initial weights in the range [−c, c], where c = ( 6 (nin+nout adjusts to the use of rectified linear units by using a scheme of the form. orm(0, ( 2 ni ))." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Training Choices: Unconditional Case'\nProblem: 'What is the difference in the number of parameters between the WaveNet and LSTM architectures?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/200.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Training Choices: Unconditional Case" ], "reasoning": "The table shows that WaveNet has 32 parameters while LSTM has 2726 parameters. The difference is calculated as 2726 - 32.", "question": "What is the difference in the number of parameters between the WaveNet and LSTM architectures?", "answer": "So, the answer is {\"answer\": \"2694\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LSTM architecture uses He initialization.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/200.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Training Choices: Unconditional Case" ], "reasoning": "The table indicates that the LSTM architecture uses Xavier initialization, not He initialization.", "statement": "The LSTM architecture uses He initialization.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Test set RMSE for one-step ahead forecast for x, y and z'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/201.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Test set RMSE for one-step ahead forecast for x, y and z" ], "reasoning": "The Conditional WaveNet model achieves the lowest RMSE for x prediction, significantly outperforming other models, including the Unconditional WaveNet and LSTM architectures.", "description": "The following table presents the RMSE for a range of scenarios calculated on the test set of size 500. Several random seeds were used, for example 1234, 1235 and 42. From the benchmarks (average over the three series) for gradient descent with 20000 iterations unconditional WaveNet architecture is , with conditional WaveNet is, with LSTM is 0.00675, and from with feed forward networks from 1992 the benchmark was 0.065.. Calculated over two sets of starting values and parameters for the Lorenz map, the Conditional WaveNet produces and average ranging between 0.004 and 0.007. The LSTM architecture ranges around 0.0063. The gains in speed versus full gradient descent are at least ten fold. Even if the time series are of short history, there are gains to using a stochastic gradient descent optimization routine. In Table 2 several itemized results means and standard deviations are reported.. The suspicion that rigorous tuning can lead to significant improvement is the 10 fold improvement in x prediction RMSE over best benchmark achieved uniformly over all runs with the conditional WaveNet and different Lorenz data distributions (different σ, r and b parameters)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Test set RMSE for one-step ahead forecast for x, y and z'\nProblem: 'Which model has the lowest RMSE for x prediction and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/201.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Test set RMSE for one-step ahead forecast for x, y and z" ], "reasoning": "The table shows that the Conditional WaveNet model has the lowest RMSE for x prediction with a mean value of 0.00047.", "question": "Which model has the lowest RMSE for x prediction and what is its value?", "answer": "So, the answer is {\"answer\": \"Conditional WaveNet with 0.00047\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Unconditional LSTM random sampling model has a lower RMSE for x prediction than the Conditional WaveNet model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/201.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Test set RMSE for one-step ahead forecast for x, y and z" ], "reasoning": "The table shows that the Unconditional LSTM random sampling model has an RMSE of 0.00300 for x prediction, which is higher than the Conditional WaveNet model's RMSE of 0.00047.", "statement": "The Unconditional LSTM random sampling model has a lower RMSE for x prediction than the Conditional WaveNet model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: AV dataset comparison. The top part of the table indicates datasets without range data. The middle and lower parts indicate datasets (not publications) with range data released until and after the initial release of this dataset. We use bold highlights to indicate the best entries in every column among the datasets with range data. Only datasets which provide annotations for at least car, pedestrian and bicycle are included in this comparison. (†) We report numbers only for scenes annotated with cuboids. (‡) The current Waymo Open dataset size is comparable to nuScenes, but at a 5x higher annotation frequency. (††) Lidar pointcloud count collected from each lidar. (**) [41] provides static depth maps. (-) indicates that no information is provided. SG: Singapore, NY: New York, SF: San Francisco, PT: Pittsburgh, AS: ApolloScape.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/202.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: AV dataset comparison. The top part of the table indicates datasets without range data. The middle and lower parts indicate datasets (not publications) with range data released until and after the initial release of this dataset. We use bold highlights to indicate the best entries in every column among the datasets with range data. Only datasets which provide annotations for at least car, pedestrian and bicycle are included in this comparison. (†) We report numbers only for scenes annotated with cuboids. (‡) The current Waymo Open dataset size is comparable to nuScenes, but at a 5x higher annotation frequency. (††) Lidar pointcloud count collected from each lidar. (**) [41] provides static depth maps. (-) indicates that no information is provided. SG: Singapore, NY: New York, SF: San Francisco, PT: Pittsburgh, AS: ApolloScape." ], "reasoning": "nuScenes significantly increases dataset size and capture frequency compared to KITTI and is one of the few datasets with a high capture frequency of 10Hz.", "description": "nuScenes represents a large leap forward in terms of data volumes and complexities (Table 1),. After the initial nuScenes release, [71, 10, 60, 33, 43] followed to release their own large-scale AV datasets (Table 1).. Only the Waymo Open dataset provides a similarly high capture frequency of 10Hz (Table 1).. One of the contributions of nuScenes is the dataset size, and in particular the increase compared to KITTI (Table 1)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: AV dataset comparison. The top part of the table indicates datasets without range data. The middle and lower parts indicate datasets (not publications) with range data released until and after the initial release of this dataset. We use bold highlights to indicate the best entries in every column among the datasets with range data. Only datasets which provide annotations for at least car, pedestrian and bicycle are included in this comparison. (†) We report numbers only for scenes annotated with cuboids. (‡) The current Waymo Open dataset size is comparable to nuScenes, but at a 5x higher annotation frequency. (††) Lidar pointcloud count collected from each lidar. (**) [41] provides static depth maps. (-) indicates that no information is provided. SG: Singapore, NY: New York, SF: San Francisco, PT: Pittsburgh, AS: ApolloScape.'\nProblem: 'Which dataset has the highest number of annotated frames and what is that number?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/202.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: AV dataset comparison. The top part of the table indicates datasets without range data. The middle and lower parts indicate datasets (not publications) with range data released until and after the initial release of this dataset. We use bold highlights to indicate the best entries in every column among the datasets with range data. Only datasets which provide annotations for at least car, pedestrian and bicycle are included in this comparison. (†) We report numbers only for scenes annotated with cuboids. (‡) The current Waymo Open dataset size is comparable to nuScenes, but at a 5x higher annotation frequency. (††) Lidar pointcloud count collected from each lidar. (**) [41] provides static depth maps. (-) indicates that no information is provided. SG: Singapore, NY: New York, SF: San Francisco, PT: Pittsburgh, AS: ApolloScape." ], "reasoning": "To find the dataset with the highest number of annotated frames, look at the 'Ann. frames' column and identify the dataset with the largest value.", "question": "Which dataset has the highest number of annotated frames?", "answer": "So, the answer is {\"answer\": \"D2-City\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nnuScenes has more RGB images than Waymo Open.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/202.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: AV dataset comparison. The top part of the table indicates datasets without range data. The middle and lower parts indicate datasets (not publications) with range data released until and after the initial release of this dataset. We use bold highlights to indicate the best entries in every column among the datasets with range data. Only datasets which provide annotations for at least car, pedestrian and bicycle are included in this comparison. (†) We report numbers only for scenes annotated with cuboids. (‡) The current Waymo Open dataset size is comparable to nuScenes, but at a 5x higher annotation frequency. (††) Lidar pointcloud count collected from each lidar. (**) [41] provides static depth maps. (-) indicates that no information is provided. SG: Singapore, NY: New York, SF: San Francisco, PT: Pittsburgh, AS: ApolloScape." ], "reasoning": "To verify this statement, compare the 'RGB imgs' column for nuScenes and Waymo Open. nuScenes has 1.4M RGB images, while Waymo Open has 1M RGB images. Therefore, nuScenes has more RGB images than Waymo Open.", "statement": "nuScenes has more RGB images than Waymo Open.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Object detection results on the test set of nuScenes. PointPillars, OFT and SSD+3D are baselines provided in this paper, other methods are the top submissions to the nuScenes detection challenge leaderboard. (†) use only monocular camera images as input. All other methods use lidar. PP: PointPillars [51], MDIS: MonoDIS [70].'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/203.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Object detection results on the test set of nuScenes. PointPillars, OFT and SSD+3D are baselines provided in this paper, other methods are the top submissions to the nuScenes detection challenge leaderboard. (†) use only monocular camera images as input. All other methods use lidar. PP: PointPillars [51], MDIS: MonoDIS [70]." ], "reasoning": "The detection challenge improved lidar-based and camera-based detection methods significantly, with PointPillars achieving higher NDS than MonoDIS despite similar mAP.", "description": "The detection challenge enabled lidar-based and camera-based detection works such as [84, 66], that improved over the state-of-the-art at the time of initial release [49, 65] by 40% and 81% (Table 4).. We compare performance of published methods (Table 4) when using our proposed 2m center-distance matching versus the IOU matching used in KITTI.. We compare PointPillars, which is a fast and light lidar detector with MonoDIS, a top image detector (Table 4). The two methods achieve similar mAP (30.5% vs. 30.4%), but PointPillars has higher NDS (45.3% vs. 38.4%).. Compared to the mAP and NDS detection results in Table 4, the ranking is similar." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Object detection results on the test set of nuScenes. PointPillars, OFT and SSD+3D are baselines provided in this paper, other methods are the top submissions to the nuScenes detection challenge leaderboard. (†) use only monocular camera images as input. All other methods use lidar. PP: PointPillars [51], MDIS: MonoDIS [70].'\nProblem: 'Which method has the highest NDS and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/203.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Object detection results on the test set of nuScenes. PointPillars, OFT and SSD+3D are baselines provided in this paper, other methods are the top submissions to the nuScenes detection challenge leaderboard. (†) use only monocular camera images as input. All other methods use lidar. PP: PointPillars [51], MDIS: MonoDIS [70]." ], "reasoning": "To find the method with the highest NDS, look at the NDS column and identify the highest value. The method with the highest NDS is Megvii with an NDS of 63.3%.", "question": "Which method has the highest NDS and what is its value?", "answer": "So, the answer is {\"answer\": \"Megvii\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPointPillars has a higher mAP than MonoDIS.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/203.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Object detection results on the test set of nuScenes. PointPillars, OFT and SSD+3D are baselines provided in this paper, other methods are the top submissions to the nuScenes detection challenge leaderboard. (†) use only monocular camera images as input. All other methods use lidar. PP: PointPillars [51], MDIS: MonoDIS [70]." ], "reasoning": "To verify this statement, compare the mAP values of PointPillars (PP) and MonoDIS (MDIS). PointPillars has an mAP of 30.5%, while MonoDIS has an mAP of 30.4%. Therefore, the statement is refuted.", "statement": "PointPillars has a lower mAP than MonoDIS.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: PointPillars [51] detection performance on the val set. We can see that more lidar sweeps lead to a significant performance increase and that pretraining with ImageNet is on par with KITTI.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/204.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: PointPillars [51] detection performance on the val set. We can see that more lidar sweeps lead to a significant performance increase and that pretraining with ImageNet is on par with KITTI." ], "reasoning": "Increasing the number of lidar sweeps improves detection and velocity estimates, but the rate of improvement diminishes. The network's final performance varies only slightly with different pretrainings.", "description": "The results show that both detection and velocity estimates improve with an increasing number of lidar sweeps but with diminishing rate of return (Table 3).. the final performance of the network only marginally varied between different pretrainings (Table 3)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: PointPillars [51] detection performance on the val set. We can see that more lidar sweeps lead to a significant performance increase and that pretraining with ImageNet is on par with KITTI.'\nProblem: 'What is the NDS percentage improvement when increasing lidar sweeps from 1 to 10 with KITTI pretraining?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/204.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: PointPillars [51] detection performance on the val set. We can see that more lidar sweeps lead to a significant performance increase and that pretraining with ImageNet is on par with KITTI." ], "reasoning": "To find the NDS percentage improvement, subtract the NDS value for 1 lidar sweep with KITTI pretraining from the NDS value for 10 lidar sweeps with KITTI pretraining.", "question": "What is the NDS percentage improvement when increasing lidar sweeps from 1 to 10 with KITTI pretraining?", "answer": "So, the answer is {\"answer\": \"13.0%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe mAVE value is lowest when using 10 lidar sweeps with KITTI pretraining.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/204.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: PointPillars [51] detection performance on the val set. We can see that more lidar sweeps lead to a significant performance increase and that pretraining with ImageNet is on par with KITTI." ], "reasoning": "Compare the mAVE values for all rows with 10 lidar sweeps. The lowest mAVE value is 0.30, which corresponds to KITTI pretraining.", "statement": "The mAVE value is lowest when using 10 lidar sweeps with KITTI pretraining.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Object detection performance drop evaluated on subsets of the nuScenes val set. Performance is reported as the relative drop in mAP compared to evaluating on the entire val set. We evaluate the performance on Singapore data, rain data and night data for three object detection methods. Note that the MDIS results are not directly comparable to other sections of this work, as a ResNet34 [39] backbone and a different training protocol are used. (†) use only monocular camera images as input. PP uses only lidar.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/205.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Object detection performance drop evaluated on subsets of the nuScenes val set. Performance is reported as the relative drop in mAP compared to evaluating on the entire val set. We evaluate the performance on Singapore data, rain data and night data for three object detection methods. Note that the MDIS results are not directly comparable to other sections of this work, as a ResNet34 [39] backbone and a different training protocol are used. (†) use only monocular camera images as input. PP uses only lidar." ], "reasoning": "PointPillars performs better for cars and pedestrians, while MonoDIS excels in bicycles and cones. PointPillars achieves only 1% AP for bicycles.", "description": "Class specifc performance is in Table 6-SM. PointPillars was stronger for the two most common classes: cars (68.4% vs. 47.8% AP), and pedestrians (59.7% vs. 37.0% AP). MonoDIS, on the other hand, was stronger for the smaller classes bicycles (24.5% vs. 1.1% AP) and cones (48.7% vs. 30.8% AP).. Pillars is shown in Table 6-SM (top) The per class performance of Point. In Section 4.2 and Table 6-SM we show that the PointPillars baseline achieves only an AP of 1% on the bicycle class." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Object detection performance drop evaluated on subsets of the nuScenes val set. Performance is reported as the relative drop in mAP compared to evaluating on the entire val set. We evaluate the performance on Singapore data, rain data and night data for three object detection methods. Note that the MDIS results are not directly comparable to other sections of this work, as a ResNet34 [39] backbone and a different training protocol are used. (†) use only monocular camera images as input. PP uses only lidar.'\nProblem: 'Which method has the highest performance in the Night condition?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/205.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Object detection performance drop evaluated on subsets of the nuScenes val set. Performance is reported as the relative drop in mAP compared to evaluating on the entire val set. We evaluate the performance on Singapore data, rain data and night data for three object detection methods. Note that the MDIS results are not directly comparable to other sections of this work, as a ResNet34 [39] backbone and a different training protocol are used. (†) use only monocular camera images as input. PP uses only lidar." ], "reasoning": "By comparing the Night column values for each method, MDIS has the highest performance with 58%.", "question": "Which method has the highest performance in the Night condition?", "answer": { "answer": "MDIS" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPointPillars has the lowest performance in the Singapore condition.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/205.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Object detection performance drop evaluated on subsets of the nuScenes val set. Performance is reported as the relative drop in mAP compared to evaluating on the entire val set. We evaluate the performance on Singapore data, rain data and night data for three object detection methods. Note that the MDIS results are not directly comparable to other sections of this work, as a ResNet34 [39] backbone and a different training protocol are used. (†) use only monocular camera images as input. PP uses only lidar." ], "reasoning": "The table shows that PointPillars (PP) has 1% in the Singapore condition, which is lower than OFT's 6% and MDIS's 8%.", "statement": "PointPillars has the lowest performance in the Singapore condition.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Example of available compute power on mobile in gigaflops (billion floating point instructions per second). FP16 and FP32 refer to 16- and 32-bit floating point arithmetic, respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/206.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Example of available compute power on mobile in gigaflops (billion floating point instructions per second). FP16 and FP32 refer to 16- and 32-bit floating point arithmetic, respectively." ], "reasoning": "The table shows that the GPU has higher compute power than the CPU across all listed devices.", "description": "Table 1 demonstrates that GPU has significantly more compute power than CPU." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Example of available compute power on mobile in gigaflops (billion floating point instructions per second). FP16 and FP32 refer to 16- and 32-bit floating point arithmetic, respectively.'\nProblem: 'What is the difference in compute power between the CPU and GPU for the Samsung Galaxy S7?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/206.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Example of available compute power on mobile in gigaflops (billion floating point instructions per second). FP16 and FP32 refer to 16- and 32-bit floating point arithmetic, respectively." ], "reasoning": "The compute power for the CPU in the Samsung Galaxy S7 is 124, and for the GPU it is 730. The difference is 730 - 124.", "question": "What is the difference in compute power between the CPU and GPU for the Samsung Galaxy S7?", "answer": "So, the answer is {\"answer\": \"606\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe GPU in the Samsung Galaxy S9 has the same compute power as the GPU in the Samsung Galaxy S7.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/206.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Example of available compute power on mobile in gigaflops (billion floating point instructions per second). FP16 and FP32 refer to 16- and 32-bit floating point arithmetic, respectively." ], "reasoning": "The table shows that the GPU compute power for both the Samsung Galaxy S9 and Samsung Galaxy S7 is 730.", "statement": "The GPU in the Samsung Galaxy S9 has the same compute power as the GPU in the Samsung Galaxy S7.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Optimal work group sizes for Adreno GPUs.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/207.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Optimal work group sizes for Adreno GPUs." ], "reasoning": "Work groups from Table 2 are used in TFLite GPU and are proven to be stable, providing top 10% performance regardless of convolution parameters.", "description": "Work groups from the Table 2 are currently used in TFLite GPU and their stability is statistically proven. While they do not necessarily result in peak optimal time across all parameters, they are reliable in giving top 10% performance regardless of the convolution parameters." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Optimal work group sizes for Adreno GPUs.'\nProblem: 'Which Adreno GPU model uses the work group (8,4,4) for both conv_2d and depthwise_conv?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/207.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Optimal work group sizes for Adreno GPUs." ], "reasoning": "By examining the table, we see that the Adreno GPU model 510 uses the work group (8,4,4) for both conv_2d and depthwise_conv.", "question": "Which Adreno GPU model uses the work group (8,4,4) for both conv_2d and depthwise_conv?", "answer": { "answer": "510" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Adreno GPU model 540 uses the work group (8,8,2) for depthwise_conv.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/207.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Optimal work group sizes for Adreno GPUs." ], "reasoning": "The table shows that the Adreno GPU model 540 indeed uses the work group (8,8,2) for depthwise_conv.", "statement": "The Adreno GPU model 540 uses the work group (8,8,2) for depthwise_conv.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Total memory allocated (in MB) for all intermediate tensors. Naïve means no memory manager and serves as baseline. Bold number means the smallest memory footprint for each model.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/208.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Total memory allocated (in MB) for all intermediate tensors. Naïve means no memory manager and serves as baseline. Bold number means the smallest memory footprint for each model." ], "reasoning": "The minimal memory footprint between the greedy and MCFP algorithms varies depending on the network, with TFLite GPU using the greedy algorithm by default.", "description": "There is no clear winner between these two memory management algorithms in terms of the minimal memory footprint, and it depends on the network (Table 3). TFLite GPU is using the greedy algorithm by default with the developer being able to choose the MCFP algorithm if desired." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Total memory allocated (in MB) for all intermediate tensors. Naïve means no memory manager and serves as baseline. Bold number means the smallest memory footprint for each model.'\nProblem: 'Which strategy has the lowest memory footprint for the MobileNetV2 network?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/208.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Total memory allocated (in MB) for all intermediate tensors. Naïve means no memory manager and serves as baseline. Bold number means the smallest memory footprint for each model." ], "reasoning": "To find the strategy with the lowest memory footprint for MobileNetV2, compare the values in the MobileNetV2 column. The Greedy strategy has a memory footprint of 4.0, and the MCFP strategy has a memory footprint of 3.8. Therefore, the MCFP strategy has the lowest memory footprint for MobileNetV2.", "question": "Which strategy has the lowest memory footprint for the MobileNetV2 network?", "answer": "So, the answer is {\"answer\": \"MCFP\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Greedy strategy has the lowest memory footprint for the MobileNet network.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/208.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Total memory allocated (in MB) for all intermediate tensors. Naïve means no memory manager and serves as baseline. Bold number means the smallest memory footprint for each model." ], "reasoning": "To verify this statement, compare the memory footprints for the MobileNet network. The Greedy strategy has a memory footprint of 2.3, which is lower than the Naïve strategy (9.6) and the MCFP strategy (2.7). Therefore, the statement is supported by the table.", "statement": "The Greedy strategy has the lowest memory footprint for the MobileNet network.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Average inference latency (in milliseconds) of Android-compatible ML frameworks on MobileNet v1. Note that TFLite GPU employs OpenGL and thus has the widest coverage with reasonable performance. MACE and SNPE employ OpenCL and may run faster on devices shipped with OpenCL, but may not run on all devices. 1 Arm Mali GPUs are not compatible with SNPE. 2 Google Pixel devices do not support OpenCL.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/209.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Average inference latency (in milliseconds) of Android-compatible ML frameworks on MobileNet v1. Note that TFLite GPU employs OpenGL and thus has the widest coverage with reasonable performance. MACE and SNPE employ OpenCL and may run faster on devices shipped with OpenCL, but may not run on all devices. 1 Arm Mali GPUs are not compatible with SNPE. 2 Google Pixel devices do not support OpenCL." ], "reasoning": "TFLite GPU uses OpenGL for broad compatibility, while MACE and SNPE use OpenCL, which may offer better performance but is not standard on all Android devices.", "description": "Android-compatible ML frameworks on MobileNet v1, respectively. Note that TFLite GPU employs OpenGL for the widest coverage with reasonable performance. MACE and SNPE employ OpenCL and may outperform TFLite GPU on some mobile devices shipped with OpenCL. As OpenCL is not a part of the standard Android distribution, apps using those frameworks may not be able to guarantee their inference performance e.g. on Google Pixel devices. Also note that SNPE does not run on devices with Arm Mali GPUs." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Average inference latency (in milliseconds) of Android-compatible ML frameworks on MobileNet v1. Note that TFLite GPU employs OpenGL and thus has the widest coverage with reasonable performance. MACE and SNPE employ OpenCL and may run faster on devices shipped with OpenCL, but may not run on all devices. 1 Arm Mali GPUs are not compatible with SNPE. 2 Google Pixel devices do not support OpenCL.'\nProblem: 'Which framework has the highest performance on the Huawei P20 Pro?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/209.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Average inference latency (in milliseconds) of Android-compatible ML frameworks on MobileNet v1. Note that TFLite GPU employs OpenGL and thus has the widest coverage with reasonable performance. MACE and SNPE employ OpenCL and may run faster on devices shipped with OpenCL, but may not run on all devices. 1 Arm Mali GPUs are not compatible with SNPE. 2 Google Pixel devices do not support OpenCL." ], "reasoning": "The table shows the performance of different frameworks on various devices. For the Huawei P20 Pro, MACE has a performance of 45, which is higher than TFLite GPU's 13.5 and SNPE's N/A.", "question": "Which framework has the highest performance on the Huawei P20 Pro?", "answer": "So, the answer is {\"answer\": \"MACE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSNPE runs on the Xiaomi Mi8 SE.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/209.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Average inference latency (in milliseconds) of Android-compatible ML frameworks on MobileNet v1. Note that TFLite GPU employs OpenGL and thus has the widest coverage with reasonable performance. MACE and SNPE employ OpenCL and may run faster on devices shipped with OpenCL, but may not run on all devices. 1 Arm Mali GPUs are not compatible with SNPE. 2 Google Pixel devices do not support OpenCL." ], "reasoning": "The table shows that SNPE has a performance value of 20 on the Xiaomi Mi8 SE, indicating that it runs on this device.", "statement": "SNPE runs on the Xiaomi Mi8 SE.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption '(a) Mean Accuracy'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/210.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Mean Accuracy" ], "reasoning": "VFDT was statistically better than other algorithms in terms of accuracy, but adding OLBoost to SVFDT-I and SVFDT-II improved their accuracy beyond VFDT.", "description": "We present the observed results in Tables Ia, Ib, and Ic.. Considering accuracy (Table Ia), the. VFDT was statistically better than the other algorithms more times than any. other algorithm. Moreover, we can also observe that by adding OLBoost to the SVFDT-I and SVFDT-II, both algorithms were able to surpass the accuracy achieved by even the best performing technique (VFDT)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: '(a) Mean Accuracy'\nProblem: 'Which algorithm had the highest number of wins against O_VFDT?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/210.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Mean Accuracy" ], "reasoning": "By examining the column for O_VFDT, we see that SVFDT-I had 9 wins, which is the highest number of wins against O_VFDT.", "question": "Which algorithm had the highest number of wins against O_VFDT?", "answer": "So, the answer is {\"answer\": \"SVFDT-I\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSVFDT-II had more wins against VFDT than O_SVFDT-I.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/210.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Mean Accuracy" ], "reasoning": "By comparing the number of wins against VFDT, SVFDT-II had 0 wins while O_SVFDT-I had 7 wins. Therefore, the statement is refuted.", "statement": "SVFDT-II had more wins against VFDT than O_SVFDT-I.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Equal Error Rate and AR with and without the mitigation strategy. Green (resp., red) shades highlight improvement (resp., deterioration) in FPR and AR. Color intensity is proportional to degree of performance change.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/211.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Equal Error Rate and AR with and without the mitigation strategy. Green (resp., red) shades highlight improvement (resp., deterioration) in FPR and AR. Color intensity is proportional to degree of performance change." ], "reasoning": "Adding beta noise significantly decreases AR in all configurations, with AR ≤ 0.04 in 14 out of 16 cases, and minimal impact on FPR, which remains unchanged or improves in 13 out of 16 instances.", "description": "In Table I, we show the resulting FPR and AR after the addition of beta noise at the equal error rate.. In every configuration (classifier-dataset pairs), we see a significant decrease in AR. The AR is now lower than FPR in every configuration. In 14 out of 16 cases, the AR is ≤ 0.04. The two exceptions are LinSVM (with face and voice datasets). We further see that in 13 out of 16 instances the FPR either remains unchanged or improves! The 3 instances where the FPR degrades are LinSVM with face and face datasets both by +0.015, and DNN with Touch where the difference is +0.05.. Thus, adding beta distributed noise does indeed decrease the AR with minimal impact on FPR.. The resulting FPR is marginally higher than the FPR from only beta-distributed noise in some cases (Table I)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Equal Error Rate and AR with and without the mitigation strategy. Green (resp., red) shades highlight improvement (resp., deterioration) in FPR and AR. Color intensity is proportional to degree of performance change.'\nProblem: 'Which classifier-dataset pair has the highest AR after mitigation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/211.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Equal Error Rate and AR with and without the mitigation strategy. Green (resp., red) shades highlight improvement (resp., deterioration) in FPR and AR. Color intensity is proportional to degree of performance change." ], "reasoning": "To find the highest AR after mitigation, we need to compare the AR values in the 'Mitigation' columns for all classifier-dataset pairs. The highest AR value is 0.11 for the Linear SVM with the Face dataset.", "question": "Which classifier-dataset pair has the highest AR after mitigation?", "answer": "So, the answer is {\"answer\": \"Linear SVM with Face dataset\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe AR for the Deep Neural Network with the Touch dataset is 0.00 after mitigation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/211.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Equal Error Rate and AR with and without the mitigation strategy. Green (resp., red) shades highlight improvement (resp., deterioration) in FPR and AR. Color intensity is proportional to degree of performance change." ], "reasoning": "The table shows that the AR for the Deep Neural Network with the Touch dataset is 0.00 after mitigation.", "statement": "The AR for the Deep Neural Network with the Touch dataset is 0.00 after mitigation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Equal Error Rate and RAR with and without the mitigation strategy. The AR values remain the same as in Table I. β-RAR indicates RAR treated with only β noise. RAR indicates the inclusion of both β noise and raw random input samples.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/212.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Equal Error Rate and RAR with and without the mitigation strategy. The AR values remain the same as in Table I. β-RAR indicates RAR treated with only β noise. RAR indicates the inclusion of both β noise and raw random input samples." ], "reasoning": "The inclusion of beta-distributed noise in the training data marginally reduces the raw acceptance rate (RAR) while maintaining minimal impact on false positive rate (FPR) and false rejection rate (FRR).", "description": "Interestingly, beta distributed noise only marginally reduces the raw acceptance rate as can be seen in Table II (columns labeled β-RAR).. Table II shows that the mitigation strategy reduces the RAR to less than or equal to 0.03 in all instances (columns labeled RAR).. Thus, the inclusion of betadistributed noise in conjunction with subset of raw inputs in the training data reduces both AR and RAR with minimal impact on FPR and FRR." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Equal Error Rate and RAR with and without the mitigation strategy. The AR values remain the same as in Table I. β-RAR indicates RAR treated with only β noise. RAR indicates the inclusion of both β noise and raw random input samples.'\nProblem: 'What is the RAR for the Touch Raw modality using Radial SVM with mitigation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/212.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Equal Error Rate and RAR with and without the mitigation strategy. The AR values remain the same as in Table I. β-RAR indicates RAR treated with only β noise. RAR indicates the inclusion of both β noise and raw random input samples." ], "reasoning": "To find the RAR for the Touch Raw modality using Radial SVM with mitigation, locate the row for Touch Raw and the column for Radial SVM Mitigation under RAR. The value is 0.01.", "question": "What is the RAR for the Touch Raw modality using Radial SVM with mitigation?", "answer": "So, the answer is {\"answer\": \"0.01\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe mitigation strategy reduces the RAR to less than or equal to 0.03 in all instances.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/212.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Equal Error Rate and RAR with and without the mitigation strategy. The AR values remain the same as in Table I. β-RAR indicates RAR treated with only β noise. RAR indicates the inclusion of both β noise and raw random input samples." ], "reasoning": "By examining the RAR values in the table for all modalities and models with mitigation, we see that all RAR values are indeed less than or equal to 0.03.", "statement": "The mitigation strategy reduces the RAR to less than or equal to 0.03 in all instances.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Performance analysis on rank-1 recognition accuracy. The highest accuracy is written in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/213.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance analysis on rank-1 recognition accuracy. The highest accuracy is written in bold." ], "reasoning": "The proposed network achieved the highest rank-1 recognition accuracy of 85.0±1.9%, outperforming both single-stream CNN and dual-stream CNN without shared weights.", "description": "Table 2 presents the performance analysis on the capability of a single-stream CNN with respective features, followed by dual-stream CNN without sharing the weights in conv and fc layers, and our proposed network. Note that, the dual-stream CNN without sharing. the weights also implemented the late-fusion and score fusion.. Table 2 shows that our proposed network achieved the highest rank-1 recognition accuracy with 85.0±1.9%. However, single-stream CNN only achieved 80.8±1.4% and 66.6±2.2% with RGB data and OC-LBCP, respectively. As compared to single-stream CNN, the results indicate that the late-fusion layers are significant to correlate the RGB data and OC-LBCP in order to achieve better recognition performance.. Compared with dual-stream CNN without shared weights, the experimental results in Table 2 show that our proposed network is well-performed than dual-stream CNN without shared weights at least 2.9% improvement. This is because our proposed network utilised the shared conv and the fusion fc layers to aggregate the RGB data and OCLBCP." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Performance analysis on rank-1 recognition accuracy. The highest accuracy is written in bold.'\nProblem: 'What is the accuracy improvement of the proposed network over the dual-stream CNN without shared weights?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/213.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance analysis on rank-1 recognition accuracy. The highest accuracy is written in bold." ], "reasoning": "The proposed network achieved an accuracy of 85.0±1.9%, while the dual-stream CNN without shared weights achieved 82.1±1.6%. The improvement is calculated as 85.0 - 82.1.", "question": "What is the accuracy improvement of the proposed network over the dual-stream CNN without shared weights?", "answer": "So, the answer is {\"answer\": \"2.9%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed network achieved a higher accuracy than the single-stream CNN using RGB data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/213.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance analysis on rank-1 recognition accuracy. The highest accuracy is written in bold." ], "reasoning": "The proposed network achieved an accuracy of 85.0±1.9%, while the single-stream CNN using RGB data achieved 80.8±1.4%. Therefore, the statement is supported.", "statement": "The proposed network achieved a higher accuracy than the single-stream CNN using RGB data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Evaluation of recognition performance on the Ethnic-ocular dataset and UBIPr dataset. The highest accuracy is written in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/214.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation of recognition performance on the Ethnic-ocular dataset and UBIPr dataset. The highest accuracy is written in bold." ], "reasoning": "Dual-stream CNN achieves the highest average rank-1 and rank-5 recognition accuracies, followed by multi-abstract fusion CNN, while the proposed network shows competitive performance.", "description": "Table 3 presents that dual-stream CNN achieves the highest average rank-1 and rank-5 recognition accuracies with 91.28±1.18% and 98.59±0.44%, respectively. The second best is achieved by multi-abstract fusion CNN with 90.75±1.01% and 97.44±0.34% as rank-1 and rank-5 accuracies.. We presented the experimental results in Table 3 by following the recognition protocol as mentioned in Section 4.2. To evaluate the performance of. the proposed network, we compared our results with seven benchmark approaches (see Table 3). For the results of recognition, our proposed network achieved 85.03±1.88% and 94.23±1.26% as rank-1 and rank-5 accuracies, respectively.. Besides, the results proved that our network can learn new features from the late-fusion layers for better recognition." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Evaluation of recognition performance on the Ethnic-ocular dataset and UBIPr dataset. The highest accuracy is written in bold.'\nProblem: 'Which approach achieved the highest rank-1 accuracy for the UBIPr dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/214.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation of recognition performance on the Ethnic-ocular dataset and UBIPr dataset. The highest accuracy is written in bold." ], "reasoning": "By comparing the rank-1 accuracies for the UBIPr dataset in the table, Proposed network has the highest rank-1 accuracy with 91.28±1.18%.", "question": "Which approach achieved the highest rank-1 accuracy for the UBIPr dataset?", "answer": "So, the answer is {\"answer\": \"Proposed network\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed network achieved the highest rank-5 accuracy for the UBIPR dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/214.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation of recognition performance on the Ethnic-ocular dataset and UBIPr dataset. The highest accuracy is written in bold." ], "reasoning": "The table shows that the proposed network achieved a rank-5 accuracy of 98.59±0.44% for the UBIPR dataset, which is the highest.", "statement": "The proposed network achieved the highest rank-5 accuracy for the UBIPR dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Perplexity results over the PTB. Symbols: WT = weight tying Press and Wolf (2016); BD = Bayesian Dropout Gal and Ghahramani (2015). Please note that we could not calculate the number of parameters for some models given missing information in the original publications.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/215.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Perplexity results over the PTB. Symbols: WT = weight tying Press and Wolf (2016); BD = Bayesian Dropout Gal and Ghahramani (2015). Please note that we could not calculate the number of parameters for some models given missing information in the original publications." ], "reasoning": "The Averaging RNN-LM achieves the lowest perplexity for a single model on the PTB dataset and is competitive compared to ensemble methods despite having significantly fewer parameters.", "description": "Table 1 presents the results in terms of perplexity of the models trained over the PTB dataset.. The Averaging RNN-LM achieves the lowest perplexity for a single model on the PTB. Given the similarity of the results between the Attentive RNN-LMs. and the Averaging RNN-LM it would appear that our hypothesis that the Attentive RNN-LMs was (indirectly) learning to use the dynamics of the LSTM gating mechanism is correct.. The Averaging RNN-LM is also very competitive on the PTB compared to the ensemble methods and in this case the difference in total number of parameters between the Averaging RNN-LM and these ensemble models is significant." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Perplexity results over the PTB. Symbols: WT = weight tying Press and Wolf (2016); BD = Bayesian Dropout Gal and Ghahramani (2015). Please note that we could not calculate the number of parameters for some models given missing information in the original publications.'\nProblem: 'Which single model achieves the lowest perplexity on the PTB test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/215.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Perplexity results over the PTB. Symbols: WT = weight tying Press and Wolf (2016); BD = Bayesian Dropout Gal and Ghahramani (2015). Please note that we could not calculate the number of parameters for some models given missing information in the original publications." ], "reasoning": "The table shows that the Averaging RNN-LM has the lowest perplexity of 69.9 among single models on the PTB test set.", "question": "Which single model achieves the lowest perplexity on the PTB test set?", "answer": "So, the answer is {\"answer\": \"Averaging RNN-LM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Averaging LSTM-LM has fewer parameters than the Attentive LM w/ combined score function.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/215.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Perplexity results over the PTB. Symbols: WT = weight tying Press and Wolf (2016); BD = Bayesian Dropout Gal and Ghahramani (2015). Please note that we could not calculate the number of parameters for some models given missing information in the original publications." ], "reasoning": "The table shows that the Averaging RNN-LM has 14.1M parameters, while the Attentive LM w/ combined score function has 14.5M parameters.", "statement": "The Averaging RNN-LM has fewer parameters than the Attentive LM w/ combined score function.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Perplexity results over the wikitext2. Please note that we could not calculate the number of parameters for some models given missing information in the original publications.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/216.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Perplexity results over the wikitext2. Please note that we could not calculate the number of parameters for some models given missing information in the original publications." ], "reasoning": "The Neural cache model is the best performing model on the wikitext2 dataset, with the lowest perplexity score, while the Averaging LSTM-LM shows competitive performance despite resetting its memory at each sentence boundary.", "description": "Table 2 presents the results in terms of perplexity of the models trained over the wikitext2 dataset.. Focusing on the results for the wikitext2 dataset (see Table 2) the Neural cache model is still the best performing model on this dataset.. In discussing the wikietext2 results it is worth noting that the Attentive RNN-LMs and the Averaging LSTM-LM are the only models in Table 2 that reset their memory at each sentence boundary whereas the memory buffers of other models were allowed to span sentence boundaries.. Given this difference the competitive performance of the Averaging LSTM-LM is encouraging." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Perplexity results over the wikitext2. Please note that we could not calculate the number of parameters for some models given missing information in the original publications.'\nProblem: 'Which model has the lowest perplexity score on the wikitext2 test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/216.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Perplexity results over the wikitext2. Please note that we could not calculate the number of parameters for some models given missing information in the original publications." ], "reasoning": "By examining the table, the Neural cache model (size = 2000) has the lowest perplexity score of 68.9 on the test set.", "question": "Which model has the lowest perplexity score on the wikitext2 test set?", "answer": "So, the answer is {\"answer\": \"Neural cache model (size = 2000)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Averaging LSTM-LM has a lower perplexity score than the Attentive LM w/ single score function on the wikitext2 test set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/216.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Perplexity results over the wikitext2. Please note that we could not calculate the number of parameters for some models given missing information in the original publications." ], "reasoning": "The table shows that the Averaging LSTM-LM has a perplexity score of 71.3, which is higher than the Attentive LM w/ single score function's score of 69.7.", "statement": "The Averaging LSTM-LM has a lower perplexity score than the Attentive LM w/ single score function on the wikitext2 test set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Accuracy of ensemble of 50 PPD models on ℓ∞ and ℓ2 attacks for MNIST and CIFAR-10. The lowest accuracy for each perturbation strength is in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/217.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Accuracy of ensemble of 50 PPD models on ℓ∞ and ℓ2 attacks for MNIST and CIFAR-10. The lowest accuracy for each perturbation strength is in bold." ], "reasoning": "PPD maintains significant resistance to (cid:96)∞ and (cid:96)2 perturbations, achieving 67.5% accuracy on MNIST and 45.1% on CIFAR-10 under higher attack budgets.", "description": "achieving 88.8% accuracy on (cid:96)∞ attack of strength 0.3 for MNIST 4 and 44.7% on (cid:96)∞ attack of strength 0.03 on CIFAR10 5. With the same attack budget, our worst accuracy on MNIST and CIFAR-10 are 87.4% and 47.6%, respectively (Table I).. Increasing (cid:96)∞ attack budget to 0.4 for MNIST and 0.1 for CIFAR-10 brings Madry's accuracy down to 0% and 10%, respectively (Figure 6 of ), while PPD still gives accuracy of 67.5% and 45.1%, respectively.. This is while PPD retains significant resistance to (cid:96)2 perturbations (Table I)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Accuracy of ensemble of 50 PPD models on ℓ∞ and ℓ2 attacks for MNIST and CIFAR-10. The lowest accuracy for each perturbation strength is in bold.'\nProblem: 'What is the accuracy of PPD on CIFAR-10 with an (cid:96)∞ attack budget of 0.1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/217.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Accuracy of ensemble of 50 PPD models on ℓ∞ and ℓ2 attacks for MNIST and CIFAR-10. The lowest accuracy for each perturbation strength is in bold." ], "reasoning": "To find the accuracy of PPD on CIFAR-10 with an (cid:96)∞ attack budget of 0.1, look at the table under the column labeled 'CIFAR-10 0.1' and the row labeled 'MIM'. The value is 45.1%.", "question": "What is the accuracy of PPD on CIFAR-10 with an (cid:96)∞ attack budget of 0.1?", "answer": { "answer": "45.1%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPPD achieves 67.5% accuracy on MNIST with an (cid:96)∞ attack budget of 0.4.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/217.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Accuracy of ensemble of 50 PPD models on ℓ∞ and ℓ2 attacks for MNIST and CIFAR-10. The lowest accuracy for each perturbation strength is in bold." ], "reasoning": "The table shows that under the column 'MNIST 0.4' and the row 'MIM', the accuracy is 67.5%, which supports the statement.", "statement": "PPD achieves 67.5% accuracy on MNIST with an (cid:96)∞ attack budget of 0.4.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Mean AUC results of cross-validation procedure.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/218.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Mean AUC results of cross-validation procedure." ], "reasoning": "The self-attentive model (LSTM+SA) consistently outperforms the standard LSTM in terms of AUC across different time gaps, demonstrating its superior ability to handle longer inputs.", "description": "As baseline, we compared with a LSTM network without self-attention, but with the same pre-trained input embeddings. Table 3 report the average area under ROC curve (AUC) over folds with the self-attentive model (LSTM+SA) and with standard LSTM.. Comparing self-attentive model with standard LSTM we can see a better performance. This is due to the ability to deal with longer inputs with the self-attention (SA) mechanism. Figure 1 shows a part of neural network attention mapping scores for a particular individual. SA allowing us to see which parts of the input are attended to predict diabetes disease evolution." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Mean AUC results of cross-validation procedure.'\nProblem: 'What is the difference in AUC between the self-attentive model and the standard LSTM for a time gap of 60?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/218.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Mean AUC results of cross-validation procedure." ], "reasoning": "The AUC for the self-attentive model (LSTM+SA) at a time gap of 60 is 0.83, while the AUC for the standard LSTM is 0.72. The difference is 0.83 - 0.72.", "question": "What is the difference in AUC between the self-attentive model and the standard LSTM for a time gap of 60?", "answer": "So, the answer is {\"answer\": \"0.11\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe self-attentive model has a higher AUC than the standard LSTM for all time gaps.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/218.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Mean AUC results of cross-validation procedure." ], "reasoning": "The table shows that the AUC for the self-attentive model (LSTM+SA) is higher than the AUC for the standard LSTM for all listed time gaps (60, 120, 180, and 240).", "statement": "The self-attentive model has a higher AUC than the standard LSTM for all time gaps.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Architecture of FastMobileNet (upsample factor for all FMNet blocks is set to k=6)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/219.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Architecture of FastMobileNet (upsample factor for all FMNet blocks is set to k=6)" ], "reasoning": "The FMNet architecture is based on MNv2-0.5, with halved channel sizes in all layers, and includes multiple FMNet blocks with varying output sizes and strides.", "description": "the FMNet architecture corresponding to the CNN part is shown in Table I, where the layer sizes and block repeats of the model are based on MNv2-0.5 (i.e., MNv2 with halved channel sizes in all layers)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Architecture of FastMobileNet (upsample factor for all FMNet blocks is set to k=6)'\nProblem: 'What is the output size after the FMNet block 3 layer?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/219.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Architecture of FastMobileNet (upsample factor for all FMNet blocks is set to k=6)" ], "reasoning": "The table shows that the output size after FMNet block 3 is 19×19×32.", "question": "What is the output size after the FMNet block 3 layer?", "answer": { "answer": "19×19×32" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Conv 3×3 layer has an output size of 150×150×24.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/219.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Architecture of FastMobileNet (upsample factor for all FMNet blocks is set to k=6)" ], "reasoning": "The table indicates that the Conv 3×3 layer indeed has an output size of 150×150×24.", "statement": "The Conv 3×3 layer has an output size of 150×150×24.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Comparison of various CNN architectures (all models except the last one use the concatenation feature fusion)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/220.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Comparison of various CNN architectures (all models except the last one use the concatenation feature fusion)" ], "reasoning": "FMNet with spatial feature fusion achieves the best accuracy and lowest latency among the compared CNN architectures, while also having the lowest FLOPS, number of parameters, and MAC.", "description": "First, we compared the prediction accuracy and inference latency on several base CNN architectures. We found that the proposed FMNet gives similar prediction accuracy as other modern architectures such as ResNet, MNv2, and MnasNet, while being much faster during inference. In terms of the number of FLOPS and parameters, FMNet is similar to MnasNet-0.5 and MNv2-0.5 (which it is based on), while AlexNet and ResNet18 have many more FLOPS and parameters. Fast inference of FMNet can be explained by low MAC and operation counts, which we specifically optimized for during the model design phase. It is interesting to note that AlexNet is the second fastest CNN while having the second largest FLOPS. This is possibly due to it having the smallest number of layers, as evidenced by its lowest operation counts, although its accuracy is not on par with the competing networks.. In the first set of experiments we compared a number of CNN architectures, summarizing results in Table II. To ensure fair comparison and avoid potential issues with small data sets, we trained all models on vehicle actors where we set the prediction horizon to 6s. Average prediction error and latency are reported in the table. We skip the feature fusion layers when computing the number of parameters, as the concatenation feature fusion adds a large amount of parameters which complicates the comparison (an 1024 × 4096 fully-connected layer for feature fusion adds 4M extra parameters). MAC is approximated by the sum of tensor sizes of all graph nodes. Column \"Num. ops\" refers to the total number of operations in the TensorFlow graph of each model. The inference latency is measured at a batch of 32 actors on a GTX 1080Ti GPU.. For simplicity and to facilitate fair comparison, we implemented all models using TensorFlow built-in operations without additional optimization.. Secondly, we found that FMNet with spatial feature fusion further improves the accuracy and inference time when compared to the model with feature fusion through concatenation.. This resulted in lower FLOPS, as well as lower number of parameters and MAC. Following these results we use the best performing FMNet with spatial fusion as the model architecture in the following ablation studies on rasterization choices." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Comparison of various CNN architectures (all models except the last one use the concatenation feature fusion)'\nProblem: 'Which CNN architecture has the lowest number of operations and what is its latency?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/220.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Comparison of various CNN architectures (all models except the last one use the concatenation feature fusion)" ], "reasoning": "From the table, AlexNet has the lowest number of operations (131). The latency for AlexNet is 15.8 ms.", "question": "Which CNN architecture has the lowest number of operations and what is its latency?", "answer": "So, the answer is {\"answer\": \"15.8 ms\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFMNet with spatial feature fusion has the lowest MAC among all the architectures.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/220.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Comparison of various CNN architectures (all models except the last one use the concatenation feature fusion)" ], "reasoning": "The table shows that FMNet with spatial feature fusion has a MAC of 47 MB, which is the lowest among all the listed architectures.", "statement": "FMNet with spatial feature fusion has the lowest MAC among all the architectures.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Comparison of prediction displacement errors (in meters) for different experimental settings'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/221.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Comparison of prediction displacement errors (in meters) for different experimental settings" ], "reasoning": "The ablation study shows that RasterNet with 0.1m resolution has the lowest error for pedestrians, while 0.3m resolution performs the worst. Removing traffic light rasterization and lane heading information increases error for both actor types.", "description": "We conducted ablation study regarding the rasterization setup, modifying various parameters of the rasterization configuration of the base setup. Empirical results in terms of average prediction error, as well as short- and long-term errors are given in Table III. We can see that the all variants of the proposed CNN approach (referred to as RasterNet) significantly outperform UKF, especially at longer horizons.. As can be seen, for pedestrians 0.1m-resolution indeed resulted in lower error, while setting 0.3m gave the worst performance. We observed that resolution of 0.1m showed no significant difference for bicyclists, while 0.3m resulted in slightly higher error.. Next, we evaluated the impact of not rotating raster such that actor heading points up, as discussed in Section III-B,. which resulted in a significant drop of accuracy for both actor types.. We observed error increase without traffic light rasterization for both actor types, matching this intuition.. Furthermore, we removed lane heading information provided in raster images, encoded by using different colors to indicate different directions. Without lane heading bicyclist model degraded slightly in performance, while pedestrian model was unaffected.. Finally, we tried learning raster colors instead of setting them manually. The results indicate that learned colors slightly improved accuracy for bicyclists, whereas the pedestrian model slightly degraded compared to the baseline.. Lastly, due to significantly larger amount of vehicle data as compared to VRUs, instead of training from scratch we finetuned VRU models using preloaded weights from a pretrained vehicle model. As observed in the second-to-last row, directly applying the vehicle model to bicyclists and pedestrians without fine-tuning results in poor performance across the board, even worse than UKF, due to a different nature of these actor types as compared to vehicles. On the other hand, with additional fine-tuning using training data of corresponding actor types the bicyclist performance improved over the baseline, indicating that bicyclists may exhibit similar behavior to vehicles. We can also see that the pedestrian model regressed, explained by the fact that pedestrian motion is very different from vehicle motion, thus making vehicle pretraining ineffective." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Comparison of prediction displacement errors (in meters) for different experimental settings'\nProblem: 'Which RasterNet configuration resulted in the lowest average prediction error for pedestrians?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/221.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Comparison of prediction displacement errors (in meters) for different experimental settings" ], "reasoning": "By examining the table, the RasterNet configuration with 0.1m resolution has the lowest average prediction error for pedestrians, which is 0.51.", "question": "Which RasterNet configuration resulted in the lowest average prediction error for pedestrians?", "answer": "So, the answer is {\"answer\": \"RasterNet 0.1m\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRemoving lane heading information from the raster images did not affect the pedestrian model's performance.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/221.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Comparison of prediction displacement errors (in meters) for different experimental settings" ], "reasoning": "The description states that removing lane heading information slightly degraded the bicyclist model's performance while the pedestrian model was unaffected. This matches the table data where the pedestrian average error remains 0.52 with or without lane headings.", "statement": "Removing lane heading information from the raster images did not affect the pedestrian model's performance.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: FID score comparison for generated and reconstructed images of the presented Wavelet-VAE and Wavelet-VAE-MR models against standard VAE trained with MSE loss and VAE trained with cross-entropy loss for the likelihood term. For each dataset, we performed 5 independent trials and report the mean and standard deviation of the FID scores.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/222.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: FID score comparison for generated and reconstructed images of the presented Wavelet-VAE and Wavelet-VAE-MR models against standard VAE trained with MSE loss and VAE trained with cross-entropy loss for the likelihood term. For each dataset, we performed 5 independent trials and report the mean and standard deviation of the FID scores." ], "reasoning": "The Wavelet-VAE consistently outperforms the baseline VAE models across all three natural image datasets, with the multi-resolution version achieving the best performance in two out of three datasets.", "description": "In Table 1, we present the FID score for the presented Wavelet-VAE compared against the two standard VAE models for both generated and reconstructed image samples.. For all three natural image datasets, the Wavelet-VAE is consistently better than both the baseline VAE models. Among the two out of three datasets, we achieved better performance using the multi-resolution version of the proposed Wavelet-VAE." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: FID score comparison for generated and reconstructed images of the presented Wavelet-VAE and Wavelet-VAE-MR models against standard VAE trained with MSE loss and VAE trained with cross-entropy loss for the likelihood term. For each dataset, we performed 5 independent trials and report the mean and standard deviation of the FID scores.'\nProblem: 'Which model achieved the best FID score for reconstructed images on the CelebA dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/222.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: FID score comparison for generated and reconstructed images of the presented Wavelet-VAE and Wavelet-VAE-MR models against standard VAE trained with MSE loss and VAE trained with cross-entropy loss for the likelihood term. For each dataset, we performed 5 independent trials and report the mean and standard deviation of the FID scores." ], "reasoning": "To determine the best FID score for reconstructed images on the CelebA dataset, compare the values in the 'Reconstructed Images CelebA' column. The lowest FID score indicates the best performance.", "question": "Which model achieved the best FID score for reconstructed images on the CelebA dataset?", "answer": "So, the answer is {\"answer\": \"Wavelet-VAE-MR\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe VAE-c model achieved the best FID score for generated images on the CIFAR-10 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/222.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: FID score comparison for generated and reconstructed images of the presented Wavelet-VAE and Wavelet-VAE-MR models against standard VAE trained with MSE loss and VAE trained with cross-entropy loss for the likelihood term. For each dataset, we performed 5 independent trials and report the mean and standard deviation of the FID scores." ], "reasoning": "To verify this statement, compare the FID scores for generated images on the CIFAR-10 dataset across all models. The lowest FID score indicates the best performance.", "statement": "The VAE-c model achieved the best FID score for generated images on the CIFAR-10 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Sample Browser history dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/223.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Sample Browser history dataset" ], "reasoning": "The table shows the URLs visited, along with the first and last visit times, URL counts, and frecency scores.", "description": "Thus after preprocessing, sample dataset looks like Table I:" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Sample Browser history dataset'\nProblem: 'Which URL has the highest frecency score and how many times was it visited?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/223.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Sample Browser history dataset" ], "reasoning": "To find the URL with the highest frecency score, look at the 'Frecency' column and identify the highest value, then check the corresponding 'URL Counts' for that row.", "question": "How many visit times for the URL has the highest frecency score ?", "answer": "So, the answer is {\"answer\": \"177\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe URL with the highest number of visits is https://web.facebook.com/.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/223.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Sample Browser history dataset" ], "reasoning": "To verify this statement, compare the 'URL Counts' for all rows and identify the highest value, then check if it corresponds to https://web.facebook.com/.", "statement": "The URL with the highest number of visits is https://web.facebook.com/.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Experimental results of frecency prediction.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/224.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Experimental results of frecency prediction." ], "reasoning": "The experimental results show a Mean Square Error of 586430.6574, a Root Mean Square Error of 765.7876, and a Score of 87.62%.", "description": "Experimental results of frecency prediction are shown in Table III." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Experimental results of frecency prediction.'\nProblem: 'What is the relationship between the Mean Square Error and the Score in the experimental results?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/224.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Experimental results of frecency prediction." ], "reasoning": "The table shows that a Mean Square Error of 586430.6574.", "question": "What is the Mean Square Error and the Score?", "answer": "So, the answer is {\"answer\": \"586430.6574\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Root Mean Square Error is less than 800.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/224.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Experimental results of frecency prediction." ], "reasoning": "The table shows that the Root Mean Square Error is 765.7876, which is indeed less than 800.", "statement": "The Root Mean Square Error is less than 800.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Predicted Frecency.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/225.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Predicted Frecency." ], "reasoning": "The search term 'loc' will predict URLs listed in Table IV, which includes URLs with 'localhost'.", "description": "The link prediction for the search term \"loc\" will predict Table IV URLs." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Predicted Frecency.'\nProblem: 'Which URL has the highest frequency and how many times does it appear?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/225.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Predicted Frecency." ], "reasoning": "To find the URL with the highest frequency, look at the 'Frecency' column and identify the highest value. Then, check the corresponding 'URL Counts' for that URL.", "question": "How many times does the URL with highest frequency appear?", "answer": "So, the answer is {\"answer\": \"16\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe URL 'http://localhost:8888/tree' has a frequency higher than 2500.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/225.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Predicted Frecency." ], "reasoning": "Check the 'Frecency' column for the URL 'http://localhost:8888/tree' and see if its value is greater than 2500.", "statement": "The URL 'http://localhost:8888/tree' has a frequency higher than 2500.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: Best Parameters'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/226.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Best Parameters" ], "reasoning": "The best parameters for the model were found through random search over 8 iterations, with clf-alpha representing the learning rate.", "description": "Table V and all parameters list is shown in Table VI. The best parameters are found by random search analyzing 8 iterations. Here clf-alpha is a learning rate. Table VII shows the Precision, Recall, F1 score and" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: Best Parameters'\nProblem: 'What is the mean value obtained from the best parameters found by random search?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/226.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Best Parameters" ], "reasoning": "The table shows the mean value as one of the parameters obtained from the random search.", "question": "What is the mean value obtained from the best parameters found by random search?", "answer": "So, the answer is {\"answer\": \"0.69971\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe standard deviation of the best parameters found by random search is 0.00035.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/226.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Best Parameters" ], "reasoning": "The table lists the standard deviation as one of the parameters obtained from the random search.", "statement": "The standard deviation of the best parameters found by random search is 0.00035.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VI: All Parameters list by random search'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/227.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: All Parameters list by random search" ], "reasoning": "The best parameters for the model were found by random search analyzing 8 iterations, with clf-alpha representing the learning rate.", "description": "Table V and all parameters list is shown in Table VI. The best parameters are found by random search analyzing 8 iterations. Here clf-alpha is a learning rate. Table VII shows the Precision, Recall, F1 score and" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VI: All Parameters list by random search'\nProblem: 'What is the mean score for the model with the lowest standard deviation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/227.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: All Parameters list by random search" ], "reasoning": "To find the mean score for the model with the lowest standard deviation, identify the row with the smallest value in the 'standard-deviation' column and then look at the corresponding 'mean' value.", "question": "What is the mean score for the model with the lowest standard deviation?", "answer": "So, the answer is {\"answer\": \"0.69153\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model with the highest mean score used a clf-alpha of 0.01.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/227.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: All Parameters list by random search" ], "reasoning": "To verify this statement, find the highest value in the 'mean' column and check the corresponding 'clf-alpha' value.", "statement": "The model with the highest mean score used a clf-alpha of 0.01.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VII: Classification Accuracy Without Random Search (Previous Method)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/228.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: Classification Accuracy Without Random Search (Previous Method)" ], "reasoning": "The best parameters were found by random search analyzing 8 iterations, and the performance metrics for different classes are shown in Table VII.", "description": "Table V and all parameters list is shown in Table VI. The best parameters are found by random search analyzing 8 iterations. Here clf-alpha is a learning rate. Table VII shows the Precision, Recall, F1 score and" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VII: Classification Accuracy Without Random Search (Previous Method)'\nProblem: 'Which class has the highest F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/228.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: Classification Accuracy Without Random Search (Previous Method)" ], "reasoning": "To determine the class with the highest F1 score, compare the F1 scores of all classes in the table. Class 2 has the highest F1 score of 83.22%.", "question": "Which class has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nClass 4 has a higher Precision than Class 15.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/228.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: Classification Accuracy Without Random Search (Previous Method)" ], "reasoning": "To verify this statement, compare the Precision values of Class 4 and Class 15. Class 4 has a Precision of 73.45%, which is higher than Class 15's Precision of 61.66%.", "statement": "Class 4 has a higher Precision than Class 15.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VIII: Classification Accuracy With Random Search (Proposed Method)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/229.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VIII: Classification Accuracy With Random Search (Proposed Method)" ], "reasoning": "Our approach outperforms previous methods in terms of Precision, Recall, F1 score, and Accuracy across all classes.", "description": "Comparing Table VII and Table VIII, it is clear that our approach gives a better result than previous." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VIII: Classification Accuracy With Random Search (Proposed Method)'\nProblem: 'Which class has the highest F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/229.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VIII: Classification Accuracy With Random Search (Proposed Method)" ], "reasoning": "To determine the class with the highest F1 score, compare the F1 scores of all classes. Class 2 has the highest F1 score of 84.20%.", "question": "Which class has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nClass 4 has a higher Precision than Class 15.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/229.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VIII: Classification Accuracy With Random Search (Proposed Method)" ], "reasoning": "By comparing the Precision values, Class 4 has a Precision of 73.74%, which is higher than Class 15's Precision of 56.34%.", "statement": "Class 4 has a higher Precision than Class 15.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IX: Predicted sample result for frecency and category'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/230.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IX: Predicted sample result for frecency and category" ], "reasoning": "The table lists various URLs along with their counts, frequency, and categories, showing the total visits for each category.", "description": "Table IX and the total visit of individual category." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IX: Predicted sample result for frecency and category'\nProblem: 'What is the total frequency of visits for the 'Computers' category?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/230.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IX: Predicted sample result for frecency and category" ], "reasoning": "To find the total frequency of visits for the 'Computers' category, sum the frequency values for all URLs in the 'Computers' category: 102108.26 + 4873.655 + 528.395.", "question": "What is the total frequency of visits for the 'Computers' category?", "answer": { "answer": "107510.31" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe URL with the highest frequency belongs to the 'Computers' category.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/230.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IX: Predicted sample result for frecency and category" ], "reasoning": "The URL with the highest frequency is 'https://web.facebook.com/' with a frequency of 102108.26, which belongs to the 'Computers' category.", "statement": "The URL with the highest frequency belongs to the 'Computers' category.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Classification accuracy (mean ± std %) values of target domain over five independent runs on the digits datasets. The best performance is indicated in bold and the second best one is underlined.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/231.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Classification accuracy (mean ± std %) values of target domain over five independent runs on the digits datasets. The best performance is indicated in bold and the second best one is underlined." ], "reasoning": "Our approach, DM-ADA, achieves state-of-the-art performance on all four settings, outperforming former GAN-based approaches.", "description": "Table 1 presents the results of our approach in. comparison with other adaptation approaches on the digits datasets.. Our approach achieves the stateof-the-art performance on all four settings. Especially, it outperforms former GAN-based approaches (Bousmalis et al. 2017; Ghifary et al. 2016; Sankaranarayanan et al. 2018)," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Classification accuracy (mean ± std %) values of target domain over five independent runs on the digits datasets. The best performance is indicated in bold and the second best one is underlined.'\nProblem: 'Which method achieves the highest accuracy for the MN → US (f) setting?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/231.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Classification accuracy (mean ± std %) values of target domain over five independent runs on the digits datasets. The best performance is indicated in bold and the second best one is underlined." ], "reasoning": "By comparing the accuracy values for the MN → US (f) setting across all methods, DM-ADA achieves the highest accuracy of 96.7±0.5.", "question": "Which method achieves the highest accuracy for the MN → US (f) setting?", "answer": { "answer": "DM-ADA" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ADR method achieves the highest accuracy for the US → MN setting.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/231.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Classification accuracy (mean ± std %) values of target domain over five independent runs on the digits datasets. The best performance is indicated in bold and the second best one is underlined." ], "reasoning": "By comparing the accuracy values for the US → MN setting across all methods, DM-ADA achieves the highest accuracy of 94.2±0.9, not ADR.", "statement": "The ADR method achieves the highest accuracy for the US → MN setting.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: ECE and UCE test set results in % (M=15 bins). 0 % means perfect calibration. In TS calibration with MC dropout the same value of T was used to report both ECE and UCE.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/232.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: ECE and UCE test set results in % (M=15 bins). 0 % means perfect calibration. In TS calibration with MC dropout the same value of T was used to report both ECE and UCE." ], "reasoning": "MC dropout reduces miscalibration of model likelihood significantly, and with TS calibration, it further reduces ECE and UCE, especially for larger networks.", "description": "Tab. 1 reports test set results for different networks [17, 18] and data sets used to evaluate the performance of temperature scaling for dropout variational inference. The proposed UCE metric is used to quantify calibration of uncertainty.. Uncalibrated ECE shows, that MC dropout already reduces miscalibration of model likelihood by up to 6.6 percentage points. With TS calibration, MC dropout reduces ECE by 45–66 % and UCE drops drastically (especially for larger networks)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: ECE and UCE test set results in % (M=15 bins). 0 % means perfect calibration. In TS calibration with MC dropout the same value of T was used to report both ECE and UCE.'\nProblem: 'Which model and dataset combination shows the highest reduction in ECE after TS calibration with MC dropout?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/232.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: ECE and UCE test set results in % (M=15 bins). 0 % means perfect calibration. In TS calibration with MC dropout the same value of T was used to report both ECE and UCE." ], "reasoning": "By comparing the ECE values before and after TS calibration with MC dropout for each model and dataset combination, the highest reduction is observed for the CIFAR-10 dataset with the ResNet-18 model, where ECE drops from 8.41 to 0.47.", "question": "Which model and dataset combination shows the highest reduction in ECE after TS calibration with MC dropout?", "answer": "So, the answer is {\"answer\": \"CIFAR-10 with ResNet-18\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTS calibration with MC dropout reduces UCE for the CIFAR-100 dataset with the DenseNet-169 model to below 3.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/232.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: ECE and UCE test set results in % (M=15 bins). 0 % means perfect calibration. In TS calibration with MC dropout the same value of T was used to report both ECE and UCE." ], "reasoning": "The table shows that for the CIFAR-100 dataset with the DenseNet-169 model, the UCE after TS calibration with MC dropout is 2.69, which is below 3.", "statement": "TS calibration with MC dropout reduces UCE for the CIFAR-100 dataset with the DenseNet-169 model to below 3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Quantitative comparisons of mean per joint position error (MPJPE), PCK and AUC between the estimated 3D pose and ground truth on H3.6M under Protocol #1, #2, #3 and MPI-INF-3DHP under Protocol #1, #2. - indicates results not reported. Lower MPJPE, higher PCK and AUC indicate better performance. Best scores are marked in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/233.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Quantitative comparisons of mean per joint position error (MPJPE), PCK and AUC between the estimated 3D pose and ground truth on H3.6M under Protocol #1, #2, #3 and MPI-INF-3DHP under Protocol #1, #2. - indicates results not reported. Lower MPJPE, higher PCK and AUC indicate better performance. Best scores are marked in bold." ], "reasoning": "DenseRaC outperforms other parametric body model estimators across multiple protocols, demonstrating the effectiveness of each component in its design.", "description": "As reported in Table 1, we can observe each component in DenseRaC contributes to the final performance and leads DenseRaC to outperform state-of-the-art parametric body model estimators by a large margin." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Quantitative comparisons of mean per joint position error (MPJPE), PCK and AUC between the estimated 3D pose and ground truth on H3.6M under Protocol #1, #2, #3 and MPI-INF-3DHP under Protocol #1, #2. - indicates results not reported. Lower MPJPE, higher PCK and AUC indicate better performance. Best scores are marked in bold.'\nProblem: 'Which model has the lowest MPJPE score in Protocol #2 and how does it compare to DenseRaC baseline?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/233.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Quantitative comparisons of mean per joint position error (MPJPE), PCK and AUC between the estimated 3D pose and ground truth on H3.6M under Protocol #1, #2, #3 and MPI-INF-3DHP under Protocol #1, #2. - indicates results not reported. Lower MPJPE, higher PCK and AUC indicate better performance. Best scores are marked in bold." ], "reasoning": "By examining Protocol #2, we see that Yang (CVPR'18) has the lowest MPJPE score of 37.7.", "question": "Which model has the lowest MPJPE score in Protocol #2?", "answer": "So, the answer is {\"answer\": \"Yang (CVPR'18)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDenseRaC baseline has the highest MPJPE score in Protocol #1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/233.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Quantitative comparisons of mean per joint position error (MPJPE), PCK and AUC between the estimated 3D pose and ground truth on H3.6M under Protocol #1, #2, #3 and MPI-INF-3DHP under Protocol #1, #2. - indicates results not reported. Lower MPJPE, higher PCK and AUC indicate better performance. Best scores are marked in bold." ], "reasoning": "By examining Protocol #1, we see that DenseRaC baseline has an MPJPE score of 82.4, which is not the highest. HMR (CVPR'18) has the highest score of 88.0.", "statement": "DenseRaC baseline has the highest MPJPE score in Protocol #1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Quantitative comparisons of mean per joint position error (MPJPE), PCK and AUC between the estimated 3D pose and ground truth on H3.6M under Protocol #1, #2, #3 and MPI-INF-3DHP under Protocol #1, #2. - indicates results not reported. Lower MPJPE, higher PCK and AUC indicate better performance. Best scores are marked in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/234.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Quantitative comparisons of mean per joint position error (MPJPE), PCK and AUC between the estimated 3D pose and ground truth on H3.6M under Protocol #1, #2, #3 and MPI-INF-3DHP under Protocol #1, #2. - indicates results not reported. Lower MPJPE, higher PCK and AUC indicate better performance. Best scores are marked in bold." ], "reasoning": "DenseRaC, with its components, significantly improves performance over state-of-the-art parametric body model estimators.", "description": "As reported in Table 1, we can observe each component in DenseRaC contributes to the final performance and leads DenseRaC to outperform state-of-the-art parametric bodymodel estimators by a large margin." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Quantitative comparisons of mean per joint position error (MPJPE), PCK and AUC between the estimated 3D pose and ground truth on H3.6M under Protocol #1, #2, #3 and MPI-INF-3DHP under Protocol #1, #2. - indicates results not reported. Lower MPJPE, higher PCK and AUC indicate better performance. Best scores are marked in bold.'\nProblem: 'What is the MPJPE score for DenseRaC baseline under Protocol #1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/234.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Quantitative comparisons of mean per joint position error (MPJPE), PCK and AUC between the estimated 3D pose and ground truth on H3.6M under Protocol #1, #2, #3 and MPI-INF-3DHP under Protocol #1, #2. - indicates results not reported. Lower MPJPE, higher PCK and AUC indicate better performance. Best scores are marked in bold." ], "reasoning": "To find the MPJPE score for DenseRaC baseline under Protocol #1, locate the row labeled 'DenseRaC baseline' and the column labeled 'Protocol #1 MPJPE'. The value is 123.1.", "question": "What is the MPJPE score for DenseRaC baseline under Protocol #1?", "answer": "So, the answer is {\"answer\": \"123.1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDenseRaC with synthetic data achieves the highest PCK score under Protocol #1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/234.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Quantitative comparisons of mean per joint position error (MPJPE), PCK and AUC between the estimated 3D pose and ground truth on H3.6M under Protocol #1, #2, #3 and MPI-INF-3DHP under Protocol #1, #2. - indicates results not reported. Lower MPJPE, higher PCK and AUC indicate better performance. Best scores are marked in bold." ], "reasoning": "To verify this statement, compare the PCK scores under Protocol #1 for all methods. DenseRaC with synthetic data has a PCK score of 76.9, which is the highest among all listed methods.", "statement": "DenseRaC with synthetic data achieves the highest PCK score under Protocol #1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Correlation of extrapolation scores and ensemble std. deviations on 4 datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/235.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Correlation of extrapolation scores and ensemble std. deviations on 4 datasets." ], "reasoning": "The Pearson correlation coefficients indicate a strong linear relationship in the Boston, Abalone, and Wine datasets, while the Diabetes dataset shows a weaker relationship with higher standard deviations.", "description": "On each dataset, we found a similar, significantly linear relationship (see Table 1 and Appendix A). We note the relationship is weaker with the Diabetes dataset; the standard deviations of these ensembles are an order of magnitude higher than the other datasets, indicating much noisier data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Correlation of extrapolation scores and ensemble std. deviations on 4 datasets.'\nProblem: 'Which dataset has the weakest linear relationship according to the Pearson correlation coefficient?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/235.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Correlation of extrapolation scores and ensemble std. deviations on 4 datasets." ], "reasoning": "The Pearson correlation coefficient for the Diabetes dataset is 0.50, which is lower than the coefficients for the Boston, Abalone, and Wine datasets.", "question": "Which dataset has the weakest linear relationship according to the Pearson correlation coefficient?", "answer": "So, the answer is {\"answer\": \"Diabetes\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Wine dataset has the strongest linear relationship.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/235.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Correlation of extrapolation scores and ensemble std. deviations on 4 datasets." ], "reasoning": "The Pearson correlation coefficient for the Wine dataset is 0.87, which is the highest among the listed datasets.", "statement": "The Wine dataset has the strongest linear relationship.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: AUC for Latent Factors OOD detection task. Column heading denotes in-distribution definitions: labels are M (Male) and A (Attractive); spurious correlates are E (Eyeglasses) and H (Wearing Hat). Image is in-distribution iff label = spurious correlate. LE stands for local ensembles. Each Lanczos iteration uses 3000 eigenvectors. 500 examples from each test set are used. 95% CI is bolded.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/236.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: AUC for Latent Factors OOD detection task. Column heading denotes in-distribution definitions: labels are M (Male) and A (Attractive); spurious correlates are E (Eyeglasses) and H (Wearing Hat). Image is in-distribution iff label = spurious correlate. LE stands for local ensembles. Each Lanczos iteration uses 3000 eigenvectors. 500 examples from each test set are used. 95% CI is bolded." ], "reasoning": "The loss gradient variant of local ensembles achieves the best performance on most settings, while the effect of increasing m is inconsistent between experiments.", "description": "In Table 2, we present results for each of the four L,. settings, showing both the loss gradient and the prediction gradient variant of local ensembles. Note that the loss gradient cannot be calculated at test time since we do not have labels available — instead, we calculate a separate extrapolation score using the gradient for the loss with respect to each possible label, and take the minimum. Our method achieves the best performance on most settings, and is competitive with the best baseline on each. However, the variation between the tasks is quite noteworthy. We note two patterns in particular. Firstly, we note that the performance of MaxProb and the loss gradient variant of our method are quite correlated, and we hypothesize this correlation is related to ∇ ˆY (cid:96). Additionally, observe the effect of increasing m is inconsistent between experiments:" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: AUC for Latent Factors OOD detection task. Column heading denotes in-distribution definitions: labels are M (Male) and A (Attractive); spurious correlates are E (Eyeglasses) and H (Wearing Hat). Image is in-distribution iff label = spurious correlate. LE stands for local ensembles. Each Lanczos iteration uses 3000 eigenvectors. 500 examples from each test set are used. 95% CI is bolded.'\nProblem: 'Which method achieves the highest performance in the M/H setting?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/236.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: AUC for Latent Factors OOD detection task. Column heading denotes in-distribution definitions: labels are M (Male) and A (Attractive); spurious correlates are E (Eyeglasses) and H (Wearing Hat). Image is in-distribution iff label = spurious correlate. LE stands for local ensembles. Each Lanczos iteration uses 3000 eigenvectors. 500 examples from each test set are used. 95% CI is bolded." ], "reasoning": "To determine the highest performance in the M/H setting, we compare the values in the M/H column for all methods. The highest value is 0.684, achieved by the LE (Loss) method.", "question": "Which method achieves the highest performance in the M/H setting?", "answer": "So, the answer is {\"answer\": \"LE (Loss)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LE (Predictions) method achieves the highest performance in the A/H setting.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/236.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: AUC for Latent Factors OOD detection task. Column heading denotes in-distribution definitions: labels are M (Male) and A (Attractive); spurious correlates are E (Eyeglasses) and H (Wearing Hat). Image is in-distribution iff label = spurious correlate. LE stands for local ensembles. Each Lanczos iteration uses 3000 eigenvectors. 500 examples from each test set are used. 95% CI is bolded." ], "reasoning": "To verify this statement, we compare the values in the A/H column for all methods. The highest value is 0.582, achieved by the LE (Predictions) method.", "statement": "The LE (Predictions) method achieves the highest performance in the A/H setting.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Single-View Reconstruction. We report Chamfer-L1 distances wrt. the ground truth meshes for the single-view experiment. We compare against Differentiable Ray Consistency (DRC) [79] (2D and 2.5D supervision), Soft Rasterizer [44] (2D supervision), 3D-R2N2 [13], Occupancy Networks (ONet) [48], and Pixel2Mesh [80] (all 3D supervision).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/237.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Single-View Reconstruction. We report Chamfer-L1 distances wrt. the ground truth meshes for the single-view experiment. We compare against Differentiable Ray Consistency (DRC) [79] (2D and 2.5D supervision), Soft Rasterizer [44] (2D supervision), 3D-R2N2 [13], Occupancy Networks (ONet) [48], and Pixel2Mesh [80] (all 3D supervision)." ], "reasoning": "Our method achieves the highest performance among 2D supervised approaches and rivals methods with full 3D supervision.", "description": "In Table 1 and Fig. 4 we show quantitative and qualitative results for our method and various baselines. We can see that our method is able to infer accurate 3D shape and texture representations from single-view images when only trained on multi-view images and object masks as supervision signal. Quantitatively (Table 1), our method performs best among the approaches with 2D supervision and rivals the quality of methods with full 3D supervision. When trained with depth, our method performs comparably to the best methods which use full 3D information. Qualita" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Single-View Reconstruction. We report Chamfer-L1 distances wrt. the ground truth meshes for the single-view experiment. We compare against Differentiable Ray Consistency (DRC) [79] (2D and 2.5D supervision), Soft Rasterizer [44] (2D supervision), 3D-R2N2 [13], Occupancy Networks (ONet) [48], and Pixel2Mesh [80] (all 3D supervision).'\nProblem: 'Which method performs best in the 'lamp' category under 2D supervision?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/237.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Single-View Reconstruction. We report Chamfer-L1 distances wrt. the ground truth meshes for the single-view experiment. We compare against Differentiable Ray Consistency (DRC) [79] (2D and 2.5D supervision), Soft Rasterizer [44] (2D supervision), 3D-R2N2 [13], Occupancy Networks (ONet) [48], and Pixel2Mesh [80] (all 3D supervision)." ], "reasoning": "By comparing the values under the 'lamp' category for all 2D supervision methods, '2D Supervision SoftRas' has the highest score of 0.413.", "question": "Which method performs best in the 'lamp' category under 2D supervision?", "answer": "So, the answer is {\"answer\": \"2D Supervision SoftRas\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur method performs best in the 'car' category under 2D supervision.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/237.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Single-View Reconstruction. We report Chamfer-L1 distances wrt. the ground truth meshes for the single-view experiment. We compare against Differentiable Ray Consistency (DRC) [79] (2D and 2.5D supervision), Soft Rasterizer [44] (2D supervision), 3D-R2N2 [13], Occupancy Networks (ONet) [48], and Pixel2Mesh [80] (all 3D supervision)." ], "reasoning": "In the 'car' category, '2D Supervision Ours (LRGB)' has a score of 0.196, which is lower than '2D Supervision DRC (Mask)' with a score of 0.340 and '2D Supervision SoftRas' with a score of 0.221.", "statement": "Our method performs best in the 'car' category under 2D supervision.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Multi-View Stereo. We show quantitative results for scans 65, 106, and 118 on the DTU dataset. For the baselines, we perform screened Poisson surface reconstruction (sPSR) [34] with trim parameters 0, 5, and 7 to obtain the final output. It shows that our generic method achieves results comparable to the highly optimized MVS methods.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/238.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Multi-View Stereo. We show quantitative results for scans 65, 106, and 118 on the DTU dataset. For the baselines, we perform screened Poisson surface reconstruction (sPSR) [34] with trim parameters 0, 5, and 7 to obtain the final output. It shows that our generic method achieves results comparable to the highly optimized MVS methods." ], "reasoning": "Table 2 shows that our approach achieves competitive results compared to highly tuned MVS algorithms, particularly excelling in Chamfer-L1 metrics.", "description": "Results: We show qualitative and quantitative results in Fig. 6 and Table 2. Qualitatively, we find that our method can be used for multi-view 3D reconstruction, directly resulting in watertight meshes. The ability to accurately model cavities of the objects shows that our model uses texture information to improve over the visual hull (Fig. 7). Quantitatively, Table 2 shows that our approach rivals the results from highly tuned MVS algorithms. We note that the DTU ground truth is itself sparse (Fig. 7c) and methods are therefore rewarded for trading off completeness for accuracy, which explains the better quantitative performance of the baselines for higher trim parameters (Fig. 8)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Multi-View Stereo. We show quantitative results for scans 65, 106, and 118 on the DTU dataset. For the baselines, we perform screened Poisson surface reconstruction (sPSR) [34] with trim parameters 0, 5, and 7 to obtain the final output. It shows that our generic method achieves results comparable to the highly optimized MVS methods.'\nProblem: 'Which method has the best Chamfer-L1 score at trim parameter 7?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/238.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Multi-View Stereo. We show quantitative results for scans 65, 106, and 118 on the DTU dataset. For the baselines, we perform screened Poisson surface reconstruction (sPSR) [34] with trim parameters 0, 5, and 7 to obtain the final output. It shows that our generic method achieves results comparable to the highly optimized MVS methods." ], "reasoning": "To find the best Chamfer-L1 score at trim parameter 7, look at the Chamfer-L1 column for rows with trim parameter 7. The lowest value is 0.733 for Colmap + sPSR.", "question": "Which method has the best Chamfer-L1 score at trim parameter 7?", "answer": "So, the answer is {\"answer\": \"Colmap + sPSR\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOurs (LRGB + LDepth) has the highest completeness score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/238.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Multi-View Stereo. We show quantitative results for scans 65, 106, and 118 on the DTU dataset. For the baselines, we perform screened Poisson surface reconstruction (sPSR) [34] with trim parameters 0, 5, and 7 to obtain the final output. It shows that our generic method achieves results comparable to the highly optimized MVS methods." ], "reasoning": "To verify this, compare the completeness scores of all methods. Ours (LRGB + LDepth) has a completeness score of 0.775, which is not the highest. The highest completeness score is 1.424 for Tola + sPSR at trim parameter 7.", "statement": "Ours (LRGB + LDepth) has the highest completeness score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: MAE on QM9. DimeNet sets the state of the art on 11 targets, outperforming the second-best model on average by 31\\char37 (mean std. MAE).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/239.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: MAE on QM9. DimeNet sets the state of the art on 11 targets, outperforming the second-best model on average by 31\\char37 (mean std. MAE)." ], "reasoning": "DimeNet achieves the lowest mean standardized MAE and logMAE, setting the new state of the art on 11 out of 12 targets.", "description": "In Table 1 we report the mean absolute error (MAE) of each target and the overall mean standardized MAE (std. MAE) and mean standardized logMAE. DimeNet sets the new state of the art on 11 out of 12 targets and decreases mean std. MAE by 31 % and mean logMAE by 0.22 compared to the second-best model." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: MAE on QM9. DimeNet sets the state of the art on 11 targets, outperforming the second-best model on average by 31\\char37 (mean std. MAE).'\nProblem: 'Which model has the lowest mean standardized MAE and by how much does it decrease compared to the second-best model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/239.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: MAE on QM9. DimeNet sets the state of the art on 11 targets, outperforming the second-best model on average by 31\\char37 (mean std. MAE)." ], "reasoning": "The table shows that DimeNet has the lowest mean standardized MAE of 1.05. The description states that this is a 31% decrease compared to the second-best model.", "question": "How much does the model has the lowest mean standardized MAE decrease compared to the second-best model?", "answer": "So, the answer is {\"answer\": \"31%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDimeNet has the lowest logMAE among all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/239.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: MAE on QM9. DimeNet sets the state of the art on 11 targets, outperforming the second-best model on average by 31\\char37 (mean std. MAE)." ], "reasoning": "The table shows that DimeNet has a logMAE of -5.57, which is the lowest among all the models listed.", "statement": "DimeNet has the lowest logMAE among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Classification accuracy (mean ± std %) values of target domain over five independent runs on the Office-31 dataset. The best performance is indicated in bold and the second best one is underlined.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/240.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Classification accuracy (mean ± std %) values of target domain over five independent runs on the Office-31 dataset. The best performance is indicated in bold and the second best one is underlined." ], "reasoning": "Our method achieves the best performance in three out of four hard cases and ranks in the top two for the easier cases with accuracy higher than 99.5%.", "description": "Table 2 reports the performance of our method compared with other works.. Our approach obtains the best performance in three of four hard cases: A → W, W → A and A → D. For two easier cases: W → D and D → W, our approach achieves accuracy higher than 99.5% and ranks the first two places." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Classification accuracy (mean ± std %) values of target domain over five independent runs on the Office-31 dataset. The best performance is indicated in bold and the second best one is underlined.'\nProblem: 'Which method achieves the highest average performance across all cases?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/240.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Classification accuracy (mean ± std %) values of target domain over five independent runs on the Office-31 dataset. The best performance is indicated in bold and the second best one is underlined." ], "reasoning": "By comparing the average performance values in the last column, DM-ADA (ours) has the highest average performance of 81.6.", "question": "Which method achieves the highest average performance across all cases?", "answer": "So, the answer is {\"answer\": \"DM-ADA (ours)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDM-ADA (ours) achieves the highest accuracy in the W → D case.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/240.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Classification accuracy (mean ± std %) values of target domain over five independent runs on the Office-31 dataset. The best performance is indicated in bold and the second best one is underlined." ], "reasoning": "The table shows that DM-ADA (ours) has an accuracy of 99.9±0.1 in the W → D case, which is not the highest as MADA multi-adversarial has 100.0±0.0.", "statement": "DM-ADA (ours) achieves the highest accuracy in the W → D case.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Figure 2: Bi-LSTM results per class.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/241.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 2: Bi-LSTM results per class." ], "reasoning": "The BI-LSTM model achieves a mean precision of 85% and an F1-Score of 84% without preprocessing heuristics, unlike the CNN model which uses hand-crafted rules and regular expressions.", "description": "Figures 2 and 3 detail our results. Our BI-LSTM model archives a mean precision of 85% and f1-Score 84% without requiring any preprocessing heuristics and regular expressions. This contrasts with the CNN model of , which uses a set of hand crafted rules in the tokenization process as well as regular expressions to remove noisy text." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Figure 2: Bi-LSTM results per class.'\nProblem: 'What is the F1-Score of the Sent. category and how does it compare to the average F1-Score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/241.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 2: Bi-LSTM results per class." ], "reasoning": "The F1-Score of the Sent. category is 0.93, which is higher than the average F1-Score of 0.84.", "question": "What is the F1-Score of the Sent. category?", "answer": "So, the answer is {\"answer\": \"0.93\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe RE category has the lowest precision score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/241.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 2: Bi-LSTM results per class." ], "reasoning": "The precision score for the RE category is 0.77, which is higher than the precision score for the Acordão category, which is 0.71.", "statement": "The RE category has the lowest precision score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Comparison of metrics across different epochs'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/242.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Comparison of metrics across different epochs" ], "reasoning": "The model's recall decreases with more epochs, while precision increases, resulting in a saturated recall value of 0.4042 due to high-order pixel-wise classification of occluded images.", "description": "After training the model with the available data-set of 1500 distinct images over GPU, the weights of the trained model are saved and the model is evaluated to get the confusion matrix metrics as shown in Table.II and test-accuracy using the loss function (mean-squared error) as given in (3).. As depicted in (Table II), we notice that recall descends with the data-set unlike precision. This is because the classification performed by the model is a pixel-wise classification of occluded images, which is of very high order per image and the reason the saturated recall value for the available dataset is 0.4042 which is not the best for the available dataset due to surplus hardware." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Comparison of metrics across different epochs'\nProblem: 'What is the test-accuracy of the model after 115 epochs?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/242.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Comparison of metrics across different epochs" ], "reasoning": "The table shows the test-accuracy for different epochs. For 115 epochs, the test-accuracy is listed as 0.9610.", "question": "What is the test-accuracy of the model after 115 epochs?", "answer": "So, the answer is {\"answer\": \"0.9610\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model's precision is highest after 80 epochs.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/242.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Comparison of metrics across different epochs" ], "reasoning": "The table shows the precision values for different epochs. The highest precision value is 0.9889, which occurs after 115 epochs, not 80 epochs.", "statement": "The model's precision is highest after 80 epochs.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Performance of the current Segnet model'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/243.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Performance of the current Segnet model" ], "reasoning": "The model was trained on various data-set sizes and epochs, achieving a test accuracy of up to 96.1% with 1500 training images and 115 epochs.", "description": "With this support, the model has been trained on data-sets of 500 and 1500 images with their labelled ground-truth data, followed by validation on 200 and 300 images respectively (Table I), and the test-accuracy is obtained using the rest of data-set of 300 and 500 images respectively.. Similarly, certain distinctive test data-sets have been evaluated over different hyper-parameters to obtain the over-all accuracy of the model and the results had been tabulated in Table.I. We observed a significant improvement in the results in comparison to the existing methods like SVM, MLPC, as shown in Fig.6 and a few other adaptive algorithms, as in , .. To start with, we used smaller data-set of 100 images tested on the model trained with 300 images (Table I), on a system with specifications of I7 processor, 8GB RAM and 4GB Nvidia G-force GPU. But, the time-complexity hasn't reduced when compared to the previous models. Hence, the model has been implemented on a 8-core cluster based online (cloud) GPU with low time-complexity. As tabulated in [Table I], effective training with larger data-sets and better efficiency ( 97%) is made possible with CNN's. The images obtained through a set of Google APIs, near the surroundings of our institute (Fig.7) have been tested with the correlation coefficient approximated to value 1." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Performance of the current Segnet model'\nProblem: 'What is the test accuracy when the model is trained with 1500 images and tested with 500 images?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/243.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Performance of the current Segnet model" ], "reasoning": "The table shows that when the model is trained with 1500 images and tested with 500 images, the test accuracy is 94.7% for 80 epochs and 96.1% for 115 epochs.", "question": "What is the test accuracy when the model is trained with 1500 images and tested with 500 images for 115 epochs.?", "answer": "So, the answer is {\"answer\": \"96.1%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model achieved a test accuracy of 85.5% when trained with 500 images.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/243.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Performance of the current Segnet model" ], "reasoning": "The table indicates that the model achieved a test accuracy of 85.5% when trained with 500 images and tested with 300 images.", "statement": "The model achieved a test accuracy of 85.5% when trained with 500 images.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Mean test errors (%) over 30 trials at the final iteration in the experiment of selection of layers. The values in parentheses denote the standard deviation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/244.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Mean test errors (%) over 30 trials at the final iteration in the experiment of selection of layers. The values in parentheses denote the standard deviation." ], "reasoning": "AdaptiveLayer (a) shows the best performance with the lowest test error, while AdaptiveLayer (b) performs significantly worse with bad initialization. The performance differences between deterministic and stochastic predictions are not significant.", "description": "Table 1 shows the test error of each method at the final iteration.. We observe that AdaptiveLayer. (a) shows the best performance among the proposed methods, and the performances of AdaptiveLayer (b) become significantly worse when the bad initialization (θinit ≈ 0.968) is used.. Comparing the deterministic and stochastic predictions, the performance differences are not significant. The test error of StochasticLayer is inferior to the most proposed methods;" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Mean test errors (%) over 30 trials at the final iteration in the experiment of selection of layers. The values in parentheses denote the standard deviation.'\nProblem: 'Which method shows the best performance at the final iteration?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/244.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Mean test errors (%) over 30 trials at the final iteration in the experiment of selection of layers. The values in parentheses denote the standard deviation." ], "reasoning": "By comparing the test errors in the table, AdaptiveLayer (a) with θinit=0.5 Deterministic shows the lowest test error of 2.200, indicating the best performance.", "question": "Which method shows the best performance at the final iteration?", "answer": "So, the answer is {\"answer\": \"AdaptiveLayer (a) with θinit=0.5 Deterministic\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAdaptiveLayer (b) with λ=2 performs better than StochasticLayer.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/244.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Mean test errors (%) over 30 trials at the final iteration in the experiment of selection of layers. The values in parentheses denote the standard deviation." ], "reasoning": "Comparing the test errors, AdaptiveLayer (b) with λ=2 has a test error of 15.69 (θinit=0.5 Deterministic), which is worse than StochasticLayer's test error of 4.704.", "statement": "AdaptiveLayer (b) with λ=2 performs better than StochasticLayer.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Mean test errors (%) over 30 trials at the final iteration in the experiment of selection of activation functions. The values in parentheses denote the standard deviation. The training time of a typical single run is reported.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/245.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Mean test errors (%) over 30 trials at the final iteration in the experiment of selection of activation functions. The values in parentheses denote the standard deviation. The training time of a typical single run is reported." ], "reasoning": "AdaptiveActivation (stochastic) has the lowest test error but requires the most training time, while AdaptiveActivation (deterministic) and StochasticActivation have competitive performance.", "description": "Table 2 shows the test error and training time of each algorithm.. We observe that AdaptiveActivation (stochastic) outperforms StochasticActivation in which the Bernoulli parameters stay constant,. The predictive performance of AdaptiveActivation (deterministic) is competitive with StochasticActivation,. the obtained networks by AdaptiveActivation have a better classification performance compared to both uniform activations: ReLU and hyperbolic tangent.. Comparing the training time, we observe that the proposed method needs about twice the computational time for training compared to the fixed structured neural networks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Mean test errors (%) over 30 trials at the final iteration in the experiment of selection of activation functions. The values in parentheses denote the standard deviation. The training time of a typical single run is reported.'\nProblem: 'Which algorithm has the lowest test error and how much training time does it require?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/245.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Mean test errors (%) over 30 trials at the final iteration in the experiment of selection of activation functions. The values in parentheses denote the standard deviation. The training time of a typical single run is reported." ], "reasoning": "AdaptiveActivation (stochastic) has the lowest test error of 1.407.", "question": "Which algorithm has the lowest test error?", "answer": "So, the answer is {\"answer\": \"AdaptiveActivation (stochastic)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAdaptiveActivation (deterministic) has a lower test error than ReLU.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/245.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Mean test errors (%) over 30 trials at the final iteration in the experiment of selection of activation functions. The values in parentheses denote the standard deviation. The training time of a typical single run is reported." ], "reasoning": "The test error for AdaptiveActivation (deterministic) is 1.414, which is lower than the test error for ReLU, which is 1.609.", "statement": "AdaptiveActivation (deterministic) has a lower test error than ReLU.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Test errors (%) and computational time of the proposed method (AdaptiveNet) and the Bayesian optimization (BO) with different budgets in the experiment of adaptation of stochastic network. The mean values over 30 trials are reported in the proposed method, and the value in parentheses denotes the standard deviation. For the Bayesian optimization, the result of a single run is reported.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/246.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Test errors (%) and computational time of the proposed method (AdaptiveNet) and the Bayesian optimization (BO) with different budgets in the experiment of adaptation of stochastic network. The mean values over 30 trials are reported in the proposed method, and the value in parentheses denotes the standard deviation. For the Bayesian optimization, the result of a single run is reported." ], "reasoning": "The proposed method, AdaptiveNet, achieves a competitive test error with significantly lower computational time compared to Bayesian optimization, which requires more time as the budget increases.", "description": "Table 3 shows that the test errors of the stochastic networks obtained by the proposed method and the Bayesian optimization with different budgets, where budget indicates the number of hyper-parameters to be evaluated.. we observe that the computational time of the Bayesian optimization proportionally increases for the number of budgets while our method is more computationally efficient.. The proposed method can find a competitive stochastic network with reasonable computational time.. the Bayesian optimization could find a better configuration in this case within several ten budgets," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Test errors (%) and computational time of the proposed method (AdaptiveNet) and the Bayesian optimization (BO) with different budgets in the experiment of adaptation of stochastic network. The mean values over 30 trials are reported in the proposed method, and the value in parentheses denotes the standard deviation. For the Bayesian optimization, the result of a single run is reported.'\nProblem: 'Which method achieves the lowest test error and how much time does it take?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/246.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Test errors (%) and computational time of the proposed method (AdaptiveNet) and the Bayesian optimization (BO) with different budgets in the experiment of adaptation of stochastic network. The mean values over 30 trials are reported in the proposed method, and the value in parentheses denotes the standard deviation. For the Bayesian optimization, the result of a single run is reported." ], "reasoning": "The table shows that BO with a budget of 20 achieves the lowest test error of 1.490%, and it takes 18.29 hours.", "question": "Which method achieves the lowest test error?", "answer": "So, the answer is {\"answer\": \"BO (budget=20)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAdaptiveNet is more computationally efficient than Bayesian optimization with a budget of 10.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/246.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Test errors (%) and computational time of the proposed method (AdaptiveNet) and the Bayesian optimization (BO) with different budgets in the experiment of adaptation of stochastic network. The mean values over 30 trials are reported in the proposed method, and the value in parentheses denotes the standard deviation. For the Bayesian optimization, the result of a single run is reported." ], "reasoning": "The table shows that AdaptiveNet takes 1.01 hours, while Bayesian optimization with a budget of 10 takes 9.59 hours, indicating that AdaptiveNet is more computationally efficient.", "statement": "AdaptiveNet is more computationally efficient than Bayesian optimization with a budget of 10.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Test errors (%) at the final iteration in the experiment of connection selection for DenseNets. The values in parentheses denote the standard deviation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/247.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Test errors (%) at the final iteration in the experiment of connection selection for DenseNets. The values in parentheses denote the standard deviation." ], "reasoning": "AdaptiveConnection performs slightly better than DenseNet on CIFAR-100 but is inferior on CIFAR-10.", "description": "Table 4 shows the test errors of AdaptiveConnection and the normal DenseNet at the final iteration.. the stochastic prediction is slightly better than the deterministic one, but the difference is not significant.. The difference of the predictive performances between AdaptiveConnection and the normal DenseNet is not significant for the CIFAR-100 datasets, whereas AdaptiveConnection is inferior for the CIFAR-10 dataset." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Test errors (%) at the final iteration in the experiment of connection selection for DenseNets. The values in parentheses denote the standard deviation.'\nProblem: 'Which model has a lower test error on CIFAR-10 deterministic, and what is the error?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/247.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Test errors (%) at the final iteration in the experiment of connection selection for DenseNets. The values in parentheses denote the standard deviation." ], "reasoning": "Compare the CIFAR-10 deterministic test errors of AdaptiveConnection and Normal DenseNet. AdaptiveConnection has an error of 5.427, while Normal DenseNet has an error of 5.050.", "question": "Which model has a lower test error on CIFAR-10 deterministic?", "answer": "So, the answer is {\"answer\": \"Normal DenseNet\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAdaptiveConnection has a lower test error than Normal DenseNet on CIFAR-100 deterministic.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/247.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Test errors (%) at the final iteration in the experiment of connection selection for DenseNets. The values in parentheses denote the standard deviation." ], "reasoning": "Compare the CIFAR-100 deterministic test errors of AdaptiveConnection and Normal DenseNet. AdaptiveConnection has an error of 25.461, while Normal DenseNet has an error of 25.518.", "statement": "AdaptiveConnection has a lower test error than Normal DenseNet on CIFAR-100 deterministic.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Classification accuracy on the validation set of VisDA-2017 challenge.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/248.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Classification accuracy on the validation set of VisDA-2017 challenge." ], "reasoning": "Our approach, DM-ADA, achieves the highest accuracy of 75.6% on the VisDA-2017 cross-domain classification dataset, significantly outperforming other adaptation methods.", "description": "Table 3 reports the results on the VisDA-2017 cross-domain classification dataset.. Our approach achieves the highest accuracy among all adaptation approaches, and exceeds the baseline with a great margin." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Classification accuracy on the validation set of VisDA-2017 challenge.'\nProblem: 'Which method achieves the highest accuracy on the VisDA-2017 cross-domain classification dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/248.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Classification accuracy on the validation set of VisDA-2017 challenge." ], "reasoning": "By examining the table, we see that DM-ADA (ours) has the highest accuracy of 75.6%, which is greater than all other methods listed.", "question": "Which method achieves the highest accuracy on the VisDA-2017 cross-domain classification dataset?", "answer": "So, the answer is {\"answer\": \"DM-ADA\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe GTA method achieves an accuracy of 69.5% on the VisDA-2017 cross-domain classification dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/248.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Classification accuracy on the validation set of VisDA-2017 challenge." ], "reasoning": "The table shows that the GTA method has an accuracy of 69.5%, which matches the statement.", "statement": "The GTA method achieves an accuracy of 69.5% on the VisDA-2017 cross-domain classification dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II AUC NUMBERS FOR SHALLOW AND DEEP MODELS. NUMBERS IN BOLD INDICATE THE BEST MODELS FOR EACH ICU DOMAIN.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/249.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II AUC NUMBERS FOR SHALLOW AND DEEP MODELS. NUMBERS IN BOLD INDICATE THE BEST MODELS FOR EACH ICU DOMAIN." ], "reasoning": "CNN LSTM consistently outperforms all shallow baselines and shows a macro-averaged AUC of 0.832. Domain adaptation is beneficial for most domains except for the Coronary domain.", "description": "AUC NUMBERS FOR SHALLOW AND DEEP MODELS. NUMBERS IN BOLD INDICATE THE BEST MODELS FOR EACH ICU DOMAIN. TABLE II. Table II shows AUC numbers for each model. We report numbers for each ICU domain, and also the macro-averaged result. Clearly,. − CNN LSTM consistently outperforms all shallow baselines, and also . Employing CNN and LSTM components together is beneficial, since NT is consistently superior than CNN and LSTM. Domain adaptation is beneficial for most of the domains. The only exception occurs with the Coronary domain for which performance remains statistically the same when compared with NT. Overall, CNN LSTM shows a macro-averaged AUC of 0.832." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II AUC NUMBERS FOR SHALLOW AND DEEP MODELS. NUMBERS IN BOLD INDICATE THE BEST MODELS FOR EACH ICU DOMAIN.'\nProblem: 'Which model has the highest AUC for the Cardiac domain?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/249.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II AUC NUMBERS FOR SHALLOW AND DEEP MODELS. NUMBERS IN BOLD INDICATE THE BEST MODELS FOR EACH ICU DOMAIN." ], "reasoning": "By examining the table, the highest AUC value for the Cardiac domain is 0.885, which corresponds to the CNN-LSTM model.", "question": "Which model has the highest AUC for the Cardiac domain?", "answer": { "answer": "CNN-LSTM" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe CNN model has the highest AUC for the Surgical domain.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/249.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II AUC NUMBERS FOR SHALLOW AND DEEP MODELS. NUMBERS IN BOLD INDICATE THE BEST MODELS FOR EACH ICU DOMAIN." ], "reasoning": "The table shows that the highest AUC for the Surgical domain is 0.827, which corresponds to the CNN-LSTM model, not the CNN model.", "statement": "The CNN model has the highest AUC for the Surgical domain.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: AUC numbers for different feature transference approaches. Numbers in bold indicate the best transference approach for each target ICU domain.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/250.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: AUC numbers for different feature transference approaches. Numbers in bold indicate the best transference approach for each target ICU domain." ], "reasoning": "The best feature transference approach varies by ICU domain, with A1 and A2 being best for Surgical, A3 for Medical, and A2 for Cardiac and Coronary.", "description": "TABLE III AUC NUMBERS FOR DIFFERENT FEATURE TRANSFERENCE APPROACHES. NUMBERS IN BOLD INDICATE THE BEST TRANSFERENCE APPROACH FOR EACH TARGET ICU DOMAIN.. − Table III shows AUC numbers for CNN LSTM models learned following the different feature transference approaches. The best transference approach varies depending on the target ICU domain. Randomly initializing the weights for fine-tuning does not show to be the best approach, as A4 and A5 were not the best performers for any target domain. It seems that specific temporal patterns play an important role for mortality prediction in the Surgical domain, as A1 and A2 were the best approaches for this domain. For the Medical domain, A3 was the best approach, suggesting that features learned from other domains are effective. For the Cardiac and Coronary domains, A2 was the best transference approach, which indicates that specific features are important in this domain." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: AUC numbers for different feature transference approaches. Numbers in bold indicate the best transference approach for each target ICU domain.'\nProblem: 'Which feature transference approach is the best for the Medical domain?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/250.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: AUC numbers for different feature transference approaches. Numbers in bold indicate the best transference approach for each target ICU domain." ], "reasoning": "The table shows that A3 has the highest AUC number (0.782) for the Medical domain, indicating it is the best approach for this domain.", "question": "Which feature transference approach is the best for the Medical domain?", "answer": "So, the answer is {\"answer\": \"A3\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nA2 is the best feature transference approach for the Surgical domain.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/250.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: AUC numbers for different feature transference approaches. Numbers in bold indicate the best transference approach for each target ICU domain." ], "reasoning": "The table shows that A2 has the highest AUC number (0.827) for the Surgical domain, indicating it is the best approach for this domain.", "statement": "A2 is the best feature transference approach for the Surgical domain.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: CGR, GR and LP tasks under slicing ways S1 (left) and S2 (right): each entry (in %) is calculated by the mean over 20 time steps and over 10 runs; the best result of each half row is in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/251.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: CGR, GR and LP tasks under slicing ways S1 (left) and S2 (right): each entry (in %) is calculated by the mean over 20 time steps and over 10 runs; the best result of each half row is in bold." ], "reasoning": "Our method significantly outperforms other methods in most CGR and GR tasks, except for the GR-AP@10-S2-DNC case where both BCGD-local and our method perform poorly due to sparse DNC snapshots.", "description": "Both tasks are then evaluated by Average Precision@k (AP@k) score .. LP task is then evaluated by Area under the ROC Curve (AUC) score. As shown in Table 2, for all CGR and GR tasks, our method significantly outperforms other methods in most cases. One exception is the GR-AP@10-S2-DNC case where BCGD-local and our method both obtain bad results, which is due to that S2 makes the snapshots of DNC snapshots dramatically sparse as shown in Table 1. For all LP tasks, all methods except DynGEM are comparable, since no method can always outperform others." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: CGR, GR and LP tasks under slicing ways S1 (left) and S2 (right): each entry (in %) is calculated by the mean over 20 time steps and over 10 runs; the best result of each half row is in bold.'\nProblem: 'Which method achieved the highest score for the LP-AUC-S2 task on the Elec dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/251.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: CGR, GR and LP tasks under slicing ways S1 (left) and S2 (right): each entry (in %) is calculated by the mean over 20 time steps and over 10 runs; the best result of each half row is in bold." ], "reasoning": "To find the highest score for the LP-AUC-S2 task on the Elec dataset, look at the LP-AUC-S2 column for the Elec row. The highest score is 90.34, achieved by our method.", "question": "What is the highest score for the LP-AUC-S2 task on the Elec dataset?", "answer": "So, the answer is {\"answer\": \"90.34\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBCGD g outperforms our method in the CGR-AP@10-S1 task for the Chess dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/251.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: CGR, GR and LP tasks under slicing ways S1 (left) and S2 (right): each entry (in %) is calculated by the mean over 20 time steps and over 10 runs; the best result of each half row is in bold." ], "reasoning": "To verify this statement, compare the scores of BCGD g and our method in the CGR-AP@10-S1 task for the Chess dataset. BCGD g has a score of 15.85, while our method has a score of 80.27. Therefore, our method outperforms BCGD g.", "statement": "BCGD g outperforms our method in the CGR-AP@10-S1 task for the Chess dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Wall-clock time under slicing way S1: the left part shows the total time over 21 time steps including I/O; the right part shows the detailed time of the offline or online stage excluding I/O.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/252.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Wall-clock time under slicing way S1: the left part shows the total time over 21 time steps including I/O; the right part shows the detailed time of the offline or online stage excluding I/O." ], "reasoning": "The table shows that the method 'Ours0.2,0.5' is faster than other methods, especially in the online stage for t≥1,avg.", "description": "To access wall-clock time, all the results as shown in Table 3 are produced. It is obvious that our method is much faster than other methods. Considering online stage Ours0.2,0.5 t≥1,avg, the superiority will become more obvious if lasting for more times steps." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Wall-clock time under slicing way S1: the left part shows the total time over 21 time steps including I/O; the right part shows the detailed time of the offline or online stage excluding I/O.'\nProblem: 'Which method has the lowest wall-clock time for the GEM 'DNC' in the online stage for t≥1,avg?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/252.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Wall-clock time under slicing way S1: the left part shows the total time over 21 time steps including I/O; the right part shows the detailed time of the offline or online stage excluding I/O." ], "reasoning": "To find the method with the lowest wall-clock time for 'DNC' in the online stage for t≥1,avg, we compare the values in the 'Ours0.2,0.5 t≥1,avg' column. The lowest value is 3.89.", "question": "Which method has the lowest wall-clock time for the GEM 'DNC' in the online stage for t≥1,avg?", "answer": "So, the answer is {\"answer\": \"Ours0.2,0.5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe method 'Ours0.2,0.5' has the highest wall-clock time for the GEM 'FBW' in the online stage for t≥1,avg.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/252.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Wall-clock time under slicing way S1: the left part shows the total time over 21 time steps including I/O; the right part shows the detailed time of the offline or online stage excluding I/O." ], "reasoning": "To verify this statement, we compare the values in the 'Ours0.2,0.5 t≥1,avg' column for 'FBW' with other methods. The value for 'Ours0.2,0.5' is 100.83, which is not the highest.", "statement": "The method 'Ours0.2,0.5' has the highest wall-clock time for the GEM 'FBW' in the online stage for t≥1,avg.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Classification errors(%) on CIFAR10.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/253.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Classification errors(%) on CIFAR10." ], "reasoning": "The Cayley ADAM algorithm achieves the best error rates of 3.57% on CIFAR10 and 18.10% on CIFAR100 when used with the WRN-28-10 network.", "description": "Table 1 and Table 2 show classification errors on CIFAR10 and CIFAR100 respectively using different optimization algorithms. As shown in the tables, the proposed two algorithms achieve competitive performance, and for certain deep architectures, the best performance. Specifically, the network WRN-28-10 trained with Cayley ADAM achieves the best error rate of 3.57% and 18.10% on CIFAR10 and CIFAR100 respectively." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Classification errors(%) on CIFAR10.'\nProblem: 'Which optimization algorithm achieves the best error rate on the WRN-28-10 network for CIFAR10?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/253.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Classification errors(%) on CIFAR10." ], "reasoning": "By examining the table, the lowest error rate for the WRN-28-10 network on CIFAR10 is 3.57%, which is achieved by the Cayley ADAM algorithm.", "question": "Which optimization algorithm achieves the best error rate on the WRN-28-10 network for CIFAR10?", "answer": "So, the answer is {\"answer\": \"Cayley ADAM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Cayley SGD algorithm achieves the best error rate on the VGG-16 network for CIFAR10.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/253.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Classification errors(%) on CIFAR10." ], "reasoning": "By examining the table, the lowest error rate for the VGG-16 network on CIFAR10 is 5.77%, which is achieved by the Cayley SGD algorithm.", "statement": "The Cayley SGD algorithm achieves the best error rate on the VGG-16 network for CIFAR10.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Classification errors(%) on CIFAR100.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/254.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Classification errors(%) on CIFAR100." ], "reasoning": "The Cayley ADAM algorithm achieves the best error rates of 3.57% on CIFAR10 and 18.10% on CIFAR100 when used with the WRN-28-10 network.", "description": "Table 1 and Table 2 show classification errors on CIFAR10 and CIFAR100 respectively using different optimization algorithms. As shown in the tables, the proposed two algorithms achieve competitive performance, and for certain deep architectures, the best performance. Specifically, the network WRN-28-10 trained with Cayley ADAM achieves the best error rate of 3.57% and 18.10% on CIFAR10 and CIFAR100 respectively." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Classification errors(%) on CIFAR100.'\nProblem: 'Which optimization algorithm achieves the best error rate on CIFAR100 for the WRN-28-10 network?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/254.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Classification errors(%) on CIFAR100." ], "reasoning": "The table shows that the WRN-28-10 network trained with Cayley ADAM achieves the best error rate of 18.10% on CIFAR100.", "question": "Which optimization algorithm achieves the best error rate on CIFAR100 for the WRN-28-10 network?", "answer": "So, the answer is {\"answer\": \"Cayley ADAM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe WRN-28-10 network trained with Cayley SGD achieves the best error rate on CIFAR100.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/254.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Classification errors(%) on CIFAR100." ], "reasoning": "The table shows that the WRN-28-10 network trained with Cayley ADAM, not Cayley SGD, achieves the best error rate of 18.10% on CIFAR100.", "statement": "The WRN-28-10 network trained with Cayley SGD achieves the best error rate on CIFAR100.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Effectiveness of pixel-level mixup (PM), feature-level mixup (FM) and triplet loss (Tri).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/255.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Effectiveness of pixel-level mixup (PM), feature-level mixup (FM) and triplet loss (Tri)." ], "reasoning": "Feature-level mixup and pixel-level mixup both improve model performance, with additional gains observed when triplet loss is included.", "description": "Table 4 examines the effectiveness of pixel-level mixup (PM) and feature-level mixup (FM).. In the fourth row, featurelevel mixup achieves notable improvement compared with baseline,. In the fifth row, pixel-level mixup further enhance model's performance. In Table 4, we evaluate another key component, i.e., triplet loss (Tri).. In the third and sixth rows, it can also be observed that model's performance is improved after adding the triplet loss to discriminator's training process," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Effectiveness of pixel-level mixup (PM), feature-level mixup (FM) and triplet loss (Tri).'\nProblem: 'What is the accuracy improvement when both pixel-level mixup and triplet loss are applied compared to the baseline?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/255.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Effectiveness of pixel-level mixup (PM), feature-level mixup (FM) and triplet loss (Tri)." ], "reasoning": "The baseline accuracy is 76.7%. When both pixel-level mixup and triplet loss are applied, the accuracy is 83.9%. The improvement is 83.9% - 76.7% = 7.2%.", "question": "What is the accuracy improvement when both pixel-level mixup and triplet loss are applied compared to the baseline?", "answer": "So, the answer is {\"answer\": \"7.2%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model achieves the highest accuracy when both pixel-level mixup and triplet loss are applied.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/255.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Effectiveness of pixel-level mixup (PM), feature-level mixup (FM) and triplet loss (Tri)." ], "reasoning": "The highest accuracy in the table is 83.9%, which is achieved when both pixel-level mixup and triplet loss are applied.", "statement": "The model achieves the highest accuracy when both pixel-level mixup and triplet loss are applied.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Error rate and training time per epoch comparison to baselines with WRN-28-10 on CIFAR10 and CIFAR100. All experiments are performed on one TITAN Xp GPU.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/256.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Error rate and training time per epoch comparison to baselines with WRN-28-10 on CIFAR10 and CIFAR100. All experiments are performed on one TITAN Xp GPU." ], "reasoning": "The proposed algorithms achieve comparable error rates with state-of-the-art methods but run much faster, except for the baseline SGD and ADAM.", "description": "We compare the proposed algorithms with two sets of state of the art. The first set of approaches are soft orthonormality regularization approaches (Bansal et al., 2018).. The second set of approaches includes the following hard orthonormality methods: Polar decomposition(Absil et al., 2009), QR decomposition(Absil et al., 2009), closed-form Cayley transform, Wen&Yin (Wen & Yin, 2013), OMDSM(Huang et al., 2018a), DBN(Huang et al., 2018b).. Also, we use the closed-form Cayley transform without momentum as an ablation study of the momentum effect. All experiments are evaluated on the benchmark network WRN28-10. Table 3 shows that our algorithms achieve comparable error rates with state of the art. All algorithms are run on one TITAN Xp GPU, and their average training time are compared per epoch. Table 3 shows that our algorithms run much faster than existing algorithms, except for the baseline SGD and ADAM which do not impose orthonormality constraints." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Error rate and training time per epoch comparison to baselines with WRN-28-10 on CIFAR10 and CIFAR100. All experiments are performed on one TITAN Xp GPU.'\nProblem: 'Which method has the lowest error rate on CIFAR100 and what is its training time?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/256.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Error rate and training time per epoch comparison to baselines with WRN-28-10 on CIFAR10 and CIFAR100. All experiments are performed on one TITAN Xp GPU." ], "reasoning": "From the table, the method with the lowest error rate on CIFAR100 is 'Cayley ADAM (Ours)' with an error rate of 18.10%. The training time for this method is 224.4 seconds.", "question": "What is its training time for method has the lowest error rate on CIFAR100?", "answer": "So, the answer is {\"answer\": \"224.4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Cayley SGD (Ours) method has a lower error rate on CIFAR10 than the SRIP method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/256.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Error rate and training time per epoch comparison to baselines with WRN-28-10 on CIFAR10 and CIFAR100. All experiments are performed on one TITAN Xp GPU." ], "reasoning": "From the table, the error rate of Cayley SGD (Ours) on CIFAR10 is 3.66%, while the error rate of the SRIP method on CIFAR10 is 3.60%. Therefore, the statement is refuted.", "statement": "The Cayley SGD (Ours) method has a lower error rate on CIFAR10 than the SRIP method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Pixel-by-pixel MNIST accuracy and training time per iteration of the closed-form Cayley Transform, Cayley SGD, and Cayley ADAM for Full-uRNNs (Wisdom et al., 2016). All experiments are performed on one TITAN Xp GPU.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/257.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Pixel-by-pixel MNIST accuracy and training time per iteration of the closed-form Cayley Transform, Cayley SGD, and Cayley ADAM for Full-uRNNs (Wisdom et al., 2016). All experiments are performed on one TITAN Xp GPU." ], "reasoning": "The proposed Cayley SGD and Cayley ADAM algorithms reduce training time by about 35% while maintaining accuracy, as shown in Table 4.", "description": "Wisdom et al. (2016) restricted the transition unitary matrix on the Stiefel manifold via a closed-form Cayley transform. On the contrary, we use Cayley SGD with momentum and Cayley ADAM to reduce the training time. Table 4 shows that the proposed algorithms reduce the training. time by about 35% for all settings of the network, while maintaining the same level of accuracy. All experiments are performed on one TITAN Xp GPU." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Pixel-by-pixel MNIST accuracy and training time per iteration of the closed-form Cayley Transform, Cayley SGD, and Cayley ADAM for Full-uRNNs (Wisdom et al., 2016). All experiments are performed on one TITAN Xp GPU.'\nProblem: 'What is the accuracy of the Full-uRNN model with a hidden size of 512 when using Cayley ADAM?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/257.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Pixel-by-pixel MNIST accuracy and training time per iteration of the closed-form Cayley Transform, Cayley SGD, and Cayley ADAM for Full-uRNNs (Wisdom et al., 2016). All experiments are performed on one TITAN Xp GPU." ], "reasoning": "The table shows the accuracy of the Full-uRNN model with a hidden size of 512 when using Cayley ADAM as 96.9%.", "question": "What is the accuracy of the Full-uRNN model with a hidden size of 512 when using Cayley ADAM?", "answer": { "answer": "96.9%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Full-uRNN model with a hidden size of 116 has a higher accuracy with Cayley ADAM than with Closed-Form.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/257.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Pixel-by-pixel MNIST accuracy and training time per iteration of the closed-form Cayley Transform, Cayley SGD, and Cayley ADAM for Full-uRNNs (Wisdom et al., 2016). All experiments are performed on one TITAN Xp GPU." ], "reasoning": "The table shows that the Full-uRNN model with a hidden size of 116 has an accuracy of 92.7% with Cayley ADAM and 92.8% with Closed-Form, indicating that the statement is false.", "statement": "The Full-uRNN model with a hidden size of 116 has a higher accuracy with Cayley ADAM than with Closed-Form.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Checking unitariness by computing the error ||KHK−I||F for varying numbers of iterations in the iterative Cayley transform and the closed-form Cayley transform.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/258.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Checking unitariness by computing the error ||KHK−I||F for varying numbers of iterations in the iterative Cayley transform and the closed-form Cayley transform." ], "reasoning": "The iterative Cayley transform can approximate the unitary matrix effectively, with the error term decreasing significantly as the number of iterations s increases.", "description": "To show that the proposed algorithms are valid optimization algorithms on the Stiefel manifold, we check the unitariness of the hidden-to-hidden matrix K by computing the error term ||K H K − I||F during training. Table 5 compares average errors for varying numbers of iterations s. As can be seen, the iterative Cayley transform can approximate the unitary matrix when s = 2." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Checking unitariness by computing the error ||KHK−I||F for varying numbers of iterations in the iterative Cayley transform and the closed-form Cayley transform.'\nProblem: 'What is the average error for hidden size n=512 when s=2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/258.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Checking unitariness by computing the error ||KHK−I||F for varying numbers of iterations in the iterative Cayley transform and the closed-form Cayley transform." ], "reasoning": "To find the average error for hidden size n=512 when s=2, locate the row corresponding to n=512 and the column corresponding to s=2. The value in this cell is the average error.", "question": "What is the average error for hidden size n=512 when s=2?", "answer": "So, the answer is {\"answer\": \"2.562e-5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average error for hidden size n=116 when s=3 is greater than the average error for hidden size n=512 when s=0.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/258.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Checking unitariness by computing the error ||KHK−I||F for varying numbers of iterations in the iterative Cayley transform and the closed-form Cayley transform." ], "reasoning": "To verify this statement, compare the average error for hidden size n=116 when s=3 (7.353e-6) with the average error for hidden size n=512 when s=0 (6.787e-3). Since 7.353e-6 is less than 6.787e-3, the statement is refuted.", "statement": "The average error for hidden size n=116 when s=3 is greater than the average error for hidden size n=512 when s=0.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Numerical results shown in Fig. 5.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/259.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Numerical results shown in Fig. 5." ], "reasoning": "The optimized configurations (Opt. Simulator and Opt. Emulator) show a significant improvement in SINR values compared to the baseline setup, with approximately a 3 dB increase.", "description": "The optimization results obtained within the scenario described in the previous section are presented in Table.. Results show that in the proposed scenario, a baseline setup. performs significantly worse than the optimized ones.. The other two configurations represent the optimum obtained over the collected dataset (Opt. Simulator,. and the global optimum obtained using our framework (Opt. Emulator).. Results show a ∼ 3 dB improvement over the trivial baseline. Although in this case the results are really close" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Numerical results shown in Fig. 5.'\nProblem: 'What is the SINR value for the Opt. Emulator configuration at the 95th percentile?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/259.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Numerical results shown in Fig. 5." ], "reasoning": "The table shows the SINR values for different configurations. For the Opt. Emulator configuration, the SINR value at the 95th percentile (SINR95) is 39.64.", "question": "What is the SINR value for the Opt. Emulator configuration at the 95th percentile?", "answer": "So, the answer is {\"answer\": \"39.64\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe baseline setup has a higher SINR value at the 5th percentile compared to the Opt. Simulator.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/259.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Numerical results shown in Fig. 5." ], "reasoning": "The table shows that the SINR value at the 5th percentile (SINR5) for the baseline setup is 4.91, while for the Opt. Simulator it is 7.25. Therefore, the statement is refuted.", "statement": "The baseline setup has a higher SINR value at the 5th percentile compared to the Opt. Simulator.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Data sets and training parameters used for evaluation. #neurons refers to number of neurons per hidden layer.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/260.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Data sets and training parameters used for evaluation. #neurons refers to number of neurons per hidden layer." ], "reasoning": "The table provides details on various data sets used in experiments, including the number of instances, features, classes, neurons, batch size, learning rate, epochs, minimum samples per leaf, and whether pruning was applied.", "description": "Table. summarizes the data sets and sizes of the hidden layers of the MLPs used for the experiments." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Data sets and training parameters used for evaluation. #neurons refers to number of neurons per hidden layer.'\nProblem: 'Which data set has the highest number of instances and what is its batch size?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/260.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Data sets and training parameters used for evaluation. #neurons refers to number of neurons per hidden layer." ], "reasoning": "To find the data set with the highest number of instances, look at the 'instances' column and identify the maximum value, which is 100,000 for the Diabetes data set. Then, check the 'batch size' for this data set, which is 512.", "question": "What is its batch size for the data set with the highest number of instances?", "answer": "So, the answer is {\"answer\": \"512\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Iris data set has more features than the Titanic data set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/260.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Data sets and training parameters used for evaluation. #neurons refers to number of neurons per hidden layer." ], "reasoning": "The table shows that the Iris data set has 4 features, while the Titanic data set has 11 features. Therefore, the statement is incorrect.", "statement": "The Iris data set has more features than the Titanic data set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Consistency of DT predictions.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/261.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Consistency of DT predictions." ], "reasoning": "The table lists the results for four data sets in ascending order of the complexity of the generated decision trees (APL).", "description": "The results for four data sets are listed in Table III in ascending order of the complexity of the generated DTs (APL)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Consistency of DT predictions.'\nProblem: 'Which data set has the highest L1-O APL value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/261.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Consistency of DT predictions." ], "reasoning": "To find the data set with the highest L1-O APL value, compare the L1-O APL values for all data sets. The highest value is 57 for the Diabetes data set.", "question": "Which data set has the highest L1-O APL value?", "answer": "So, the answer is {\"answer\": \"Diabetes\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Titanic data set has the lowest FN APL value.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/261.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Consistency of DT predictions." ], "reasoning": "To verify this statement, compare the FN APL values for all data sets. The lowest FN APL value is 0.94 for the Mushroom data set, not the Titanic data set.", "statement": "The Titanic data set has the lowest FN APL value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Computation time in seconds.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/262.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Computation time in seconds." ], "reasoning": "The table shows that the run times of tree-regularized models are significantly higher than those of L1-O-regularized models, by a factor of 30 to 70.", "description": "As Table IV indicates, the run times of treeregularized models are 30–70 times higher than those of an L1-O-regularized model." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Computation time in seconds.'\nProblem: 'What is the run time difference between the Tree-regularized model and the L1-O-regularized model for the 2D-parabola dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/262.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Computation time in seconds." ], "reasoning": "The run time for the Tree-regularized model on the 2D-parabola dataset is 2,841.56, while the run time for the L1-O-regularized model is 42.72. The difference is 2,841.56 - 42.72.", "question": "What is the run time difference between the Tree-regularized model and the L1-O-regularized model for the 2D-parabola dataset?", "answer": "So, the answer is {\"answer\": \"2798.84\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe run time of the Tree-regularized model for the Iris dataset is more than 30 times higher than that of the L1-O-regularized model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/262.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Computation time in seconds." ], "reasoning": "The run time for the Tree-regularized model on the Iris dataset is 17.78, and for the L1-O-regularized model, it is 0.59. 17.78 is more than 30 times higher than 0.59.", "statement": "The run time of the Tree-regularized model for the Iris dataset is more than 30 times higher than that of the L1-O-regularized model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: P-value of RCMSE features of ECG signals (Left:Arousal, Right:Valence)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/263.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: P-value of RCMSE features of ECG signals (Left:Arousal, Right:Valence)" ], "reasoning": "RCMSE of ECG achieves the best p-value at a scaling factor of 2 for both arousal and valence.", "description": "The p-value of RCMSE and MMSE of ECG and GSR are shown in Table. and II.. RCMSE of ECG has the best p-value when setting scaling factor to 2 for both arousal and valence." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: P-value of RCMSE features of ECG signals (Left:Arousal, Right:Valence)'\nProblem: 'What is the p-value of RCMSE of ECG when the scaling factor is set to 2 and m=1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/263.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: P-value of RCMSE features of ECG signals (Left:Arousal, Right:Valence)" ], "reasoning": "To find the p-value of RCMSE of ECG when the scaling factor is set to 2 and m=1, locate the row where m=1 and the column where the scale is 2. The intersection of this row and column gives the p-value.", "question": "What is the p-value of RCMSE of ECG when the scaling factor is set to 2 and m=1?", "answer": "So, the answer is {\"answer\": \"0.07\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe p-value of RCMSE of ECG is lowest when the scaling factor is set to 2 and m=0.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/263.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: P-value of RCMSE features of ECG signals (Left:Arousal, Right:Valence)" ], "reasoning": "To verify this statement, locate the row where m=1 and the column where the scale is 2. The p-value at this intersection is 0.07, which is the lowest value in the table.", "statement": "The p-value of RCMSE of ECG is lowest when the scaling factor is set to 2 and m=1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: P-value of RCMSE features of ECG signals (Left:Arousal, Right:Valence)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/264.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: P-value of RCMSE features of ECG signals (Left:Arousal, Right:Valence)" ], "reasoning": "For arousal, the p-value becomes significant (0.01) when the scaling factor is greater than 5, indicating that higher scaling factors result in higher RCMSE for the positive class.", "description": "For RCMSE of GSR, we can observe that for arousal, the p-value becomes significant (0.01) when scaling factor is greater than 5. In these settings, the positive class would always have higher RCMSE, implying that the arousal of a subject is proportional to the complexity of its physiological signals. Note that the GSR would respond relatively slow according to the affect, thus the p-values become significant by increasing the scale factor." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: P-value of RCMSE features of ECG signals (Left:Arousal, Right:Valence)'\nProblem: 'At what scaling factor does the p-value become significant for m=2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/264.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: P-value of RCMSE features of ECG signals (Left:Arousal, Right:Valence)" ], "reasoning": "The table shows that for m=2, the p-value becomes significant (0.01) at a scaling factor of 2.", "question": "At what scaling factor does the p-value become significant for m=2?", "answer": "So, the answer is {\"answer\": \"2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe p-value for m=1 is significant at a scaling factor of 3.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/264.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: P-value of RCMSE features of ECG signals (Left:Arousal, Right:Valence)" ], "reasoning": "The table shows that for m=1, the p-value at a scaling factor of 3 is 0.14, which is not significant.", "statement": "The p-value for m=1 is significant at a scaling factor of 3.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Effectiveness of Dcls and pseudo target labels.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/265.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Effectiveness of Dcls and pseudo target labels." ], "reasoning": "Adding Dcls improves classification accuracy by 1.7%, and using pseudo target labels further enhances the model's performance.", "description": "the effectiveness of them is examined in Table 5.. After appending Dcls, classification accuracy increases by 1.7%,. On such basis, pseudo target labels introduce the discriminative information of target domain to the adaptation process and make model's performance state-of-the-art." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Effectiveness of Dcls and pseudo target labels.'\nProblem: 'What is the classification accuracy when both Dcls and pseudo target labels are used?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/265.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Effectiveness of Dcls and pseudo target labels." ], "reasoning": "The table shows that the classification accuracy is 83.9% when both Dcls and pseudo target labels are used.", "question": "What is the classification accuracy when both Dcls and pseudo target labels are used?", "answer": "So, the answer is {\"answer\": \"83.9\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUsing Dcls alone increases the classification accuracy to 82.3%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/265.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Effectiveness of Dcls and pseudo target labels." ], "reasoning": "The table indicates that with only Dcls, the classification accuracy is 82.3%, which supports the statement.", "statement": "Using Dcls alone increases the classification accuracy to 82.3%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: P-value of RCMSE features of GSR signals (A: arousal, V: valence) (m2 means pattern length = 2)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/266.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: P-value of RCMSE features of GSR signals (A: arousal, V: valence) (m2 means pattern length = 2)" ], "reasoning": "RCMPE of ECG shows significantly lower p-values (0.01) in valence compared to arousal across all scales.", "description": "The p-value of RCMPE and MMPE of ECG, GSR and EEG are shown in Table III, IV and V.. RCMPE of ECG performs better in valence than arousal with the significantly low p-values (0.01) in all scale." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: P-value of RCMSE features of GSR signals (A: arousal, V: valence) (m2 means pattern length = 2)'\nProblem: 'What is the p-value of RCMPE of ECG in valence at scale 15?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/266.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: P-value of RCMSE features of GSR signals (A: arousal, V: valence) (m2 means pattern length = 2)" ], "reasoning": "The table shows the p-values for arousal (A) and valence (V) across different scales. For scale 15, the p-value for valence (V) is 0.27.", "question": "What is the p-value of RCMPE of ECG in valence at scale 15?", "answer": "So, the answer is {\"answer\": \"0.27\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe p-value of RCMPE of ECG in arousal is higher than in valence at scale 5.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/266.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: P-value of RCMSE features of GSR signals (A: arousal, V: valence) (m2 means pattern length = 2)" ], "reasoning": "At scale 5, the p-value for arousal (A) is 0.10 and for valence (V) is 0.31. Since 0.10 is less than 0.31, the statement is refuted.", "statement": "The p-value of RCMPE of ECG in arousal is higher than in valence at scale 5.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: P-value of RCMPE features of ECG signals (A: arousal, V: valence) (m3 means embedding dimension = 3)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/267.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: P-value of RCMPE features of ECG signals (A: arousal, V: valence) (m3 means embedding dimension = 3)" ], "reasoning": "RCMPE of GSR shows better performance in arousal as most features are below 0.05, indicating higher RCMPE for the positive class.", "description": "RCMPE of GSR gets greater performance in arousal since most of the features are lower than 0.05 in arousal. The positive class will have higher RCMPE which is congruent with the case. in RCMSE of GSR." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: P-value of RCMPE features of ECG signals (A: arousal, V: valence) (m3 means embedding dimension = 3)'\nProblem: 'What is the highest feature value for arousal in the given table?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/267.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: P-value of RCMPE features of ECG signals (A: arousal, V: valence) (m3 means embedding dimension = 3)" ], "reasoning": "To find the highest feature value for arousal, look at the values under the 'A (m3)' row. The highest value is 0.1.", "question": "What is the highest feature value for arousal in the given table?", "answer": "So, the answer is {\"answer\": \"0.1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe feature value for arousal at scale 3 is higher than 0.05.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/267.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: P-value of RCMPE features of ECG signals (A: arousal, V: valence) (m3 means embedding dimension = 3)" ], "reasoning": "The table shows that the feature value for arousal (A (m3)) at scale 3 is 0.1, which is higher than 0.05.", "statement": "The feature value for arousal at scale 3 is higher than 0.05.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: P-value of RCMPE features of GSR signals (A: arousal, V: valence) (m2 means embedding dimension = 2)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/268.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: P-value of RCMPE features of GSR signals (A: arousal, V: valence) (m2 means embedding dimension = 2)" ], "reasoning": "As the scale factor increases, the performance of MMPE of EEG in valence improves, highlighting the significance of the coarse-graining step.", "description": "MMPE of EEG performs much better in valence when the scale factor goes up, which indicates the importance of the coarse-graining step." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: P-value of RCMPE features of GSR signals (A: arousal, V: valence) (m2 means embedding dimension = 2)'\nProblem: 'What is the value of V (m5) at scale 20?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/268.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: P-value of RCMPE features of GSR signals (A: arousal, V: valence) (m2 means embedding dimension = 2)" ], "reasoning": "To find the value of V (m5) at scale 20, locate the row labeled 'V (m5)' and the column labeled '20'. The intersection of this row and column gives the value.", "question": "What is the value of V (m5) at scale 20?", "answer": "So, the answer is {\"answer\": \"0.41\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe value of A (m2) remains constant across all scales.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/268.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: P-value of RCMPE features of GSR signals (A: arousal, V: valence) (m2 means embedding dimension = 2)" ], "reasoning": "To verify this statement, check the values in the row labeled 'A (m2)' across all columns. Almost all values are 0.01, indicating that the value remains constant.", "statement": "The value of A (m2) remains constant across all scales.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: P-value of MMPE features of EEG signals (AF3, AF4) (A: arousal, V: valence) (m4 means embedding dimension = 4)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/269.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: P-value of MMPE features of EEG signals (AF3, AF4) (A: arousal, V: valence) (m4 means embedding dimension = 4)" ], "reasoning": "Scheme III, which uses entropy-domain features and XGBoost, shows the best performance in most situations, with significant improvements in F1 scores for both arousal and valence compared to Scheme I and Scheme II.", "description": "The emotion recognition performance is shown in Table VI.. Scheme. was the one reported in , where 213 traditional features and Gaussian Naive Bayes were employed. Scheme II was also fed with traditional features but had XGBoost as the classification model. Scheme III was the one we proposed, which utilized entropy-domain features and XGBoost. We. concatenated old traditional features with new entropy-domain features which were statistically significant (p-value < 0.05). There were 41 and 101 new entropy-domain features for arousal and valence respectively. The result shows that the Entropy-assisted model (III) we propose has the best performance in most of the situations. For single modality, all three of them in valence raise the F1score by over or around 10%. Huge improvements compared to previous methods (Scheme I), +12.1% and +25.3% for arousal and valence respectively, are found in Fusion modalities. +17.8% can be found in EEG for valence between Scheme II and Scheme III." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: P-value of MMPE features of EEG signals (AF3, AF4) (A: arousal, V: valence) (m4 means embedding dimension = 4)'\nProblem: 'What is the F1 score for arousal at scale 5 in Scheme III?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/269.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: P-value of MMPE features of EEG signals (AF3, AF4) (A: arousal, V: valence) (m4 means embedding dimension = 4)" ], "reasoning": "To find the F1 score for arousal at scale 5 in Scheme III, locate the row labeled 'A (m4)' and the column labeled '5'. The value in this cell is the F1 score.", "question": "What is the F1 score for arousal at scale 5 in Scheme III?", "answer": { "answer": "0.66" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nScheme III shows an F1 score of 0.82 for arousal at scale 4.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/269.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: P-value of MMPE features of EEG signals (AF3, AF4) (A: arousal, V: valence) (m4 means embedding dimension = 4)" ], "reasoning": "To verify this statement, locate the row labeled 'A (m4)' and the column labeled '4'. The value in this cell is 0.82, which supports the statement.", "statement": "Scheme III shows an F1 score of 0.82 for arousal at scale 4.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VI: Mean F1-score of emotion recognition on AMIGOS (up: arousal, down: valence)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/270.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Mean F1-score of emotion recognition on AMIGOS (up: arousal, down: valence)" ], "reasoning": "The inclusion of entropy-domain features (MMPE of EEG and RCMPE of ECG) in Scheme III led to performance improvements in Fusion, with increases of +1.9% for arousal and +2.9% for valence compared to Scheme II.", "description": "Dominant features in terms of the feature importance of Fusion modalities in Scheme III are shown in Table VII. entropy-domain features are highlighted in boldface. The selection of entropy-domain features (MMPE of EEG and RCMPE of ECG) vindicates the improvements in the performance of Fusion between Scheme II and III where +1.9% and +2.9% are found for arousal and valence." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VI: Mean F1-score of emotion recognition on AMIGOS (up: arousal, down: valence)'\nProblem: 'What are the improvements in Fusion performance between Scheme II and Scheme III?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/270.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Mean F1-score of emotion recognition on AMIGOS (up: arousal, down: valence)" ], "reasoning": "The table shows the Fusion performance values for Scheme II and Scheme III as 0.687 and 0.706, respectively. The description states that these improvements are +1.9% for arousal.", "question": "What are the improvements for arousal in Fusion performance between Scheme II and Scheme III?", "answer": "So, the answer is {\"answer\": \"+1.9%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Fusion performance in Scheme III is higher than in Scheme II.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/270.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Mean F1-score of emotion recognition on AMIGOS (up: arousal, down: valence)" ], "reasoning": "The table shows that the Fusion performance value for Scheme III is 0.706, which is higher than the value for Scheme II, which is 0.687.", "statement": "The Fusion performance in Scheme III is higher than in Scheme II.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Mean +- 1 standard deviations in expected reconstruction error for the Spiral and Swiss roll datasets, as a function of n and n/k.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/271.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Mean +- 1 standard deviations in expected reconstruction error for the Spiral and Swiss roll datasets, as a function of n and n/k." ], "reasoning": "M1 performs better on 2D datasets compared to 3D datasets, and estimation quality improves as n/k decreases and n increases.", "description": "Thirdly, as can be seen in Tables 1 and 2, M1 tends to perform better on the 2D datasets when compared to the 3D datasets.. Upon varying n/k and n, we would expect a general trend of estimation quality to improve as n/k decreases (the number of tangent spaces k increases) and as the training set size n increases." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Mean +- 1 standard deviations in expected reconstruction error for the Spiral and Swiss roll datasets, as a function of n and n/k.'\nProblem: 'Which model performs better on the Spiral dataset when n/k is 55 and n is 1500?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/271.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Mean +- 1 standard deviations in expected reconstruction error for the Spiral and Swiss roll datasets, as a function of n and n/k." ], "reasoning": "To determine which model performs better on the Spiral dataset when n/k is 55 and n is 1500, compare the values for Spiral M1, M2, and M3. The values are 6.86E-5, 6.88E-5, and 8.42E-5 respectively. The lowest value indicates the best performance.", "question": "Which model performs better on the Spiral dataset when n/k is 55 and n is 1500?", "answer": "So, the answer is {\"answer\": \"Spiral M1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Swiss roll M1 model performs better than the Swiss roll M2 model when n/k is 75 and n is 2300.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/271.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Mean +- 1 standard deviations in expected reconstruction error for the Spiral and Swiss roll datasets, as a function of n and n/k." ], "reasoning": "To verify this statement, compare the values for Swiss roll M1 and M2 when n/k is 75 and n is 2300. The values are 3.48E-3 for M1 and 3.22E-3 for M2. Since 3.22E-3 is lower than 3.48E-3, M2 performs better.", "statement": "The Swiss roll M1 model performs better than the Swiss roll M2 model when n/k is 75 and n is 2300.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Best hyperparameters η obtained for AdamUCB, AdamCB and AdamS algorithms when training MNIST and CIFAR-10 on various architectures.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/272.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Best hyperparameters η obtained for AdamUCB, AdamCB and AdamS algorithms when training MNIST and CIFAR-10 on various architectures." ], "reasoning": "The table lists the best hyperparameter values for different optimizers across various datasets and models, with details on batch size and dropout.", "description": "We report the mean and the standard deviation for various performance metrics such as loss and accuracy across three random runs of our experiments. The hyperparameter η is tuned for each variant of the optimizer, and the best values found are listed in Table 1." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Best hyperparameters η obtained for AdamUCB, AdamCB and AdamS algorithms when training MNIST and CIFAR-10 on various architectures.'\nProblem: 'What is the ηCB value for the CIFAR-10 dataset with a CNN model and a batch size of 128?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/272.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Best hyperparameters η obtained for AdamUCB, AdamCB and AdamS algorithms when training MNIST and CIFAR-10 on various architectures." ], "reasoning": "To find the ηCB value, locate the row with the CIFAR-10 dataset, CNN model, and batch size of 128. The ηCB value in this row is 5×10⁻⁵.", "question": "What is the ηCB value for the CIFAR-10 dataset with a CNN model and a batch size of 128?", "answer": "So, the answer is {\"answer\": \"5×10⁻⁵\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ηS value for the MNIST dataset with an MLP model and a batch size of 16 is 0.05.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/272.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Best hyperparameters η obtained for AdamUCB, AdamCB and AdamS algorithms when training MNIST and CIFAR-10 on various architectures." ], "reasoning": "Locate the row with the MNIST dataset, MLP model, and batch size of 16. The ηS value in this row is 0.05, which matches the statement.", "statement": "The ηS value for the MNIST dataset with an MLP model and a batch size of 16 is 0.05.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Number of Successful Adversarial Attacks for Different Implementations'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/273.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Number of Successful Adversarial Attacks for Different Implementations" ], "reasoning": "The table shows the number of quasi-adversarial inputs and the number of successful attacks for different implementations on MNIST and CIFAR10 datasets.", "description": "We apply random perturbations outlined in Algorithm 1 to these quasi-adversarial inputs (x1 from Section 3.2 Step 2) to obtain adversarial inputs for the verified robust input image (x0 = αxseed from Section 3.2 Step 1). All the implementations that we have considered are successfully attacked. We present the detailed numbers in Table 1." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Number of Successful Adversarial Attacks for Different Implementations'\nProblem: 'How many successful attacks were there for the NNC,C implementation on the CIFAR10 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/273.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Number of Successful Adversarial Attacks for Different Implementations" ], "reasoning": "The table lists the number of successful attacks for each implementation on the CIFAR10 dataset. For the NNC,C implementation, the number of successful attacks is 12.", "question": "How many successful attacks were there for the NNC,C implementation on the CIFAR10 dataset?", "answer": { "answer": "12" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThere were more successful attacks for the NNG,C implementation on the MNIST dataset than on the CIFAR10 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/273.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Number of Successful Adversarial Attacks for Different Implementations" ], "reasoning": "The table shows that the number of successful attacks for the NNG,C implementation is 3 for the MNIST dataset and 6 for the CIFAR10 dataset. Therefore, there were fewer successful attacks on the MNIST dataset.", "statement": "There were more successful attacks for the NNG,C implementation on the MNIST dataset than on the CIFAR10 dataset.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Adversarial Attacks for Different Implementations Allowing Modifying Softmax Bias'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/274.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Adversarial Attacks for Different Implementations Allowing Modifying Softmax Bias" ], "reasoning": "The attack success rate improves when the bias of the softmax layer is decreased by τ0, and the minimal test accuracy of modified models is evaluated for successful attacks.", "description": "Furthermore, since a large τ0 in the quasi-adversarial inputs makes our method less likely to succeed and it can result from a large value of δ in step 1 due to the solver exceeding the time limit, we also evaluate the performance of our attack algorithm when modifying the bias of the softmax layer is allowed. We decrease the bias of the target class by τ0 to obtain a model that is still verifiably robust but easier to be attacked. Since modifying the bias would affect test accuracy, we also evaluate the minimal test accuracy of modified models of the successful attacks for each implementation. The results are presented in Table 2, showing that attack success rate is improved." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Adversarial Attacks for Different Implementations Allowing Modifying Softmax Bias'\nProblem: 'What is the minimal test accuracy for the MNIST dataset when the bias of the softmax layer is modified?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/274.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Adversarial Attacks for Different Implementations Allowing Modifying Softmax Bias" ], "reasoning": "The table shows the minimal test accuracy for the MNIST dataset under different attack implementations. The values are 98.40%, 97.83%, 98.57%, 97.83%, and 97.83%.", "question": "What is the minimal test accuracy for the MNIST dataset when the bias of the softmax layer is modified?", "answer": "So, the answer is {\"answer\": \"97.83%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe attack success rate for CIFAR10 is highest when using NNG,CWG 17.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/274.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Adversarial Attacks for Different Implementations Allowing Modifying Softmax Bias" ], "reasoning": "The table shows the number of attacks for CIFAR10 under different implementations. The highest number of attacks is 26, which corresponds to NNG,CWG 17.", "statement": "The attack success rate for CIFAR10 is highest when using NNG,CWG 17.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison of Different methods.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/275.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of Different methods." ], "reasoning": "AdOR has the worst MSE and MAE among the methods but performs much better in terms of ISE.", "description": "In this part, AdOR and AdOSE are compared with four regression methods: Support Vector Regression (Smola and Sch¨olkopf 2004), neural network with same structure as AdOR with MSE loss minimization, HSIC regression proposed by Mooij et al.(2009), and Gaussian Process regression (Williams and Rasmussen 1996) with RBF kernel.. ε. In each test, 300 samples are drawn from uniform distribution X ∼. (−1, 1). The function f (.) is nonlinear and ε is generated from different non-Gaussian distributions. Note that for AdOSE, the averaged Eε [Y |X = x] is plotted by feeding 5000 samples of nG at each X = x.. Comparison between methods is shown in Table 1 for different performance measures of Mean Squared Error (MSE), Mean Absolute Error (MAE) between predictions and responses, and Integral Squared Error (ISE) between estimated function and f (x).. As can be seen, in each case, AdOR has the worst MSE and MAE among the others; in contrast, its performance is much better in terms of ISE measure." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison of Different methods.'\nProblem: 'Which method has the lowest Mean Squared Error (MSE) for the noise model e2 X ε--Rayl(4)?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/275.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of Different methods." ], "reasoning": "To find the method with the lowest MSE for the noise model e2 X ε--Rayl(4), look at the MSE row for each method under the e2 X ε--Rayl(4) column. The lowest value is 1.209e+01, which corresponds to AdOR.", "question": "Which method has the lowest Mean Squared Error (MSE) for the noise model e2 X ε--Rayl(4)?", "answer": "So, the answer is {\"answer\": \"AdOR\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAdOSE has the highest Mean Absolute Error (MAE) for the noise model sigmoid(5 X) ε--BioNom(20,0.3).\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/275.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of Different methods." ], "reasoning": "To verify this statement, look at the MAE row for each method under the sigmoid(5 X) ε--BioNom(20,0.3) column. The highest value is 1.559e+00, which corresponds to GP, not AdOSE.", "statement": "AdOSE has the highest Mean Absolute Error (MAE) for the noise model sigmoid(5 X) ε--BioNom(20,0.3).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Averaged mean squared error on the real-world datasets over 50 runs. For each method, we select the best parameters by using 3-fold cross-validation. N/A means that a memory problem occurred when computing the model on the dataset. The asterisk * means that the results were computed without cross-validation but with fixed parameters (because of a too long runtime).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/276.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Averaged mean squared error on the real-world datasets over 50 runs. For each method, we select the best parameters by using 3-fold cross-validation. N/A means that a memory problem occurred when computing the model on the dataset. The asterisk * means that the results were computed without cross-validation but with fixed parameters (because of a too long runtime)." ], "reasoning": "The FALL method generally performs better or comparably to other methods, especially in the concrete data where it significantly outperforms the baselines.", "description": "Averaged mean squared error on the real-world datasets over 50 runs. For each method, we select the best parameters by using 3-fold cross-validation. N/A means that a memory problem occurred when computing the model on the dataset. The asterisk * means that the results were computed without cross-validation but with fixed parameters (because of a too long runtime).. We compared our method on several datasets with other methods. We computed the average mean squared error with 50 runs and the training and test time. We compared the proposed FALL model with KNN, kernel ridge regression (KRR), ridge and Lasso , and network Lasso . It should be noted that network Lasso is a state-of-the-art locally linear model. To compare the methods fairly, we selected the best hyperparameters by cross-validation with optuna with a timeout of 60 s.. Through experiments, the proposed FALL method was found to compare favorably with network Lasso and other existing baselines. In particular, the concrete data, the FALL method significantly outperforms baseline methods. Overall, because the FALL method is a locally linear model, the performance of the FALL method is similar to the one with KNN." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Averaged mean squared error on the real-world datasets over 50 runs. For each method, we select the best parameters by using 3-fold cross-validation. N/A means that a memory problem occurred when computing the model on the dataset. The asterisk * means that the results were computed without cross-validation but with fixed parameters (because of a too long runtime).'\nProblem: 'Which method has the lowest average mean squared error for the Fish toxicity data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/276.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Averaged mean squared error on the real-world datasets over 50 runs. For each method, we select the best parameters by using 3-fold cross-validation. N/A means that a memory problem occurred when computing the model on the dataset. The asterisk * means that the results were computed without cross-validation but with fixed parameters (because of a too long runtime)." ], "reasoning": "By examining the Fish toxicity data row, we compare the average mean squared errors of all methods. The FALL method has an error of 0.92, Network Lasso has 0.98, KNN has 1.14, KRR has 1.12, Ridge has 1.08, and Lasso has 1.18. The lowest error is 0.92 by the FALL method.", "question": "Which method has the lowest average mean squared error for the Fish toxicity data?", "answer": "So, the answer is {\"answer\": \"FALL\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe FALL method outperforms all other methods in the P.T. motor UPDRS data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/276.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Averaged mean squared error on the real-world datasets over 50 runs. For each method, we select the best parameters by using 3-fold cross-validation. N/A means that a memory problem occurred when computing the model on the dataset. The asterisk * means that the results were computed without cross-validation but with fixed parameters (because of a too long runtime)." ], "reasoning": "By examining the P.T. motor UPDRS data row, we compare the average mean squared errors of all methods. The FALL method has an error of 20.76, Network Lasso has 24.63, KNN has 20.88, KRR has 50.51, Ridge has 50.69, and Lasso has 56.81. The lowest error is 20.76 by the FALL method, but KNN has a very close error of 20.88, making the statement not entirely accurate.", "statement": "The FALL method outperforms all other methods in the P.T. motor UPDRS data.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Training time and testing time (in seconds) on real-world datasets over 50 runs. For FALL, the training time is the sum of the creation of the anchor models and the final model. N/A means that a memory problem occurred when computing the model on the dataset. The asterisk * means that the training time and testing time were averaged with few samples (because of a too long runtime).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/277.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Training time and testing time (in seconds) on real-world datasets over 50 runs. For FALL, the training time is the sum of the creation of the anchor models and the final model. N/A means that a memory problem occurred when computing the model on the dataset. The asterisk * means that the training time and testing time were averaged with few samples (because of a too long runtime)." ], "reasoning": "The FALL method's computation time is significantly smaller than that of Network Lasso, with training times often two orders of magnitude less.", "description": "Training time and testing time (in seconds) on real-world datasets over 50 runs. For FALL, the training time is the sum of the creation of the anchor models and the final model. N/A means that a memory problem occurred when computing the model on the dataset. The asterisk * means that the training time and testing time were averaged with few samples (because of a too long runtime).. The important point here is that the computation time of the FALL method is two orders of magnitude smaller than that of network Lasso." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Training time and testing time (in seconds) on real-world datasets over 50 runs. For FALL, the training time is the sum of the creation of the anchor models and the final model. N/A means that a memory problem occurred when computing the model on the dataset. The asterisk * means that the training time and testing time were averaged with few samples (because of a too long runtime).'\nProblem: 'What is the average training time for the Network Lasso method on the Fish toxicity data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/277.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Training time and testing time (in seconds) on real-world datasets over 50 runs. For FALL, the training time is the sum of the creation of the anchor models and the final model. N/A means that a memory problem occurred when computing the model on the dataset. The asterisk * means that the training time and testing time were averaged with few samples (because of a too long runtime)." ], "reasoning": "To find the average training time for the Network Lasso method on the Fish toxicity data, locate the 'Fish toxicity data' row and the 'Network Lasso' column. The value is 13.23 seconds.", "question": "What is the average training time for the Network Lasso method on the Fish toxicity data?", "answer": "So, the answer is {\"answer\": \"13.23\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe training time for the FALL method on the Superconduct data is less than 2 seconds.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/277.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Training time and testing time (in seconds) on real-world datasets over 50 runs. For FALL, the training time is the sum of the creation of the anchor models and the final model. N/A means that a memory problem occurred when computing the model on the dataset. The asterisk * means that the training time and testing time were averaged with few samples (because of a too long runtime)." ], "reasoning": "To verify this statement, locate the 'Superconduct data' row and the 'FALL' column. The training time is 1.24 seconds, which is less than 2 seconds.", "statement": "The training time for the FALL method on the Superconduct data is less than 2 seconds.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results of the real-world data experiments for different choices of the target domain. The evaluation score is MSE normalized by that of LOO (the lower the better). All experiments were repeated 10 times with different train-test splits of target domain data, and the average performance is reported with the standard errors in the brackets. The target column indicates abbreviated country names. Bold-face indicates the best score (Prop: proposed method, TrAda: TrAdaBoost, the numbers in the brackets of IW indicate the value of α). The proposed method often improves upon the baseline TrgOnly or is relatively more resistant to negative transfer, with notable improvements in DEU, GBR, and USA.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/278.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of the real-world data experiments for different choices of the target domain. The evaluation score is MSE normalized by that of LOO (the lower the better). All experiments were repeated 10 times with different train-test splits of target domain data, and the average performance is reported with the standard errors in the brackets. The target column indicates abbreviated country names. Bold-face indicates the best score (Prop: proposed method, TrAda: TrAdaBoost, the numbers in the brackets of IW indicate the value of α). The proposed method often improves upon the baseline TrgOnly or is relatively more resistant to negative transfer, with notable improvements in DEU, GBR, and USA." ], "reasoning": "The proposed method generally performs better than TarOnly and is more resistant to negative transfer compared to other methods.", "description": "The results are reported in Table 2. We report the MSE scores normalized by that of LOO. In many of the target domain choices, the naive baselines (SrcOnly and S&TV) suffer from negative transfer, i.e., higher average MSE than TarOnly (in 12 out of 18 domains). On the other hand, the proposed method successfully performs better than TarOnly or is more resistant to negative transfer than the other compared methods.. The performances of GDM, Copula, and IW are often inferior even compared to the baseline performance of SrcAndTarValid.. TrAdaBoost works reasonably well for many but not all domains. For some domains, it suffered from negative transfer similarly to others," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results of the real-world data experiments for different choices of the target domain. The evaluation score is MSE normalized by that of LOO (the lower the better). All experiments were repeated 10 times with different train-test splits of target domain data, and the average performance is reported with the standard errors in the brackets. The target column indicates abbreviated country names. Bold-face indicates the best score (Prop: proposed method, TrAda: TrAdaBoost, the numbers in the brackets of IW indicate the value of α). The proposed method often improves upon the baseline TrgOnly or is relatively more resistant to negative transfer, with notable improvements in DEU, GBR, and USA.'\nProblem: 'Which method has the lowest average MSE score for the target domain ITA?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/278.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of the real-world data experiments for different choices of the target domain. The evaluation score is MSE normalized by that of LOO (the lower the better). All experiments were repeated 10 times with different train-test splits of target domain data, and the average performance is reported with the standard errors in the brackets. The target column indicates abbreviated country names. Bold-face indicates the best score (Prop: proposed method, TrAda: TrAdaBoost, the numbers in the brackets of IW indicate the value of α). The proposed method often improves upon the baseline TrgOnly or is relatively more resistant to negative transfer, with notable improvements in DEU, GBR, and USA." ], "reasoning": "To find the method with the lowest average MSE score for ITA, compare the MSE scores for ITA across all methods. The lowest score is 4.15 under the 'Prop' method.", "question": "Which method has the lowest average MSE score for the target domain ITA?", "answer": "So, the answer is {\"answer\": \"Prop\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed method outperforms TarOnly in the target domain BEL.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/278.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of the real-world data experiments for different choices of the target domain. The evaluation score is MSE normalized by that of LOO (the lower the better). All experiments were repeated 10 times with different train-test splits of target domain data, and the average performance is reported with the standard errors in the brackets. The target column indicates abbreviated country names. Bold-face indicates the best score (Prop: proposed method, TrAda: TrAdaBoost, the numbers in the brackets of IW indicate the value of α). The proposed method often improves upon the baseline TrgOnly or is relatively more resistant to negative transfer, with notable improvements in DEU, GBR, and USA." ], "reasoning": "For the target domain BEL, the MSE score for the proposed method (7.94) is lower than that of TarOnly (10.70), indicating that the proposed method outperforms TarOnly.", "statement": "The proposed method outperforms TarOnly in the target domain BEL.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: The mean accuracies of the 5-way 1-shot and 5-shot tasks on the miniImageNet dataset, with 95% confidence intervals.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/279.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The mean accuracies of the 5-way 1-shot and 5-shot tasks on the miniImageNet dataset, with 95% confidence intervals." ], "reasoning": "Our model achieves state-of-the-art results in both 5-way 1-shot and 5-shot tasks using Resnet, and also the highest accuracy in the 5-way 5-shot task using Conv.", "description": "We report the experiment results in table 1.. When adopting the Resnet as the embedding module, our model can achieve state-ofthe-art results both in the 5-way 1-shot and 5-shot task, especially in the 5-shot task (3.95 % higher than the 74.44% reported by DN4 [Li et al., 2019a]). Besides, when using the Conv as embedding module, our model also achieves the highest accuracy on the 5-way 5-shot task, gaining the 5.57%, 2.2%, and 0.41% over CovaMNet [Li et al., 2019b], DN4, and Dynamic-Net [Gidaris and Komodakis, 2018]. We also obtain very competitive accuracy on 5-way 1-shot task with Conv embedding module, gaining 3.8%, 2.11%, 1.76% improvement over R2D2 [Bertinetto et al., 2019], CovaMNet, and DN4. As for Dynamic-Net and SalNet on the 5-way 1shot task, they perform very complicated training steps to obtain state-of-the-art results." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: The mean accuracies of the 5-way 1-shot and 5-shot tasks on the miniImageNet dataset, with 95% confidence intervals.'\nProblem: 'What is the accuracy improvement of our model over DN4 in the 5-way 5-shot task when using Conv as the embedding module?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/279.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The mean accuracies of the 5-way 1-shot and 5-shot tasks on the miniImageNet dataset, with 95% confidence intervals." ], "reasoning": "The table shows that DN4 has an accuracy of 71.02% in the 5-way 5-shot task with Conv, while our model has an accuracy of 73.22%. The improvement is 73.22% - 71.02% = 2.2%.", "question": "What is the accuracy improvement of our model over DN4 in the 5-way 5-shot task when using Conv as the embedding module?", "answer": { "answer": "2.2%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur model achieves the highest accuracy in the 5-way 1-shot task using Conv as the embedding module.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/279.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The mean accuracies of the 5-way 1-shot and 5-shot tasks on the miniImageNet dataset, with 95% confidence intervals." ], "reasoning": "The table shows that SalNet has the highest accuracy in the 5-way 1-shot task with Conv at 57.45%, while our model has an accuracy of 53.30%. Therefore, the statement is refuted.", "statement": "Our model achieves the highest accuracy in the 5-way 1-shot task using Conv as the embedding module.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: The mean accuracies of the 5-way 1-shot and 5-shot accuracies (%) on three fined-grained datasets using the model trained on miniImageNet, with 95% confidence intervals. All the experiments are conducted with the same network for fair comparison.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/280.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The mean accuracies of the 5-way 1-shot and 5-shot accuracies (%) on three fined-grained datasets using the model trained on miniImageNet, with 95% confidence intervals. All the experiments are conducted with the same network for fair comparison." ], "reasoning": "Our method outperforms other methods on Stanford Dogs and CUB-200 datasets in both 5-way 1-shot and 5-shot tasks, and shows significant improvement on Stanford Cars using the Resnet embedding module.", "description": "As table 2 shown, compared with those methods, our method achieves the best performance on the Stanford Dogs and CUB-200 under both the 5-way 1-shot and 5-shot task. Besides, when evaluating on Stanford Cars, the Resnet embedding module gains 26.25% and 11.66% improvement over Conv embedding module, which is higher than the other two datasets (gaining 1.95% and 6.26% improvement on Stanford Dogs, 4.90% and 3.67% improvement on CUB-200). Such uncompetitive performance of Conv embedding is caused by two reasons: (i) the Resnet module can obtain more detail and more discriminate feature with the very limited samples than the Conv module which is a shallow network. (ii) the GradCAM can not generate very well initial object seeds over Stanford Cars." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: The mean accuracies of the 5-way 1-shot and 5-shot accuracies (%) on three fined-grained datasets using the model trained on miniImageNet, with 95% confidence intervals. All the experiments are conducted with the same network for fair comparison.'\nProblem: 'What is the performance improvement of the Resnet embedding module over the Conv embedding module on the Stanford Cars dataset in the 5-shot task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/280.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The mean accuracies of the 5-way 1-shot and 5-shot accuracies (%) on three fined-grained datasets using the model trained on miniImageNet, with 95% confidence intervals. All the experiments are conducted with the same network for fair comparison." ], "reasoning": "The description states that the Resnet embedding module gains 26.25% improvement over the Conv embedding module on Stanford Cars. The table shows the performance of our method on Stanford Cars in the 5-shot task as 50.10±0.69.", "question": "What is the performance improvement of the Resnet embedding module over the Conv embedding module on the Stanford Cars dataset in the 5-shot task?", "answer": "{\"answer\": \"26.25%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur method achieves the best performance on the Stanford Dogs dataset in the 5-shot task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/280.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The mean accuracies of the 5-way 1-shot and 5-shot accuracies (%) on three fined-grained datasets using the model trained on miniImageNet, with 95% confidence intervals. All the experiments are conducted with the same network for fair comparison." ], "reasoning": "The table shows that our method achieves a score of 61.50±0.73 on the Stanford Dogs dataset in the 5-shot task, which is higher than the scores of Proto Net, Relation Net, and K-tuplet loss.", "statement": "Our method achieves the best performance on the Stanford Dogs dataset in the 5-shot task.", "answer": "{\"answer\": \"not enough info\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Comparison of false alarm rate on normal videos of UCF-Crime testing dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/281.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Comparison of false alarm rate on normal videos of UCF-Crime testing dataset." ], "reasoning": "The proposed method with the new ranking loss function achieves the lowest false alarm rate on normal videos compared to other algorithms, demonstrating its effectiveness in abnormal activity detection.", "description": "Table II shows the comparison of false alarm rate at the threshold of 50% of different algorithms. These false alarm rates have been calculated on the normal videos of the UCF-Crime testing dataset.. As shown in Table II, our proposed method gives the lowest false alarm rate on normal videos as compare to other algorithms. Additionally, the effectiveness of using new proposed ranking loss function is also shown in the Table II.. This comparison shows that using both abnormal and normal videos in training the network improves the abnormal activity detection task and we can generalize our method for other real world related abnormal activity detection.. Therefore, both the tables II and III, indicate that we have reduced both cases of false alarm with the help of our proposed method." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Comparison of false alarm rate on normal videos of UCF-Crime testing dataset.'\nProblem: 'Which algorithm has the lowest false alarm rate on normal videos?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/281.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Comparison of false alarm rate on normal videos of UCF-Crime testing dataset." ], "reasoning": "By comparing the false alarm rates in the table, the proposed method with the new ranking loss function has the lowest rate of 0.80.", "question": "Which algorithm has the lowest false alarm rate on normal videos?", "answer": "So, the answer is {\"answer\": \"Proposed Method(3D ResNet+constr.+new rank.loss)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed method with the new ranking loss function has a false alarm rate of 0.83 on normal videos.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/281.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Comparison of false alarm rate on normal videos of UCF-Crime testing dataset." ], "reasoning": "The table shows that the proposed method with the new ranking loss function has a false alarm rate of 0.80, not 0.83.", "statement": "The proposed method with the new ranking loss function has a false alarm rate of 0.83 on normal videos.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: The mean accuracies of the 5-way 1-shot and 5-shot tasks on three fine-grained datasets, with 95% confidence intervals.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/282.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The mean accuracies of the 5-way 1-shot and 5-shot tasks on three fine-grained datasets, with 95% confidence intervals." ], "reasoning": "Our model achieves the highest accuracy across all three datasets, outperforming Proto Net, Relation Net, and K-tuplet loss.", "description": "The experiment results show that our model outperforms previous work(Proto Net, Relation Net, and K-tuplet loss [Li et al., 2019c]) on the three novel datasets, which demonstrates the superior generalization capacity of our approach." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: The mean accuracies of the 5-way 1-shot and 5-shot tasks on three fine-grained datasets, with 95% confidence intervals.'\nProblem: 'What is the 5-way accuracy for our model with Conv embedding on the Stanford Cars dataset for 5-shot?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/282.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The mean accuracies of the 5-way 1-shot and 5-shot tasks on three fine-grained datasets, with 95% confidence intervals." ], "reasoning": "To find the 5-way accuracy for our model with Conv embedding on the Stanford Cars dataset for 5-shot, locate the row labeled 'Ours' with 'Conv' embedding and find the value under the '5-Way accuracy(%) Stanford Cars' column for 5-shot.", "question": "What is the 5-way accuracy for our model with Conv embedding on the Stanford Cars dataset for 5-shot?", "answer": "So, the answer is {\"answer\": \"82.28±1.02\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur model with Resnet embedding achieves over 70% accuracy on the Stanford Dogs dataset for 5-shot.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/282.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The mean accuracies of the 5-way 1-shot and 5-shot tasks on three fine-grained datasets, with 95% confidence intervals." ], "reasoning": "To verify this statement, locate the row labeled 'Ours' with 'Resnet' embedding and check the value under the '5-Way accuracy(%) Stanford Dogs' column for 5-shot. The value is 73.63±0.92, which is over 70%.", "statement": "Our model with Resnet embedding achieves over 70% accuracy on the Stanford Dogs dataset for 5-shot.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: The performance evaluation of the RBM-NQS-CS and RBM-NQS-I for one-dimensional system in Ising model where the system size is 128 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/283.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The performance evaluation of the RBM-NQS-CS and RBM-NQS-I for one-dimensional system in Ising model where the system size is 128 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "In the deep paramagnetic phase (J/|h| = 0.0), both the energy and the order parameter for RBM-NQS-CS and RBM-NQS-I without training are very close to the tensor network method results.", "description": "Table 1, Table 2 and Table 3 show the evaluation of the RBM-NQS-CS and RBM-NQS-I where the size of the system is 128 and parameter of the system J/|h| = 0.0, J/|h| = 3.0 and J/|h| = −3.0, respectively.. In the case of a deep paramagnetic phase (J/|h| = 0.0 in Table 1), we observe that both the energy and the order parameter for both the RBM-NQS-CS and RBM-NQS-I without training are very close to the result of the tensor network method." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: The performance evaluation of the RBM-NQS-CS and RBM-NQS-I for one-dimensional system in Ising model where the system size is 128 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation.'\nProblem: 'What is the energy value for the RBM-NQS-CS without training and how does it compare to the tensor network method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/283.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The performance evaluation of the RBM-NQS-CS and RBM-NQS-I for one-dimensional system in Ising model where the system size is 128 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "The energy value for RBM-NQS-CS without training is 127.9799.", "question": "What is the energy value for the RBM-NQS-CS without training?", "answer": "So, the answer is {\"answer\": \"127.9799\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe energy value for RBM-NQS-I with training is higher than the tensor network method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/283.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The performance evaluation of the RBM-NQS-CS and RBM-NQS-I for one-dimensional system in Ising model where the system size is 128 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "The energy value for RBM-NQS-I with training is 127.9799, which is slightly lower than the tensor network method result of 128.0000.", "statement": "The energy value for RBM-NQS-I with training is higher than the tensor network method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for one-dimensional system in Ising model where the system size is 128 and parameter of the system J/|h|=3.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/284.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for one-dimensional system in Ising model where the system size is 128 and parameter of the system J/|h|=3.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "RBM-NQS-I achieves results closer to the tensor network method and requires fewer iterations to converge compared to RBM-NQS-CS in both ferromagnetic and antiferromagnetic phases.", "description": "In the case of a deep ferromagnetic phase (J/|h| = 3.0 in Table 2) and antiferromagnetic phase (J/|h| = −3.0 in Table 3), we observe that the results of the RBM-NQS-I are closer to the result of the tensor network method and need less iterations to converge to the stopping criterion than RBM-NQS-CS." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for one-dimensional system in Ising model where the system size is 128 and parameter of the system J/|h|=3.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation.'\nProblem: 'Which method requires fewer iterations to converge to the stopping criterion in the deep ferromagnetic phase?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/284.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for one-dimensional system in Ising model where the system size is 128 and parameter of the system J/|h|=3.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "The table shows that RBM-NQS-I requires 20.8500 iterations to converge, while RBM-NQS-CS requires 1621.7250 iterations.", "question": "Which method requires fewer iterations to converge to the stopping criterion in the deep ferromagnetic phase?", "answer": "So, the answer is {\"answer\": \"RBM-NQS-I\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRBM-NQS-I requires fewer iterations to converge than RBM-NQS-CS in the antiferromagnetic phase.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/284.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for one-dimensional system in Ising model where the system size is 128 and parameter of the system J/|h|=3.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "The table shows that RBM-NQS-I requires 20.8500 iterations to converge, while RBM-NQS-CS requires 1621.7250 iterations.", "statement": "RBM-NQS-I requires fewer iterations to converge than RBM-NQS-CS in the antiferromagnetic phase.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for two-dimensional system in Ising model where the system size is 4×4 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/285.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for two-dimensional system in Ising model where the system size is 4×4 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "Tables 6, 7, and 8 in Appendix A evaluate RBM-NQS-CS and RBM-NQS-I for a 4x4 system with J/|h| values of 0.0, 3.0, and -3.0, respectively. In the deep paramagnetic phase (J/|h| = 0.0), results are close to the exact diagonalization method.", "description": "Table 6, Table 7 and Table 8 in Appendix A show the evaluation of the RBM-NQS-CS and RBM-NQS-I for two-dimensional system where the size of the system is 4 × 4 and parameter of the system J/|h| = 0.0, J/|h| = 3.0 and J/|h| = −3.0, respectively.. In the case of a deep paramagnetic phase (J/|h| = 0.0 in Table 6 in Appendix A and Table 9 in Appendix B ), the results of the RBMNQS-CS and RBM-NQS-I are very close to the result of the exact diagonalization method.. Table 6, Table 7 and Table 8 shows the evaluation of the RBM-NQS-CS and RBM-NQS-I for two-dimensional system where the size of the system is 4 × 4 and parameter of the system J/|h| = 0.0, J/|h| = 3.0 and J/|h| = −3.0, respectively." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for two-dimensional system in Ising model where the system size is 4×4 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation.'\nProblem: 'What is the energy value for the RBM-NQS-CS without training?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/285.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for two-dimensional system in Ising model where the system size is 4×4 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "The table shows the energy value for the RBM-NQS-CS without training as 16.0000 (0.0001).", "question": "What is the energy value for the RBM-NQS-CS without training?", "answer": "So, the answer is {\"answer\": \"16.0000\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe M2 F value for the RBM-NQS-I with training is 0.0624.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/285.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for two-dimensional system in Ising model where the system size is 4×4 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "The table shows that the M2 F value for the RBM-NQS-I with training is indeed 0.0624 (0.0007).", "statement": "The M2 F value for the RBM-NQS-I with training is 0.0624.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 9: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for three-dimensional system in Ising model where the system size is 2×2×2 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/286.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for three-dimensional system in Ising model where the system size is 2×2×2 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "Tables 9, 10, and 11 evaluate the RBM-NQS-CS and RBM-NQS-I for a three-dimensional system with different J/|h| parameters.", "description": "Ta. Table 9, Table 10 and Table 11 shows the evaluation of the RBM-NQS-CS and RBM-NQS-I for three-dimensional system where the size of the system is 2 × 2 × 2 and parameter of the system J/|h| = 0.0, J/|h| = 3.0 and J/|h| = −3.0, respectively." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 9: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for three-dimensional system in Ising model where the system size is 2×2×2 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation.'\nProblem: 'What is the energy value for the RBM-NQS-CS without training?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/286.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for three-dimensional system in Ising model where the system size is 2×2×2 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "The energy value for the RBM-NQS-CS without training is found in the first row under the 'Without training RBM-NQS-CS' column.", "question": "What is the energy value for the RBM-NQS-CS without training?", "answer": "So, the answer is {\"answer\": \"-8.0000\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe M2 F value for the RBM-NQS-I with training is 0.1244.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/286.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for three-dimensional system in Ising model where the system size is 2×2×2 and parameter of the system J/|h|=0.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "The M2 F value for the RBM-NQS-I with training is found in the second row under the 'With training RBM-NQS-I' column, which is 0.1244.", "statement": "The M2 F value for the RBM-NQS-I with training is 0.1244.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for two-dimensional system in Ising model where the system size is 4×4 and parameter of the system J/|h|=3.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/287.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for two-dimensional system in Ising model where the system size is 4×4 and parameter of the system J/|h|=3.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "The results of RBM-NQS-I are closer to the exact diagonalization calculations than RBM-NQS-CS in both deep ferromagnetic and antiferromagnetic phases before training.", "description": "In the case of a deep ferromagnetic phase (J/|h| = 3.0 in Table 7 in Appendix A and Table 10 in Appendix B) and antiferromagnetic phase (J/|h| = −3.0 in Table 8 in Appendix A and Table 11 in Appendix B), we see that the results of RBM-NQS-I are closer to those of the exact diagonalization calculations than RBM-NQS-CS before training." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for two-dimensional system in Ising model where the system size is 4×4 and parameter of the system J/|h|=3.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation.'\nProblem: 'Which method's energy result is closest to the exact diagonalization calculation after training?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/287.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for two-dimensional system in Ising model where the system size is 4×4 and parameter of the system J/|h|=3.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "Compare the energy values of 'With training RBM-NQS-CS' and 'With training RBM-NQS-I' with the 'Exact Diagonalization' value. The energy value of 'With training RBM-NQS-I' is -72.9397, which is closer to the exact diagonalization value of -72.9455 than the 'With training RBM-NQS-CS' value of -72.9401.", "question": "Which method's energy result is closest to the exact diagonalization calculation after training?", "answer": "So, the answer is {\"answer\": \"With training RBM-NQS-I\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe M2 F value of RBM-NQS-I without training is closer to the exact diagonalization value than RBM-NQS-CS without training.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/287.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: The performance evaluation of RBM-NQS-CS and RBM-NQS-I for two-dimensional system in Ising model where the system size is 4×4 and parameter of the system J/|h|=3.0. The reported value is average value over 20 realisations. The value inside the parentheses is the standard deviation." ], "reasoning": "The M2 F value for 'Without training RBM-NQS-I' is 0.6064, and for 'Without training RBM-NQS-CS' is 0.0625. The exact diagonalization value is 0.9860. The value of 'Without training RBM-NQS-I' is closer to the exact diagonalization value than 'Without training RBM-NQS-CS'.", "statement": "The M2 F value of RBM-NQS-I without training is closer to the exact diagonalization value than RBM-NQS-CS without training.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: The value of the inflection point for one-, two- and three-dimensional systems of given sizes with RBM-NQS-CS, RBM-NQS-IT, tensor network and exact diagonalization method with ferromagnetic magnetisation M2F order parameter. The value inside the parentheses is the standard deviation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/288.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The value of the inflection point for one-, two- and three-dimensional systems of given sizes with RBM-NQS-CS, RBM-NQS-IT, tensor network and exact diagonalization method with ferromagnetic magnetisation M2F order parameter. The value inside the parentheses is the standard deviation." ], "reasoning": "RBM-NQS-CS performs the worst overall, especially in large systems, and is particularly unstable in one-dimensional systems with 64 and 128 particles.", "description": "Table 4 and Table 5 show the value of the inflection point for different sizes of the system of one-dimensional, two-dimensional and three-dimensional systems with RBM-NQS-CS, RBM-NQS-IT and tensor network method with ferromagnetic magnetisation M 2 F and antiferromagnetic magnetisation M 2 A order parameter, respectively.. In Table 4 and Table 5, we observe that RBM-NQS-CS performs the worst overall since the value of the inflection point is far from both the tensor network and exact diagonalization methods, especially in system of large size. It is particularly unstable in a onedimensional system with 64 and 128 particles, as shown by a very large standard deviation." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: The value of the inflection point for one-, two- and three-dimensional systems of given sizes with RBM-NQS-CS, RBM-NQS-IT, tensor network and exact diagonalization method with ferromagnetic magnetisation M2F order parameter. The value inside the parentheses is the standard deviation.'\nProblem: 'Which method has the highest standard deviation for a one-dimensional system with 128 particles?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/288.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The value of the inflection point for one-, two- and three-dimensional systems of given sizes with RBM-NQS-CS, RBM-NQS-IT, tensor network and exact diagonalization method with ferromagnetic magnetisation M2F order parameter. The value inside the parentheses is the standard deviation." ], "reasoning": "From the table, the standard deviation for RBM-NQS-CS in a one-dimensional system with 128 particles is 0.38, which is the highest among all methods listed.", "question": "Which method has the highest standard deviation for a one-dimensional system with 128 particles?", "answer": "So, the answer is {\"answer\": \"RBM-NQS-CS\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRBM-NQS-CS has a lower inflection point value than RBM-NQS-IT for a system size of 16.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/288.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The value of the inflection point for one-, two- and three-dimensional systems of given sizes with RBM-NQS-CS, RBM-NQS-IT, tensor network and exact diagonalization method with ferromagnetic magnetisation M2F order parameter. The value inside the parentheses is the standard deviation." ], "reasoning": "From the table, for a system size of 16, RBM-NQS-CS has an inflection point value of 1.007, while RBM-NQS-IT has a value of 1.040. Therefore, RBM-NQS-CS has a lower inflection point value.", "statement": "RBM-NQS-CS has a lower inflection point value than RBM-NQS-IT for a system size of 16.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Real and complex valued architecture comparison on the mackey-glass data.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/289.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Real and complex valued architecture comparison on the mackey-glass data." ], "reasoning": "Complex cells solve the task with fewer parameters but at a higher computational cost compared to real-valued cells.", "description": "We explore the effect of a complex valued cell in table 1. We apply the complex gru proposed in , and compare to a real valued gru. We speculate that complex cells are better suited to process fourier representations due to their complex nature. Our observations show that both cells solve the task, while the complex cell does so with fewer parameters at a higher cost." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Real and complex valued architecture comparison on the mackey-glass data.'\nProblem: 'Which network has the lowest mean squared error (mse) and how many weights does it have?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/289.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Real and complex valued architecture comparison on the mackey-glass data." ], "reasoning": "By comparing the mse values in the table, the cgRNN-64-fft network has the lowest mse of 3.4·10^-4. The corresponding weights for this network are 58k.", "question": "Which network has the lowest mean squared error (mse) and how many weights does it have?", "answer": "So, the answer is {\"answer\": \"58k\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe network with the fewest weights has the highest computational cost.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/289.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Real and complex valued architecture comparison on the mackey-glass data." ], "reasoning": "The network with the fewest weights is cgRNN-32-fft with 23k weights, and its computational cost is 275 minutes. This is the highest computational cost in the table.", "statement": "The network with the fewest weights has the highest computational cost.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: 60 day ahead power load prediction and ground truth. Power prediction results using various different architectures'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/290.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: 60 day ahead power load prediction and ground truth. Power prediction results using various different architectures" ], "reasoning": "Windowed methods and reduced Fourier coefficients improve prediction accuracy and reduce parameters compared to scalar-time domain approaches.", "description": "five days; all other parameters are left the same as the day-ahead prediction setting. Table 2 shows that the windowed methods are able to extract patterns, but the scalar-time domain approach failed. Additionally we observe that we do not require the full set of Fourier coefficients to construct useful predictions on the power-data set. The results are tabulated in table 2. It turns out that the lower quarter of Fourier coefficients is enough, which allowed us to reduce the number of parameters considerably." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: 60 day ahead power load prediction and ground truth. Power prediction results using various different architectures'\nProblem: 'Which network has the lowest mean squared error (mse) and how many weights does it use?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/290.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: 60 day ahead power load prediction and ground truth. Power prediction results using various different architectures" ], "reasoning": "By examining the table, the network with the lowest mse is 'fRNN-lowpass-1/4' with an mse of 7.6e^105 MW.", "question": "Which network has the lowest mean squared error (mse)?", "answer": "So, the answer is {\"answer\": \"fRNN-lowpass-1/4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe time-RNN-windowed network has a lower mean squared error (mse) than the fRNN network.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/290.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: 60 day ahead power load prediction and ground truth. Power prediction results using various different architectures" ], "reasoning": "The table shows that the time-RNN-windowed network has an mse of 8.8e^105 MW, while the fRNN network has an mse of 8.3e^105 MW. Therefore, the statement is refuted.", "statement": "The time-RNN-windowed network has a lower mean squared error (mse) than the fRNN network.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Mean Classification Accuracy (Percent)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/291.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Mean Classification Accuracy (Percent)" ], "reasoning": "Geom-GCN achieves state-of-the-art performance with the highest mean classification accuracy highlighted in the table.", "description": "Results are summarized in Table 3. The reported numbers denote the mean classification accuracy in percent. In general, Geom-GCN achieves state-of-the-art performance. The best performing method is highlighted." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Mean Classification Accuracy (Percent)'\nProblem: 'Which method achieves the highest mean classification accuracy for the Pubm. dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/291.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Mean Classification Accuracy (Percent)" ], "reasoning": "By examining the table, the highest mean classification accuracy for the Pubm. dataset is highlighted and corresponds to Geom-GCN-I with an accuracy of 90.05.", "question": "Which method achieves the highest mean classification accuracy for the Pubm. dataset?", "answer": "So, the answer is {\"answer\": \"Geom-GCN-I\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGeom-GCN-P achieves the highest mean classification accuracy for the Texa. dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/291.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Mean Classification Accuracy (Percent)" ], "reasoning": "By examining the table, the highest mean classification accuracy for the Texa. dataset is highlighted and corresponds to Geom-GCN-P with an accuracy of 67.57.", "statement": "Geom-GCN-P achieves the highest mean classification accuracy for the Texa. dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Mean Classification Accuracy (Percent)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/292.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Mean Classification Accuracy (Percent)" ], "reasoning": "Table 4 shows that assortative graphs have larger β values than disassortative graphs, and neighborhoods in latent space contribute more in disassortative graphs.", "description": "The results are summarized in Table 4, where positive improvement is denoted by an up arrow ↑ and negative improvement by a down arrow ↓. The best performing method is highlighted.. nodes tend to connect together. From Table 4, one can see that assortative graphs (e.g., citation networks) have a much larger β than disassortative graphs (e.g., WebKB networks).. Table 4 exhibits three interesting patterns: i) Neighborhoods in graph and latent space both benefit the aggregation in most cases; ii) Neighborhoods in latent space have larger contributions in disassortative graphs (with a small β) than assortative ones, which implies relevant information from disconnected nodes is captured effectively by the neighborhoods in latent space; iii) To our surprise, several variants with only one neighborhood (in Table 4) achieve better performances than the variants with two neighborhoods (in Tabel 3)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Mean Classification Accuracy (Percent)'\nProblem: 'Which method has the highest score for the Cora dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/292.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Mean Classification Accuracy (Percent)" ], "reasoning": "To find the highest score for the Cora dataset, look at the Cora column and identify the maximum value. The highest score is 87.00, achieved by Geom-GCN-S-g.", "question": "Which method has the highest score for the Cora dataset?", "answer": "So, the answer is {\"answer\": \"Geom-GCN-S-g\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGeom-GCN-I-g has the highest score for the Cite dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/292.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Mean Classification Accuracy (Percent)" ], "reasoning": "To verify this statement, look at the Cite column and compare the scores. Geom-GCN-I-g has a score of 80.64, which is indeed the highest in the Cite column.", "statement": "Geom-GCN-I-g has the highest score for the Cite dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Percentage of correctly classified gestures for different variance thresholds tvar'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/293.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Percentage of correctly classified gestures for different variance thresholds tvar" ], "reasoning": "For tvar = 0.10, 77.4% of sequences were correctly classified without a8, while 63.8% were correctly classified with a8.", "description": "From the 168 sequences which were classified, 130 were classified correctly. This equates to approximately 77.4%.. 25 of 196 sequences were. classified correctly (63.8%).. Table 2 shows the classification performance for different values of tvar." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Percentage of correctly classified gestures for different variance thresholds tvar'\nProblem: 'What is the percentage of sequences correctly classified without a8 for tvar = 0.20?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/293.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Percentage of correctly classified gestures for different variance thresholds tvar" ], "reasoning": "To find the percentage of sequences correctly classified without a8 for tvar = 0.20, look at the row where tvar = 0.20 and find the value under the column 'Correctly classified without a8'.", "question": "What is the percentage of sequences correctly classified without a8 for tvar = 0.20?", "answer": "So, the answer is {\"answer\": \"76.2%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFor tvar = 0.15, the percentage of sequences correctly classified with a8 is higher than without a8.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/293.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Percentage of correctly classified gestures for different variance thresholds tvar" ], "reasoning": "For tvar = 0.15, the table shows that 74.4% of sequences were correctly classified without a8, while 67.9% were correctly classified with a8. Since 67.9% is less than 74.4%, the statement is false.", "statement": "For tvar = 0.15, the percentage of sequences correctly classified with a8 is higher than without a8.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Pros and cons of the Offboard velocity and position control'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/294.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Pros and cons of the Offboard velocity and position control" ], "reasoning": "Velocity control in Offboard mode offers better controllability and faster guidance but is more complex compared to position control.", "description": "Hence, the Offboard mode enables to bypass the autopilot for either the position or velocity setpoint, a trade-off (Table I) had to be done considering our application. • For the bypass of the velocity, the UAV is more controllable and the guidance is faster since the inner part of the controller is modified. However, more parameters need to be selected: the velocity and a shut-off parameter. • For the bypass of the position, the UAV is less controllable and the guidance is slower (outer part of the controller), but it is less complex since only one parameter needs to be controlled and then the autopilot does the rest.. A standard velocity is used when performing the Offboard mode position control. Since robust and fast guidance is needed for sense and avoid purposes, the velocity control is chosen in the Offboard mode in this paper." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Pros and cons of the Offboard velocity and position control'\nProblem: 'Why is velocity control chosen over position control in the Offboard mode for sense and avoid purposes?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/294.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Pros and cons of the Offboard velocity and position control" ], "reasoning": "The complexity for Velocity control is 2.", "question": "What is complexity for velocity control in the Offboard mode?", "answer": "So, the answer is {\"answer\": \"2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPosition control in Offboard mode is more complex than velocity control.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/294.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Pros and cons of the Offboard velocity and position control" ], "reasoning": "The table shows that position control has a complexity of 1, while velocity control has a complexity of 2, indicating that position control is less complex.", "statement": "Position control in Offboard mode is more complex than velocity control.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparison for different prototype numbers of TDAPNet on PASCAL3D+. We just list Level-3 and Mean here, the complete results are in the Supplementary Material.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/295.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison for different prototype numbers of TDAPNet on PASCAL3D+. We just list Level-3 and Mean here, the complete results are in the Supplementary Material." ], "reasoning": "The performance improves with multiple prototypes, with 4 prototypes outperforming 1 prototype, and 8 prototypes performing similarly to 4 prototypes.", "description": "Multiple prototypes improve the performance. As shown in Table 2, 4 prototypes outperform 1 prototype, while 8 prototypes lead to about the same performance as 4 prototypes." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparison for different prototype numbers of TDAPNet on PASCAL3D+. We just list Level-3 and Mean here, the complete results are in the Supplementary Material.'\nProblem: 'What is the mean performance score for 4 prototypes, 1 recurrence under occlusion?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/295.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison for different prototype numbers of TDAPNet on PASCAL3D+. We just list Level-3 and Mean here, the complete results are in the Supplementary Material." ], "reasoning": "To find the mean performance score for 4 prototypes, 1 recurrence under occlusion, look at the row labeled '4 prototype, 1 recurrence' and the column labeled 'PASCAL3D+ Classification under Occlusion Mean'.", "question": "What is the mean performance score for 4 prototypes, 1 recurrence under occlusion?", "answer": { "answer": "92.3" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\n8 prototypes, 2 recurrence has the highest mean performance score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/295.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison for different prototype numbers of TDAPNet on PASCAL3D+. We just list Level-3 and Mean here, the complete results are in the Supplementary Material." ], "reasoning": "To verify this, compare the mean performance scores in the 'PASCAL3D+ Classification under Occlusion Mean' column for all rows. The highest score is 92.4, which corresponds to '4 prototype, 2 recurrence'.", "statement": "8 prototypes, 2 recurrence has the highest mean performance score.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Reporting the mean PSNR and the mean SSIM with SRIE [7], LIME [9], and DeepFlash [3].'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/296.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Reporting the mean PSNR and the mean SSIM with SRIE [7], LIME [9], and DeepFlash [3]." ], "reasoning": "The encoder-decoder network was pre-trained on the ImageNet dataset, and the results show improvements in the flash image enhancement task, with the highest PSNR and SSIM values compared to other methods.", "description": "We use the PSNR (Peak Signal-to-Noise Ratio) and the SSIM (Structural Similarity) to measure the performance of our quantitative results. Table 1 reports the mean PSNR and the mean SSIM on the test set, for 1000 epochs. All hyperparameters are setting on the same way for (Capece et al., 2019), and the encoderdecoder network was pre-trained on the ImageNet dataset (Deng et al., 2009) instead on a model used for face recognition (Parkhi et al., 2015). Our quantitative results do not significantly outperform the stateof-the-art image enhancement methods, but at least shows improvements on the flash image enhancement task." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Reporting the mean PSNR and the mean SSIM with SRIE [7], LIME [9], and DeepFlash [3].'\nProblem: 'Which method achieved the highest PSNR and SSIM values?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/296.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Reporting the mean PSNR and the mean SSIM with SRIE [7], LIME [9], and DeepFlash [3]." ], "reasoning": "By comparing the PSNR and SSIM values in the table, the 'Ours' method achieved the highest PSNR of 15.67 and the highest SSIM of 0.684.", "question": "Which method achieved the highest PSNR and SSIM values?", "answer": "So, the answer is {\"answer\": \"Ours\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe SRIE method achieved the highest SSIM value.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/296.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Reporting the mean PSNR and the mean SSIM with SRIE [7], LIME [9], and DeepFlash [3]." ], "reasoning": "The table shows that the SRIE method has an SSIM value of 0.659, which is lower than the SSIM values of DeepFlash (0.671) and Ours (0.684).", "statement": "The SRIE method achieved the highest SSIM value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: R2 scores on the training and test folds of different datasets for ridge regression (RR), support vector regression (SVR), random forests (RF), and the autofeat regression model with one, two, or three feature engineering steps (AFR1-3). Best results per column are in boldface (existing methods) and underlined (AFR).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/297.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: R2 scores on the training and test folds of different datasets for ridge regression (RR), support vector regression (SVR), random forests (RF), and the autofeat regression model with one, two, or three feature engineering steps (AFR1-3). Best results per column are in boldface (existing methods) and underlined (AFR)." ], "reasoning": "The AutoFeatRegressor model outperforms standard linear ridge regression while maintaining interpretability, although it does not reach the performance of a random forest regression model.", "description": "While on most datasets, the AutoFeatRegressor model does not quite reach the stateof-the-art performance of a random forest regression model (Table 2), it clearly outperforms standard linear ridge regression, while retaining its interpretability." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: R2 scores on the training and test folds of different datasets for ridge regression (RR), support vector regression (SVR), random forests (RF), and the autofeat regression model with one, two, or three feature engineering steps (AFR1-3). Best results per column are in boldface (existing methods) and underlined (AFR).'\nProblem: 'Which model has the highest performance on the airfoil test dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/297.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: R2 scores on the training and test folds of different datasets for ridge regression (RR), support vector regression (SVR), random forests (RF), and the autofeat regression model with one, two, or three feature engineering steps (AFR1-3). Best results per column are in boldface (existing methods) and underlined (AFR)." ], "reasoning": "To find the model with the highest performance on the airfoil test dataset, compare the values in the 'airfoil test' column. The highest value is 0.934, which corresponds to the RF model.", "question": "Which model has the highest performance on the airfoil test dataset?", "answer": "So, the answer is {\"answer\": \"RF\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe AutoFeatRegressor model outperforms the random forest regression model on the concrete test dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/297.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: R2 scores on the training and test folds of different datasets for ridge regression (RR), support vector regression (SVR), random forests (RF), and the autofeat regression model with one, two, or three feature engineering steps (AFR1-3). Best results per column are in boldface (existing methods) and underlined (AFR)." ], "reasoning": "To verify this statement, compare the performance of the AutoFeatRegressor models (AFR1, AFR2, AFR3) with the random forest regression model (RF) on the concrete test dataset. The RF model has a performance of 0.892, while the highest performance among the AutoFeatRegressor models is 0.818 (AFR1), which is lower than 0.892.", "statement": "The AutoFeatRegressor model outperforms the random forest regression model on the concrete test dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Number of engineered (eng) and selected (sel) additional features for each dataset from an autofeat regression model with one, two, or three feature engineering steps (AFR1-3).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/298.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Number of engineered (eng) and selected (sel) additional features for each dataset from an autofeat regression model with one, two, or three feature engineering steps (AFR1-3)." ], "reasoning": "Autofeat generated between 2 and 11 additional features with one step, and on average 31 additional features with two or three steps across all datasets.", "description": "Across all datasets, with one feature engineering step, autofeat generated between 2 and 11 additional features, while with two and three steps, it produced on average 31 additional features (Table 3)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Number of engineered (eng) and selected (sel) additional features for each dataset from an autofeat regression model with one, two, or three feature engineering steps (AFR1-3).'\nProblem: 'How many additional features were generated for the wine quality dataset with one feature engineering step?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/298.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Number of engineered (eng) and selected (sel) additional features for each dataset from an autofeat regression model with one, two, or three feature engineering steps (AFR1-3)." ], "reasoning": "The table shows that for the wine quality dataset, the number of additional features generated with one feature engineering step (AFR1) is 59.", "question": "How many additional features were generated for the wine quality dataset with one feature engineering step?", "answer": "So, the answer is {\"answer\": \"59\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAutofeat generated 530 additional features for the airfoil dataset with two feature engineering steps.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/298.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Number of engineered (eng) and selected (sel) additional features for each dataset from an autofeat regression model with one, two, or three feature engineering steps (AFR1-3)." ], "reasoning": "The table shows that for the airfoil dataset, the number of additional features generated with two feature engineering steps (AFR2) is 530.", "statement": "Autofeat generated 530 additional features for the airfoil dataset with two feature engineering steps.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Rank-1 recognition accuracies on the Carl dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/299.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Rank-1 recognition accuracies on the Carl dataset" ], "reasoning": "The proposed method outperforms the state-of-the-art models, especially when preprocessing, up convolution, and alignment are used, leading to the highest accuracies across all datasets.", "description": "Table 1, 2, and 3 present the averaged rank-1 recognition scores on the Carl, UND-X1, and EURECOM datasets, respectively. In the tables, we also include the accuracies of the stateof-the-art methods for comparison purposes. For the Carl and UND-X1 datasets, state-of-the-art models are Deep Perceptual Mapping and Hu et al. . On EURECOM dataset, only presented a study for the thermal to visible recognition. The authors propose an image synthesis method by using cascaded refinement network. However, their benchmark setup is different to visible face recognition studies. than ours and earlier thermal. The highest accuracies are reached when preprocessing, up convolution, and alignment are used. As can be seen in Table I, the proposed method is superior to the DPM, when two visible images per subject and all available visible images per subject are used in the gallery.. From Table I, it can also be observed that alignment improves the performance by around 2%.. Up convolution is found to be more useful compared to upsampling leading to increased accuracies on all there datasets. Similarly, applying preprocessing has enhanced the performance further on all three datasets, which indicates its importance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Rank-1 recognition accuracies on the Carl dataset'\nProblem: 'What is the accuracy of the Model with Up Convolution + DoG Filter (Aligned) when using all available visible images per subject?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/299.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Rank-1 recognition accuracies on the Carl dataset" ], "reasoning": "From the table, locate the row for 'Model with Up Convolution + DoG Filter (Aligned)' and find the value under the column '#Visible Image Gallery/Subject all/subject'.", "question": "What is the accuracy of the Model with Up Convolution + DoG Filter (Aligned) when using all available visible images per subject?", "answer": "So, the answer is {\"answer\": \"85%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Model with Up Convolution + DoG Filter (Aligned) achieves the highest accuracy when using two visible images per subject.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/299.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Rank-1 recognition accuracies on the Carl dataset" ], "reasoning": "From the table, compare the accuracies of all models under the column '#Visible Image Gallery/Subject 2/subject'. The Model with Up Convolution + DoG Filter (Aligned) has the highest accuracy of 60.25%.", "statement": "The Model with Up Convolution + DoG Filter (Aligned) achieves the highest accuracy when using two visible images per subject.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparison to prior work. Long+16 results are directly from Long et al. (2016). Hyperparameters are chosen by best performance on validation set (see Appendix A).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/0.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison to prior work. Long+16 results are directly from Long et al. (2016). Hyperparameters are chosen by best performance on validation set (see Appendix A)." ], "reasoning": "RANDOMER achieves the highest scores across all domains, especially excelling in the SCENE domain, while REINFORCE performs well on TANGRAMS but poorly on SCENE.", "description": "Table 2 compares RANDOMER to results from Long et al. (2016) as well as two baselines, REINFORCE and BSMML. Our approach achieves new state-of-the-art results by a significant margin, especially on the SCENE domain,. We report the results for REINFORCE, BS-MML, and RANDOMER on the seed and hyperparameters that achieve the best validation accuracy.. We note that REINFORCE performs very well on TANGRAMS but worse on ALCHEMY and very poorly on SCENE." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparison to prior work. Long+16 results are directly from Long et al. (2016). Hyperparameters are chosen by best performance on validation set (see Appendix A).'\nProblem: 'Which system achieves the highest score on the SCENE domain with 3 utterances?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/0.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison to prior work. Long+16 results are directly from Long et al. (2016). Hyperparameters are chosen by best performance on validation set (see Appendix A)." ], "reasoning": "By comparing the scores for the SCENE domain with 3 utterances, RANDOMER achieves the highest score of 64.8.", "question": "Which system achieves the highest score on the SCENE domain with 3 utterances?", "answer": { "answer": "RandoMer" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nREINFORCE performs the best on the TANGRAMS domain with 3 utterances.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/0.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison to prior work. Long+16 results are directly from Long et al. (2016). Hyperparameters are chosen by best performance on validation set (see Appendix A)." ], "reasoning": "By comparing the scores for the TANGRAMS domain with 3 utterances, REINFORCE achieves the highest score of 68.5.", "statement": "REINFORCE performs the best on the TANGRAMS domain with 3 utterances.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Randomized beam search. All listed models use gradient weight qMML and Tokens to represent execution history.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/1.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Randomized beam search. All listed models use gradient weight qMML and Tokens to represent execution history." ], "reasoning": "The table shows that (cid:15)-greedy randomized beam search consistently outperforms classic beam search, even when the beam size of classic beam search is increased to 128.", "description": "Table 3 shows that (cid:15)-greedy randomized beam search consistently outperforms classic beam search. Even when we increase the beam size of classic beam. search to 128, it still does not surpass randomized beam search with a beam of 32, and further increases yield no additional improvement." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Randomized beam search. All listed models use gradient weight qMML and Tokens to represent execution history.'\nProblem: 'What is the highest score achieved by randomized beam search with a beam size of 32 in the Scene 3utts task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/1.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Randomized beam search. All listed models use gradient weight qMML and Tokens to represent execution history." ], "reasoning": "To find the highest score achieved by randomized beam search with a beam size of 32 in the Scene 3utts task, look at the row for randomized beam search with a beam size of 32 and find the highest value in the Scene 3utts column.", "question": "What is the highest score achieved by randomized beam search with a beam size of 32 in the Scene 3utts task?", "answer": "So, the answer is {\"answer\": \"54.1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRandomized beam search with a beam size of 32 and (cid:15)=0.15 achieves a higher score in the Tangrams 3utts task than classic beam search with a beam size of 128.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/1.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Randomized beam search. All listed models use gradient weight qMML and Tokens to represent execution history." ], "reasoning": "The table shows that randomized beam search with a beam size of 32 and (cid:15)=0.15 achieves a score of 65.2 in the Tangrams 3utts task, while classic beam search with a beam size of 128 achieves a score of 60.9.", "statement": "Randomized beam search with a beam size of 32 and (cid:15)=0.15 achieves a higher score in the Tangrams 3utts task than classic beam search with a beam size of 128.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: β-meritocratic updates. All listed models use randomized beam search, ϵ=0.15 and Tokens to represent execution history.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/2.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: β-meritocratic updates. All listed models use randomized beam search, ϵ=0.15 and Tokens to represent execution history." ], "reasoning": "More uniform upweighting across reward-earning programs improves accuracy and reduces spurious programs, especially in SCENE, but no single β value is best for all domains.", "description": "Table 4 evaluates the impact of β-meritocratic parameter updates (gradient weight qβ). More uniform upweighting across reward-earning programs leads to higher accuracy and fewer spurious programs, especially in SCENE. However, no single value of β performs best over all domains." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: β-meritocratic updates. All listed models use randomized beam search, ϵ=0.15 and Tokens to represent execution history.'\nProblem: 'Which β value leads to the highest accuracy in the SCENE domain?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/2.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: β-meritocratic updates. All listed models use randomized beam search, ϵ=0.15 and Tokens to represent execution history." ], "reasoning": "To find the highest accuracy in the SCENE domain, compare the values under SCENE 3utts and SCENE 5utts for different β values. The highest accuracy is 61.0 for qβ=0 in SCENE 3utts.", "question": "Which β value leads to the highest accuracy in the SCENE domain?", "answer": { "answer": "qβ=0" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe qβ=0.25 parameter update leads to the highest accuracy in the Tangrams 3utts domain.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/2.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: β-meritocratic updates. All listed models use randomized beam search, ϵ=0.15 and Tokens to represent execution history." ], "reasoning": "To verify this statement, compare the accuracy values for Tangrams 3utts across different β values. The highest accuracy is 65.2 for qMML (qβ=1), not qβ=0.25.", "statement": "The qβ=0.25 parameter update leads to the highest accuracy in the Tangrams 3utts domain.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Cycle-of-Learning hyperparemeters for each environment: (a) LunarLanderContinuous-v2 and (b) Microsoft AirSim.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/3.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Cycle-of-Learning hyperparemeters for each environment: (a) LunarLanderContinuous-v2 and (b) Microsoft AirSim." ], "reasoning": "The table summarizes the hyperparameters used in CoL for the LunarLanderContinuous-v2 and Microsoft AirSim experiments, detailing values for both environments (a) and (b).", "description": "rel The hyperparameters used in CoL for each environment are described in the Supplementary Material.. mentation to improve reproducibility of the research work. Algorithm 1 describes all steps of the proposed method and Table 2 summarize all CoL hyperparameters used on the LunarLanderContinuous-v2 and Microsoft AirSim experiments." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Cycle-of-Learning hyperparemeters for each environment: (a) LunarLanderContinuous-v2 and (b) Microsoft AirSim.'\nProblem: 'What is the actor learning rate for both environments?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/3.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Cycle-of-Learning hyperparemeters for each environment: (a) LunarLanderContinuous-v2 and (b) Microsoft AirSim." ], "reasoning": "The table lists the actor learning rate for both environments (a) and (b) as 1.0 e−3.", "question": "What is the actor learning rate for both environments?", "answer": "So, the answer is {\"answer\": \"1.0 e−3\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe batch size used in both environments is 512.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/3.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Cycle-of-Learning hyperparemeters for each environment: (a) LunarLanderContinuous-v2 and (b) Microsoft AirSim." ], "reasoning": "The table shows that the batch size for both environments (a) and (b) is indeed 512.", "statement": "The batch size used in both environments is 512.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Error rates (%) on SVHN using CNN-13 architecture. We ran three trials for ICT.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/4.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Error rates (%) on SVHN using CNN-13 architecture. We ran three trials for ICT." ], "reasoning": "ICT achieves competitive test errors compared to other state-of-the-art SSL methods on the SVHN dataset.", "description": "For SVHN, the test errors obtained by ICT are competitive with other state-of-the-art SSL methods (Table 2)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Error rates (%) on SVHN using CNN-13 architecture. We ran three trials for ICT.'\nProblem: 'What is the test error of ICT with 500 labeled and 73257 unlabeled data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/4.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Error rates (%) on SVHN using CNN-13 architecture. We ran three trials for ICT." ], "reasoning": "To find the test error of ICT with 500 labeled and 73257 unlabeled data, locate the ICT row and the column for 500 labeled and 73257 unlabeled data.", "question": "What is the test error of ICT with 500 labeled and 73257 unlabeled data?", "answer": "So, the answer is {\"answer\": \"4.23±0.15\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nICT has a lower test error than SNTG with 1000 labeled data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/4.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Error rates (%) on SVHN using CNN-13 architecture. We ran three trials for ICT." ], "reasoning": "Compare the test errors of ICT and SNTG in the column for 1000 labeled data. ICT has a test error of 3.89±0.04, while SNTG has a test error of 3.86±0.27.", "statement": "ICT has a lower test error than SNTG with 1000 labeled data.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Test Accuracy of adversarial examples on MNIST dataset (%)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/5.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test Accuracy of adversarial examples on MNIST dataset (%)" ], "reasoning": "Our proposed gradient regularization method maintains high accuracy under various attack strengths, unlike cross entropy gradient regularization which drops significantly as attack strength increases.", "description": "On MNIST dataset, our proposed gradient regularization method can achieve ∼ 90% accuracy under all considered attacks within (cid:96)∞ = 0.1 constraints. Cross entropy gradient regularization (Ross & Doshi-Velez (2018)) achieves similar robustness as ours within (cid:96)∞ = 0.1, but their robustness performance drops very fast when adversarial attacking strengths increases, e.g. under (cid:96)∞ = 0.2, 0.3 attacks. The reason is that the softmax and cross entropy operation introduces unnecessary nonlinearity, in which case the gradients from cross entropy loss is already very small.. Final results are shown in Table. 1 and 2." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Test Accuracy of adversarial examples on MNIST dataset (%)'\nProblem: 'What is the accuracy of the 'Ours' model under FGSM 0.3 attack?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/5.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test Accuracy of adversarial examples on MNIST dataset (%)" ], "reasoning": "To find the accuracy of the 'Ours' model under FGSM 0.3 attack, locate the 'Ours' row and the FGSM 0.3 column in the table.", "question": "What is the accuracy of the 'Ours' model under FGSM 0.3 attack?", "answer": "So, the answer is {\"answer\": \"41.6\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'Ours' model has higher accuracy than the 'CrossEntropy' model under BIM 0.3 attack.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/5.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test Accuracy of adversarial examples on MNIST dataset (%)" ], "reasoning": "Compare the accuracy values of the 'Ours' model and the 'CrossEntropy' model under BIM 0.3 attack. The 'Ours' model has an accuracy of 26.7, while the 'CrossEntropy' model has an accuracy of 19.9.", "statement": "The 'Ours' model has higher accuracy than the 'CrossEntropy' model under BIM 0.3 attack.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Test Accuracy of adversarial examples on CIFAR10 dataset (%)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/6.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Test Accuracy of adversarial examples on CIFAR10 dataset (%)" ], "reasoning": "Our method achieves comparable robustness to state-of-the-art MinMax training under (cid:96)∞ = 3 constraints without needing adversarial examples, but its effectiveness decreases as (cid:96)∞ increases.", "description": "Improving robustness on CIFAR10 dataset is much harder than MNIST. State-of-the-art MinMax training achieves ∼ 40% accuracy under strongest attacks in considered settings. But this method highly relies on huge amounts of adversarial data augmentation methods, which takes over 10 times of overhead during training process. By contrast, our method doesn't need adversarial example. Final results are shown in Table. 1 and 2.. generation and can achieves comparable robustness under (cid:96)∞ = 3 constraints. We test the average time of each epoch for both natural training and gradient regularized training. Our time consumption is average 2.1 times than natural training per epoch. Notice that the robustness enhancement of our method becomes lower when (cid:96)∞ becomes larger. This shows one limitation of gradient regularization methods: Our gradient regularization approach is based on Taylor approximation in a small neighborhood. When the adversarial examples exceeds the reasonable approximating range, the gradient regularization effect also exhausts." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Test Accuracy of adversarial examples on CIFAR10 dataset (%)'\nProblem: 'Which model achieves the highest accuracy under FGSM 6 attack?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/6.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Test Accuracy of adversarial examples on CIFAR10 dataset (%)" ], "reasoning": "To find the model with the highest accuracy under FGSM 6 attack, we look at the column labeled 'FGSM 6' and identify the highest value. The highest value is 65.8, which corresponds to the MinMax* model.", "question": "Which model achieves the highest accuracy under FGSM 6 attack?", "answer": "So, the answer is {\"answer\": \"MinMax*\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Ours model achieves higher accuracy than the Natural Model under BIM 6 attack.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/6.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Test Accuracy of adversarial examples on CIFAR10 dataset (%)" ], "reasoning": "To verify this statement, we compare the accuracy of the Ours model and the Natural Model under the BIM 6 attack. The Ours model has an accuracy of 29.5, while the Natural Model has an accuracy of 0.0. Therefore, the statement is supported.", "statement": "The Ours model achieves higher accuracy than the Natural Model under BIM 6 attack.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Distances of inferred interaction matrices from gradient ascent results'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/7.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Distances of inferred interaction matrices from gradient ascent results" ], "reasoning": "The TRW approximation consistently provided the smallest distance from the gradient ascent results across all datasets, while the SM approximation always yielded the worst results.", "description": "for all three datasets, the developed TRW approximation gave the smallest distance from the gradient ascent results. Somewhat surprisingly, SM approximation always gives the worst result." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Distances of inferred interaction matrices from gradient ascent results'\nProblem: 'Which approximation method gave the smallest distance from the gradient ascent results for sample number 2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/7.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Distances of inferred interaction matrices from gradient ascent results" ], "reasoning": "The table shows that for sample number 2, the TRW approximation has the smallest value (0.39), indicating the smallest distance from the gradient ascent results.", "question": "Which approximation method gave the smallest distance from the gradient ascent results for sample number 2?", "answer": "So, the answer is {\"answer\": \"TRW\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe IP approximation gave the smallest distance from the gradient ascent results for sample number 1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/7.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Distances of inferred interaction matrices from gradient ascent results" ], "reasoning": "The table shows that for sample number 1, the TRW approximation has the smallest value (0.40), not the IP approximation (0.91).", "statement": "The IP approximation gave the smallest distance from the gradient ascent results for sample number 1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Top-10 retrieval results and running time for *SHL for various data sets. Here, running time secs means seconds, mins means minutes and hours means hours.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/8.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Top-10 retrieval results and running time for *SHL for various data sets. Here, running time secs means seconds, mins means minutes and hours means hours." ], "reasoning": "Larger clusters benefit more from parallel computing, as shown by Mnist running on a 10-node cluster needing almost 25% of the running time compared to a 2-node cluster.", "description": "As reported in Table 1, *SHL provides competitive retrieval results using about 13 hours for the 45 bits codeword. Secondly, as shown in the second and third row in the table, a larger cluster will benefit more from parallel computing. Here, Mnist run on the 10 nodes cluster need almost 25% running time compared with the same data set on a 2 node cluster. Thus, when confronting with even larger data sets, a larger cluster with more powerful nodes can be a solution." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Top-10 retrieval results and running time for *SHL for various data sets. Here, running time secs means seconds, mins means minutes and hours means hours.'\nProblem: 'How much time does Mnist take to run on a 10-node cluster with 45 bits compared to a 2-node cluster with the same bits?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/8.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Top-10 retrieval results and running time for *SHL for various data sets. Here, running time secs means seconds, mins means minutes and hours means hours." ], "reasoning": "From the table, Mnist on a 10-node cluster with 45 bits takes 4253.28 seconds (70.89 mins / 1.18 hours), while on a 2-node cluster with 45 bits, it takes 4253.28 seconds (251.85 mins / 4.20 hours).", "question": "How much time does Mnist take to run on a 10-node cluster with 45 bits compared to a 2-node cluster with the same bits?", "answer": "So, the answer is {\"answer\": \"1.18 hours\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMnist running on a 10-node cluster with 25 bits takes less than 40 minutes.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/8.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Top-10 retrieval results and running time for *SHL for various data sets. Here, running time secs means seconds, mins means minutes and hours means hours." ], "reasoning": "From the table, Mnist running on a 10-node cluster with 25 bits takes 2361.07 seconds, which is 39.35 minutes.", "statement": "Mnist running on a 10-node cluster with 25 bits takes less than 40 minutes.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Ablation studies. (*) For count, the lower the better.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/9.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Ablation studies. (*) For count, the lower the better." ], "reasoning": "The CRN+MAC model shows significant improvements in SVQA and TGIF-QA tasks, demonstrating the importance of multi-step reasoning and temporal information in video question answering.", "description": "The ablation results are presented in Table 1, 2 showing progressive improvements, which justify the added complexity.. From Table 1, it is clear that TGIF-QA is highly linguistically. biased while the problem is mitigated with SVQA dataset to some extent.. Ling.+S.Frame: This is a very basic model of VQA that combines the encoded question vector with CNN features of a random frame from a given video. As expected, this baseline gives modest improvements over the model using only linguistic features. S.Frame+MAC: To demonstrate the significance of multi-step reasoning in Video QA, we randomly select one video frame and then use its CNN feature maps as the knowledge base of the multi-step centralized reasoning module MAC. In other words, this experiment reflects the importance of multi-step reasoning in a question answering problem. As SVQA dataset contains questions with compositional sequences, it greatly benefits from the multi-step centralized reasoning module. Avg.Pool+MAC: A baseline to assess the significance of temporal information in the simplest form of average temporal pooling comparing to ones with a single frame. We follow to sparely sample 8 frames which are the middle frames of the equal size segments from a given video. As can be seen, this model is able to achieve significant improvements in both SVQA and TGIF-QA. Due to the linguistic bias, the contribution of visual information to the overall performance on TGIF-QA is much less than that of SVQA. TRN+MAC: This baseline is a special case of ours where we flatten the hierarchy, and the relation network is applied at the frame level, similar to what proposed in . The model mitigates the limit of feature engineering process of using only one single frame for video representation as well as simply temporal pooling over the whole sequence of frames. Apparently, using a single frame loses crucial temporal information of the video and is likely to fail when strong temporal reasoning is needed, particularly in state transition and counting. We use visual features processed in the Avg.Pool+MAC experiment to input into a TRN module for fair comparisons. TRN improves by more than 12% of overall performance from one with a single video frame, while that of state transition task on TGIF-QA is more than 2%, around 1.5% for both repeating action and frame QA and 0.08 MSE in case of repetition count. Although this baseline produces great increments on SVQA comparing to the experiment Avg.Pool+MAC, it barely outperforms those on TGIF-QA. CRN+MLP: In order to evaluate how the reasoning module affects the overall performance, we conduct this experiment by using a feed-forward network as the reasoning module with the proposed visual representation CRN. CRN+MAC: This is our proposed method in which we opt CRN as the knowledge base of the compositional attention module. We experience significant improvements on all tasks over the simplistic TRN on SVQA whilst that of. TGIF-QA dataset is more moderate. The results reveal the strong CRN's capability as well as a better suit of video representation for reasoning over the simplistic TRN, especially in case of compositional reasoning. The results are also consistent with our earlier arguments that sparsely sampled frames from the video are insufficient to embrace fast-pace actions/events such as repeating action and repetition count." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Ablation studies. (*) For count, the lower the better.'\nProblem: 'Which model shows the highest performance on the SVQA dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/9.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Ablation studies. (*) For count, the lower the better." ], "reasoning": "By comparing the SVQA scores of all models in the table, the CRN+MAC model has the highest score of 75.8.", "question": "Which model shows the highest performance on the SVQA dataset?", "answer": { "answer": "CRN+MAC" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe TRN+MAC model outperforms the Avg.Pool+MAC model in TGIF-QA Action task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/9.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Ablation studies. (*) For count, the lower the better." ], "reasoning": "The table shows that the TRN+MAC model has a score of 69.0 in the TGIF-QA Action task, which is lower than the Avg.Pool+MAC model's score of 70.1.", "statement": "The TRN+MAC model outperforms the Avg.Pool+MAC model in TGIF-QA Action task.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison with the state-of-the-art method on SVQA.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/10.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison with the state-of-the-art method on SVQA." ], "reasoning": "The proposed model significantly improves the overall accuracy from 44.9% to 75.8% on the SVQA dataset, indicating a substantial contribution to state-of-the-art performance.", "description": "We also compare our proposed model with other state-ofthe-art methods on both two datasets, as shown in Table 3 (SVQA, synthetic). For the SVQA, Table 1 and Table 3 reveal that the contribution of visual information to the overall performance of the best known results is very little.. We establish new qualitatively different SOTAs, jumping massively from 44.9% accuracy to 75.8% accuracy overall." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Comparison with the state-of-the-art method on SVQA.'\nProblem: 'What is the overall accuracy of the CRN+MAC model on the SVQA dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/10.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison with the state-of-the-art method on SVQA." ], "reasoning": "The table shows the overall accuracy of different models on the SVQA dataset. The CRN+MAC model has an overall accuracy of 75.8%, which is highlighted in yellow.", "question": "What is the overall accuracy of the CRN+MAC model on the SVQA dataset?", "answer": "So, the answer is {\"answer\": \"75.8\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe SA(S) model has the highest accuracy in Attribute Comparison Type.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/10.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison with the state-of-the-art method on SVQA." ], "reasoning": "The table shows the accuracy of different models in various categories. For Attribute Comparison Type, the CRN+MAC model has the highest accuracy at 90.7%, not the SA(S) model.", "statement": "The SA(S) model has the highest accuracy in Attribute Comparison Type.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Comparison with the state-of-the-art method on TGIF-QA dataset. For count, the lower the better. R: ResNet, C: C3D features, F: flow features.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/11.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison with the state-of-the-art method on TGIF-QA dataset. For count, the lower the better. R: ResNet, C: C3D features, F: flow features." ], "reasoning": "Jang et al. extended VQA 2016 challenge models to evaluate on Video QA tasks using early and late fusion, while Gao et al. and Li et al. advanced performance with co-memory and self-attention mechanisms respectively.", "description": "Table 4 (TGIF-QA, real).. For the TGIF-QA dataset, Jang et al. extended winner models of the VQA 2016 challenge to evaluate on Video QA task, namely VIS+LSTM and VQA-MCB . Early fusion and late fusion are applied to both two approaches. We also list some other methods provided by including those proposed by and . Interestingly, none of the previous work reported ablation study of utilizing only textual cues as the input of the system to assess the linguistic bias of the dataset, and the fact that some of the reported methods produced worse performance than this baseline. We suspect that improper integrating of visual information caused confusion to the system giving such low performance. In , Jang et al. also proposed their own methods of leveraging spatial and temporal attention. In Table 4, SP indicates spatial attention, ST presents temporal attention while \"R\" and \"C\" mean ResNet features and C3D features, respectively. Later, Gao et al. greatly advanced the performance on this dataset with a co-memory mechanism on two video feature streams. Li et al. recently set the state of the art performance on TGIF-QA with only ResNet features by using a novel self-attention mechanism. Our method, similarly in the sense that we only use ResNet features, is able to achieve significant improvements on three tasks including repeating action, state transition and frame QA while we are slightly advanced on repetition count task compared to our competitor using the same video features.. Specifically, we gain better performance than of around 1% for repeating action, approximately 2% for state transition, 3.5% for frame QA, and 0.04 MSE for repetition count.. For repetition counting in TGIF-QA, although we have not outperformed , it is not directly comparable since they utilized motion in addition to appearance, whilst in our case motion is not explicitly used and thus the action boundaries are not clearly detected. We hypothesize that counting task needs a specific network, as evident in recent work [21, 33]." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Comparison with the state-of-the-art method on TGIF-QA dataset. For count, the lower the better. R: ResNet, C: C3D features, F: flow features.'\nProblem: 'Which model achieved the highest performance in the repeating action task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/11.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison with the state-of-the-art method on TGIF-QA dataset. For count, the lower the better. R: ResNet, C: C3D features, F: flow features." ], "reasoning": "To determine the highest performance in the repeating action task, we need to look at the 'Action' column in the table. The model CRN+MAC(R) has the highest score of 71.3.", "question": "Which model achieved the highest performance in the repeating action task?", "answer": { "answer": "CRN+MAC(R)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model with the best performance in the state transition task used ResNet features.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/11.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison with the state-of-the-art method on TGIF-QA dataset. For count, the lower the better. R: ResNet, C: C3D features, F: flow features." ], "reasoning": "To verify this statement, we need to check the 'Trans.' column for the highest score and see if the model used ResNet features. The highest score in the 'Trans.' column is 78.7 by CRN+MAC(R), which uses ResNet features.", "statement": "The model with the best performance in the state transition task used ResNet features.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Test MSE for the non-mutually-exclusive sinusoid regression problem. We compare MAML and CNP against meta-regularized MAML (MR-MAML) and meta-regularized CNP (MR-CNP) where regularization is either on the activations (A) or the weights (W). We report the mean over 5 trials and the standard deviation in parentheses.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/12.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test MSE for the non-mutually-exclusive sinusoid regression problem. We compare MAML and CNP against meta-regularized MAML (MR-MAML) and meta-regularized CNP (MR-CNP) where regularization is either on the activations (A) or the weights (W). We report the mean over 5 trials and the standard deviation in parentheses." ], "reasoning": "MR-MAML and MR-CNP outperform MAML and CNP in both 5-shot and 10-shot tasks by avoiding memorization and adapting to the data.", "description": "once we add the additional amplitude input which indicates the task identity, we find that both MAML and CNP converge to the complete memorization solution and fail to generalize well to test data (Table 1 and Appendix Figures 7 and 8). Both meta-regularized MAML and CNP (MR-MAML) and (MR-CNP) instead converge to a solution that adapts to the data, and as a result, greatly outperform the unregularized methods.. For CNP, although the task training set contains sufficient information to infer the correct amplitude, this information is ignored and the regression curve at test-time is determined by the one-hot vector. As a result, CNP can only generalize to points from the curves it has seen in the training (Figure 7 first row). On the other hand, MAML does use the task training data (Figure 5, 8 and Table 1), however, its performance is much worse than in the mutually-exclusive task. MR-MAML and MR-CNP avoid converging to a memorization solution and achieve excellent test performance on sinusoid task." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Test MSE for the non-mutually-exclusive sinusoid regression problem. We compare MAML and CNP against meta-regularized MAML (MR-MAML) and meta-regularized CNP (MR-CNP) where regularization is either on the activations (A) or the weights (W). We report the mean over 5 trials and the standard deviation in parentheses.'\nProblem: 'Which method has the lowest error rate in the 10-shot task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/12.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test MSE for the non-mutually-exclusive sinusoid regression problem. We compare MAML and CNP against meta-regularized MAML (MR-MAML) and meta-regularized CNP (MR-CNP) where regularization is either on the activations (A) or the weights (W). We report the mean over 5 trials and the standard deviation in parentheses." ], "reasoning": "By comparing the error rates in the 10-shot task, MR-CNP (A) and MR-CNP (W) both have the lowest error rate of 0.09.", "question": "Which method has the lowest error rate in the 10-shot task?", "answer": { "answer": "MR-CNP (A) and MR-CNP (W)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMR-MAML (A) has a higher error rate than MAML in the 5-shot task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/12.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test MSE for the non-mutually-exclusive sinusoid regression problem. We compare MAML and CNP against meta-regularized MAML (MR-MAML) and meta-regularized CNP (MR-CNP) where regularization is either on the activations (A) or the weights (W). We report the mean over 5 trials and the standard deviation in parentheses." ], "reasoning": "The table shows that MR-MAML (A) has an error rate of 0.17 in the 5-shot task, which is lower than MAML's error rate of 0.46.", "statement": "MR-MAML (A) has a higher error rate than MAML in the 5-shot task.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Meta-test MSE for the pose prediction problem. We compare MR-MAML (ours) with conventional MAML and fine-tuning (FT). We report the average over 5 trials and standard deviation in parentheses.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/13.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Meta-test MSE for the pose prediction problem. We compare MR-MAML (ours) with conventional MAML and fine-tuning (FT). We report the average over 5 trials and standard deviation in parentheses." ], "reasoning": "Meta-learning with meta-regularization on weights significantly outperforms MAML, CNP, and fine-tuning methods in terms of MSE.", "description": "We compare MAML and CNP with their meta-regularized versions (Table 2). We additionally include fine-tuning as baseline, which trains a single network on all the instances jointly, and then fine-tunes on the task training data. Meta-learning with meta-regularization (on weights) outperforms all competing methods by a large margin." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Meta-test MSE for the pose prediction problem. We compare MR-MAML (ours) with conventional MAML and fine-tuning (FT). We report the average over 5 trials and standard deviation in parentheses.'\nProblem: 'Which method achieves the lowest MSE according to the table?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/13.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Meta-test MSE for the pose prediction problem. We compare MR-MAML (ours) with conventional MAML and fine-tuning (FT). We report the average over 5 trials and standard deviation in parentheses." ], "reasoning": "By comparing the MSE values of all methods listed in the table, MR-MAML (W) has the lowest MSE of 2.26.", "question": "Which method achieves the lowest MSE according to the table?", "answer": "So, the answer is {\"answer\": \"MR-MAML (W)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMR-CNP (W) has a lower MSE than FT + Weight Decay.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/13.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Meta-test MSE for the pose prediction problem. We compare MR-MAML (ours) with conventional MAML and fine-tuning (FT). We report the average over 5 trials and standard deviation in parentheses." ], "reasoning": "The table shows that MR-CNP (W) has an MSE of 2.89, which is lower than the MSE of FT + Weight Decay, which is 6.16.", "statement": "MR-CNP (W) has a lower MSE than FT + Weight Decay.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Meta-test accuracy on non-mutually-exclusive (NME) classification. The fine-tuning and nearest-neighbor baseline results for MiniImagenet are from (Ravi & Larochelle, 2016).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/14.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Meta-test accuracy on non-mutually-exclusive (NME) classification. The fine-tuning and nearest-neighbor baseline results for MiniImagenet are from (Ravi & Larochelle, 2016)." ], "reasoning": "MR-MAML significantly outperforms MAML and TAML in both 20-way 1-shot and 20-way 5-shot tasks on the NME Omniglot dataset.", "description": "We evaluate MAML, TAML (Jamal & Qi, 2019), and MR-MAML (ours) on non-mutually-exclusive classification tasks (Table 4). We find that MR-MAML significantly outperforms previous methods on all of these tasks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Meta-test accuracy on non-mutually-exclusive (NME) classification. The fine-tuning and nearest-neighbor baseline results for MiniImagenet are from (Ravi & Larochelle, 2016).'\nProblem: 'What is the performance of MR-MAML in the 20-way 1-shot task compared to MAML and TAML?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/14.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Meta-test accuracy on non-mutually-exclusive (NME) classification. The fine-tuning and nearest-neighbor baseline results for MiniImagenet are from (Ravi & Larochelle, 2016)." ], "reasoning": "To answer this question, compare the performance percentages of MR-MAML, MAML, and TAML in the 20-way 1-shot task. MR-MAML achieves 83.3%, while MAML achieves 7.8% and TAML achieves 9.6%.", "question": "What is the performance of MR-MAML in the 20-way 1-shot task compared to MAML and TAML?", "answer": "So, the answer is {\"answer\": \"83.3%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMR-MAML performs better than TAML in the 20-way 5-shot task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/14.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Meta-test accuracy on non-mutually-exclusive (NME) classification. The fine-tuning and nearest-neighbor baseline results for MiniImagenet are from (Ravi & Larochelle, 2016)." ], "reasoning": "The table shows that MR-MAML achieves 94.1% in the 20-way 5-shot task, while TAML achieves 67.9%. Therefore, MR-MAML performs better than TAML in this task.", "statement": "MR-MAML performs better than TAML in the 20-way 5-shot task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4.2: Meta-test accuracy on non-mutually-exclusive (NME) classification. The fine-tuning and nearest-neighbor baseline results for MiniImagenet are from (Ravi & Larochelle, 2016).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/15.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.2: Meta-test accuracy on non-mutually-exclusive (NME) classification. The fine-tuning and nearest-neighbor baseline results for MiniImagenet are from (Ravi & Larochelle, 2016)." ], "reasoning": "MR-MAML significantly outperforms Fine-tuning, Nearest-neighbor, MAML, and TAML in both 5-way 1-shot and 5-way 5-shot tasks.", "description": "We evaluate MAML, TAML (Jamal & Qi, 2019), MR-MAML (ours), fine-tuning, and a nearest neighbor baseline on non-mutually-exclusive classification tasks (Table 4). We find that MR-MAML significantly outperforms previous methods on all of these tasks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4.2: Meta-test accuracy on non-mutually-exclusive (NME) classification. The fine-tuning and nearest-neighbor baseline results for MiniImagenet are from (Ravi & Larochelle, 2016).'\nProblem: 'Which method has the highest performance in the 5-way 1-shot task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/15.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.2: Meta-test accuracy on non-mutually-exclusive (NME) classification. The fine-tuning and nearest-neighbor baseline results for MiniImagenet are from (Ravi & Larochelle, 2016)." ], "reasoning": "By comparing the performance of all methods in the 5-way 1-shot task, MR-MAML has the highest performance with 43.6%.", "question": "Which method has the highest performance in the 5-way 1-shot task?", "answer": "So, the answer is {\"answer\": \"MR-MAML\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMR-MAML outperforms all other methods in the 5-way 5-shot task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/15.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.2: Meta-test accuracy on non-mutually-exclusive (NME) classification. The fine-tuning and nearest-neighbor baseline results for MiniImagenet are from (Ravi & Larochelle, 2016)." ], "reasoning": "The table shows that MR-MAML has the highest performance in the 5-way 5-shot task with 53.8%, which is higher than all other methods listed.", "statement": "MR-MAML outperforms all other methods in the 5-way 5-shot task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Label noise detection accuracy. Left: average error rate over all the classes (%) Right: Label noise recall, F1 and macro-F1 (%). NoiseRank(I) is iterative NoiseRank'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/16.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Label noise detection accuracy. Left: average error rate over all the classes (%) Right: Label noise recall, F1 and macro-F1 (%). NoiseRank(I) is iterative NoiseRank" ], "reasoning": "Table 2 shows the average error rate of label noise detection on Food101-N and Clothing-1M datasets, comparing various methods including NoiseRank and DRAE.", "description": "We report the effectiveness of our proposed method on detecting label noise in Table 2, in terms of averaged detection error rate over all classes in Food101-N and Clothing-1M. Table 2 details the average error rate of label noise detection on the verified validation set compared against a wide range of baselines.. On Food101-N the estimated noise is 19.66% and the avg error rate of iterative NoiseRank and DRAE are comparable." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Label noise detection accuracy. Left: average error rate over all the classes (%) Right: Label noise recall, F1 and macro-F1 (%). NoiseRank(I) is iterative NoiseRank'\nProblem: 'What is the average error rate of the NoiseRank (I) method on the Clothing-1M dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/16.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Label noise detection accuracy. Left: average error rate over all the classes (%) Right: Label noise recall, F1 and macro-F1 (%). NoiseRank(I) is iterative NoiseRank" ], "reasoning": "To find the average error rate of the NoiseRank (I) method on the Clothing-1M dataset, locate the row for NoiseRank (I) and the column for Clothing-1M. The value is 22.81.", "question": "What is the average error rate of the NoiseRank (I) method on the Clothing-1M dataset?", "answer": "So, the answer is {\"answer\": \"22.81\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average error rate of the DRAE method on the Food101-N dataset is 18.70.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/16.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Label noise detection accuracy. Left: average error rate over all the classes (%) Right: Label noise recall, F1 and macro-F1 (%). NoiseRank(I) is iterative NoiseRank" ], "reasoning": "To verify this statement, locate the row for the DRAE method and the column for Food101-N. The value is 18.70, which matches the statement.", "statement": "The average error rate of the DRAE method on the Food101-N dataset is 18.70.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Label noise detection accuracy. Left: average error rate over all the classes (%) Right: Label noise recall, F1 and macro-F1 (%). NoiseRank(I) is iterative NoiseRank'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/17.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Label noise detection accuracy. Left: average error rate over all the classes (%) Right: Label noise recall, F1 and macro-F1 (%). NoiseRank(I) is iterative NoiseRank" ], "reasoning": "NoiseRank achieves state-of-the-art recall on Food-101N and Clothing-1M, and its F1/MacroF1 scores are competitive with CleanNet, which requires verified labels.", "description": "we further measure recall, F1 and macro-F1 scores for label noise detection in Table 2. NoiseRank has state-of-the-art recall of 85.61% on Food-101N and 74.18% on Clothing-1M. NoiseRank F1/MacroF1 is competitive with the best supervised method in noise detection CleanNet which requires verified labels in training and validation, and thus has a significant advantage compared to unsupervised and weakly-supervised methods." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Label noise detection accuracy. Left: average error rate over all the classes (%) Right: Label noise recall, F1 and macro-F1 (%). NoiseRank(I) is iterative NoiseRank'\nProblem: 'Which method has the highest recall on Food-101N and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/17.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Label noise detection accuracy. Left: average error rate over all the classes (%) Right: Label noise recall, F1 and macro-F1 (%). NoiseRank(I) is iterative NoiseRank" ], "reasoning": "To find the method with the highest recall on Food-101N, look at the Recall column for Food-101N. NoiseRank has the highest recall with a value of 85.61%.", "question": "Which method has the highest recall on Food-101N?", "answer": { "answer": "NoiseRank" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nNoiseRank has a higher F1 score than CleanNet on Clothing-1M.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/17.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Label noise detection accuracy. Left: average error rate over all the classes (%) Right: Label noise recall, F1 and macro-F1 (%). NoiseRank(I) is iterative NoiseRank" ], "reasoning": "To verify this statement, compare the F1 scores of NoiseRank and CleanNet for Clothing-1M. NoiseRank has an F1 score of 71.74, while CleanNet has an F1 score of 73.99. Therefore, the statement is refuted.", "statement": "NoiseRank has a higher F1 score than CleanNet on Clothing-1M.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Image classification on Food-101N results in terms of top-1 accuracy (%). Train data (310k) and test data (25k). CleanNet is trained with an additional 55k/5k (tr/va) verification labels to provide the required supervision on noise detection'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/18.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Image classification on Food-101N results in terms of top-1 accuracy (%). Train data (310k) and test data (25k). CleanNet is trained with an additional 55k/5k (tr/va) verification labels to provide the required supervision on noise detection" ], "reasoning": "NoiseRank achieves the highest top-1 accuracy of 85.78% on Food101-N, outperforming both unsupervised and supervised noise reduction methods.", "description": "In results table 3 and 4, in each row, the model is fine-tuned with the mentioned training examples on the specified pre-trained model (eg. \"noisy train # 1\" refers to the model # 1 referenced in the table that was trained using noisy training samples on ImageNet pre-trained Resnet-50).. In Table 3 Food101-N, NoiseRank achieves state of the art 85.78% in top-1 accuracy compared to unsupervised 's 85.11%, and 11% error reduction over supervised noise reduction method CleanNet." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Image classification on Food-101N results in terms of top-1 accuracy (%). Train data (310k) and test data (25k). CleanNet is trained with an additional 55k/5k (tr/va) verification labels to provide the required supervision on noise detection'\nProblem: 'Which method achieved the highest top-1 accuracy in the table?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/18.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Image classification on Food-101N results in terms of top-1 accuracy (%). Train data (310k) and test data (25k). CleanNet is trained with an additional 55k/5k (tr/va) verification labels to provide the required supervision on noise detection" ], "reasoning": "By examining the 'Top-1' column, the highest value is 85.78%, which corresponds to the method NoiseRank.", "question": "Which method achieved the highest top-1 accuracy in the table?", "answer": "So, the answer is {\"answer\": \"NoiseRank\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCleanNet achieved a top-1 accuracy of 83.95% using noisy and verified training data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/18.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Image classification on Food-101N results in terms of top-1 accuracy (%). Train data (310k) and test data (25k). CleanNet is trained with an additional 55k/5k (tr/va) verification labels to provide the required supervision on noise detection" ], "reasoning": "The table shows that CleanNet, when trained with noisy(+verified) data, achieved a top-1 accuracy of 83.95%.", "statement": "CleanNet achieved a top-1 accuracy of 83.95% using noisy and verified training data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Image classification on Clothing-1M results in terms of top-1 accuracy (%). Train data (1M) and test data (10k). CleanNet and Loss Correction are trained with an additional 25k/7k (train/validation) verification labels to provide required supervision on noise detection/correction'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/19.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Image classification on Clothing-1M results in terms of top-1 accuracy (%). Train data (1M) and test data (10k). CleanNet and Loss Correction are trained with an additional 25k/7k (train/validation) verification labels to provide required supervision on noise detection/correction" ], "reasoning": "NoiseRank effectively reduces label noise, improving classification accuracy from 68.94% to 73.82% on Clothing-1M, and achieves the best result of 81.16% with an additional clean 50k sample set.", "description": "In results table 3 and 4, in each row, the model is fine-tuned with the mentioned training examples on the specified pre-trained model (eg. \"noisy train # 1\" refers to the model # 1 referenced in the table that was trained using noisy training samples on ImageNet pre-trained Resnet-50).. in Table 4, \"# 4\" in the pre-training column, refers to model # 4 indicated in the table.. In Table 4 Clothing-1M, NoiseRank used to reduce label noise in noisy train (∼40% estimated noise) is effective in improving classification from 68.94% to 73.82% (16% error reduction), even. without supervision from clean set in high noise regime, and performs comparable to recent unsupervised and marginally outperforms unsupervised PENCIL .. Lastly, we also reported results of fine-tuning each method with an additional clean 50k set, as per the setting followed in . achieves best result of 81.16% with clean 50k sample set. We note that even without noise correction, the inclusion of the clean set boosts accuracy from 68.94% to 79.43% and may shadow the benefit of noise removal; with CleanNet at 79.90% and ours at 79.57% being comparable in this setting." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Image classification on Clothing-1M results in terms of top-1 accuracy (%). Train data (1M) and test data (10k). CleanNet and Loss Correction are trained with an additional 25k/7k (train/validation) verification labels to provide required supervision on noise detection/correction'\nProblem: 'What is the Top-1 accuracy of the model fine-tuned with clean50k training examples and pre-trained on model # 10?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/19.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Image classification on Clothing-1M results in terms of top-1 accuracy (%). Train data (1M) and test data (10k). CleanNet and Loss Correction are trained with an additional 25k/7k (train/validation) verification labels to provide required supervision on noise detection/correction" ], "reasoning": "The table shows that the model fine-tuned with clean50k training examples and pre-trained on model # 10 achieves a Top-1 accuracy of 81.16%.", "question": "What is the Top-1 accuracy of the model fine-tuned with clean50k training examples and pre-trained on model # 10?", "answer": "So, the answer is {\"answer\": \"81.16\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model fine-tuned with noisy train and pre-trained on ImageNet achieves a Top-1 accuracy of 68.94%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/19.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Image classification on Clothing-1M results in terms of top-1 accuracy (%). Train data (1M) and test data (10k). CleanNet and Loss Correction are trained with an additional 25k/7k (train/validation) verification labels to provide required supervision on noise detection/correction" ], "reasoning": "The table shows that the model fine-tuned with noisy train and pre-trained on ImageNet achieves a Top-1 accuracy of 68.94%.", "statement": "The model fine-tuned with noisy train and pre-trained on ImageNet achieves a Top-1 accuracy of 68.94%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Number of extracted attribute witnesses in VGG-Face. The first row lists the layers used in VGG-Face. The second row shows the number of neurons at each layer. The following four rows present the number of extracted witnesses for individual attributes. The following six rows denote the pairwise overlap of witnesses between different attributes. The bottom row shows the number of neurons shared by the witness sets of different attributes.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/20.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Number of extracted attribute witnesses in VGG-Face. The first row lists the layers used in VGG-Face. The second row shows the number of neurons at each layer. The following four rows present the number of extracted witnesses for individual attributes. The following six rows denote the pairwise overlap of witnesses between different attributes. The bottom row shows the number of neurons shared by the witness sets of different attributes." ], "reasoning": "The table shows the number of neurons and witnesses in each layer, with neurons ranging from 64 to 4096 and witnesses fewer than 20.", "description": "The witnesses extracted can be found in Table 1, which shows the layers (1st row), the number of neurons in each layer (2nd row) and the number of witnesses (remaining rows). Note that although there are 64-4096 neurons in each layer, the number of witnesses extracted is smaller than 20." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Number of extracted attribute witnesses in VGG-Face. The first row lists the layers used in VGG-Face. The second row shows the number of neurons at each layer. The following four rows present the number of extracted witnesses for individual attributes. The following six rows denote the pairwise overlap of witnesses between different attributes. The bottom row shows the number of neurons shared by the witness sets of different attributes.'\nProblem: 'Which layer has the highest number of witnesses for the Mouth?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/20.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Number of extracted attribute witnesses in VGG-Face. The first row lists the layers used in VGG-Face. The second row shows the number of neurons at each layer. The following four rows present the number of extracted witnesses for individual attributes. The following six rows denote the pairwise overlap of witnesses between different attributes. The bottom row shows the number of neurons shared by the witness sets of different attributes." ], "reasoning": "To find the layer with the highest number of witnesses for the Mouth, look at the rows corresponding to the Mouth and identify the maximum value across all layers.", "question": "Which layer has the highest number of witnesses for the Mouth?", "answer": "So, the answer is {\"answer\": \"conv3_3\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe conv4_3 layer has more witnesses for the Nose than the conv4_2 layer.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/20.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Number of extracted attribute witnesses in VGG-Face. The first row lists the layers used in VGG-Face. The second row shows the number of neurons at each layer. The following four rows present the number of extracted witnesses for individual attributes. The following six rows denote the pairwise overlap of witnesses between different attributes. The bottom row shows the number of neurons shared by the witness sets of different attributes." ], "reasoning": "The table shows that the conv4_3 layer has 17 witnesses for the Nose, while the conv4_2 layer has 8 witnesses for the Nose.", "statement": "The conv4_3 layer has more witnesses for the Nose than the conv4_2 layer.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2. Results on the MPII validation dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/21.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Results on the MPII validation dataset" ], "reasoning": "The algorithm shows slight improvements over Detectron and Part Affinity Field baselines on COCO and MPII validation datasets without retraining.", "description": "This algorithm allows us to cover up for some of the limitations that the Detectron has, and we show that we achieve slight improvements over the Detectron and Part Affinity Field baselines with absolutely zero retraining.. Note that the skeleton structure of Detectron and PAFs are different (COCO v/s MPII). We ran on COCO keypoints 2017 validation dataset, and MPII validation dataset." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2. Results on the MPII validation dataset'\nProblem: 'What is the difference in APm between Detectron and the new algorithm?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/21.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Results on the MPII validation dataset" ], "reasoning": "To find the difference in APm, subtract the APm value of Detectron from the APm value of the new algorithm: 0.42 - 0.4173.", "question": "What is the difference in APm between Detectron and the new algorithm?", "answer": "So, the answer is {\"answer\": \"0.0027\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe new algorithm has a higher AP50 score than Detectron.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/21.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Results on the MPII validation dataset" ], "reasoning": "The table shows that the AP50 score for Detectron is 0.7687 and for the new algorithm is 0.7696, which is higher.", "statement": "The new algorithm has a higher AP50 score than Detectron.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Results on the COCO keypoints 2017 validation and test datasets'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/22.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on the COCO keypoints 2017 validation and test datasets" ], "reasoning": "The algorithm improves over Detectron and Part Affinity Field baselines on COCO and MPII datasets without retraining.", "description": "This algorithm allows us to cover up for some of the limitations that the Detectron has, and we show that we achieve slight improvements over the Detectron and Part Affinity Field baselines with absolutely zero retraining.. Note that the skeleton structure of Detectron and PAFs are different (COCO v/s MPII). We ran on COCO keypoints 2017 validation dataset, and MPII validation dataset." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Results on the COCO keypoints 2017 validation and test datasets'\nProblem: 'Which metric shows the highest improvement in the 'Ours' column compared to the 'Detectron' column?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/22.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on the COCO keypoints 2017 validation and test datasets" ], "reasoning": "To find the highest improvement, compare the values in the 'Ours' column with the 'Detectron' column for each metric. The highest difference is in AP50 (0.8677 - 0.8643 = 0.0034).", "question": "Which metric shows the highest improvement in the 'Ours' column compared to the 'Detectron' column?", "answer": "So, the answer is {\"answer\": \"AP50\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe algorithm achieves the highest AP score in the 'Ours' column.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/22.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on the COCO keypoints 2017 validation and test datasets" ], "reasoning": "The AP score in the 'Ours' column is 0.6465, which is higher than the AP scores in the other columns.", "statement": "The algorithm achieves the highest AP score in the 'Ours' column.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Comparison of action sets and their impact on the maximum floor reached after 10m steps.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/23.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of action sets and their impact on the maximum floor reached after 10m steps." ], "reasoning": "Action sets 8, 20, and 27 improved from an average of three floors to five floors.", "description": "We found that the 8, 20, and 27 action sets all saw a similar improvement in performance going from an average of three floors to an average of five floors (see Table 5)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Comparison of action sets and their impact on the maximum floor reached after 10m steps.'\nProblem: 'Which action sets saw an improvement in performance from an average of three floors to five floors?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/23.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of action sets and their impact on the maximum floor reached after 10m steps." ], "reasoning": "By comparing the average floors for each action set, we see that action sets 8, 20, and 27 improved from an average of three floors to five floors.", "question": "Which action sets saw an improvement in performance from an average of three floors to five floors?", "answer": "So, the answer is {\"answer\": \"Action sets 8, 20, and 27\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAction sets 8, 20, and 27 all saw an improvement in performance from an average of three floors to five floors.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/23.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of action sets and their impact on the maximum floor reached after 10m steps." ], "reasoning": "The table shows that the average floor for action sets 8, 20, and 27 increased from three to five floors.", "statement": "Action sets 8, 20, and 27 all saw an improvement in performance from an average of three floors to five floors.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Accuracy of attribute detection. Attribute detection using extracted witnesses versus using an existing layer in VGG-Face called face descriptor (i.e., the fc7 layer), whose neurons are considered representing abstract features of human faces Parkhi et al. (2015).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/24.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy of attribute detection. Attribute detection using extracted witnesses versus using an existing layer in VGG-Face called face descriptor (i.e., the fc7 layer), whose neurons are considered representing abstract features of human faces Parkhi et al. (2015)." ], "reasoning": "Attribute witnesses consistently achieve higher accuracy than face descriptors for both VF and LFW datasets, with a notable decrease in accuracy for LFW.", "description": "The results are shown in Table 2. For the VF dataset, witnesses consistently achieve higher than 93% accuracy whereas face descriptor has lower than 86% for 3 attributes. Since LFW is a dataset with a different set of persons from those in the training set (from VF), we observe a decrease of accuracy. However, attribute witnesses still have higher accuracy than face descriptor." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Accuracy of attribute detection. Attribute detection using extracted witnesses versus using an existing layer in VGG-Face called face descriptor (i.e., the fc7 layer), whose neurons are considered representing abstract features of human faces Parkhi et al. (2015).'\nProblem: 'What is the accuracy of the face descriptor for the VF dataset's nose attribute?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/24.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy of attribute detection. Attribute detection using extracted witnesses versus using an existing layer in VGG-Face called face descriptor (i.e., the fc7 layer), whose neurons are considered representing abstract features of human faces Parkhi et al. (2015)." ], "reasoning": "The table shows the accuracy of the face descriptor for the VF dataset's nose attribute as 0.955.", "question": "What is the accuracy of the face descriptor for the VF dataset's nose attribute?", "answer": { "answer": "0.955" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAttribute witnesses achieve higher than 93% accuracy for the VF dataset's left eye attribute.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/24.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy of attribute detection. Attribute detection using extracted witnesses versus using an existing layer in VGG-Face called face descriptor (i.e., the fc7 layer), whose neurons are considered representing abstract features of human faces Parkhi et al. (2015)." ], "reasoning": "The table shows that the accuracy of attribute witnesses for the VF dataset's left eye attribute is 0.940, which is higher than 93%.", "statement": "Attribute witnesses achieve higher than 93% accuracy for the VF dataset's left eye attribute.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Sensitivity to positional bias'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/25.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Sensitivity to positional bias" ], "reasoning": "The table shows that NAB's scoring function does not distinguish well between front-predicted and back-predicted scenarios, while the model with positional bias set appropriately does.", "description": "To further illustrate, we investigated how the two models behave under two contrasting positional bias scenarios: (i) anomaly predictions overlapping with front-end portions of real anomaly ranges (Front-Predicted) and (ii) anomaly predictions overlapping with back-end portions of real anomaly ranges (Back-Predicted) as shown in Table 2.. We then scored them using Numenta_Standard , F1 _T _Front_Flat, and F1 _T _Back _Flat.. As shown in Table 2, NAB's scoring function is not sufficiently sensitive to distinguish between the two scenarios.. However, our model distinguishes between the two scenarios when its positional bias is set appropriately." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Sensitivity to positional bias'\nProblem: 'Which model's scoring function is more sensitive to positional bias scenarios?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/25.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Sensitivity to positional bias" ], "reasoning": "The table shows that the Numenta_Standard scores are similar for both front-predicted and back-predicted scenarios, while the F1_T_Front_Flat and F1_T_Back_Flat scores vary significantly, indicating that the model with positional bias is more sensitive.", "question": "Which model's scoring function is less sensitive to positional bias scenarios?", "answer": "So, the answer is {\"answer\": \"Numenta_Standard\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nNAB's scoring function can distinguish between front-predicted and back-predicted scenarios.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/25.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Sensitivity to positional bias" ], "reasoning": "The table shows that the Numenta_Standard scores for front-predicted and back-predicted scenarios are very close (0.67 and 0.63), indicating that NAB's scoring function does not distinguish well between the two scenarios.", "statement": "NAB's scoring function can distinguish between front-predicted and back-predicted scenarios.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Time (in seconds) required for MDD construction and information generation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/26.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Time (in seconds) required for MDD construction and information generation." ], "reasoning": "The MDD database construction and constraint-specific information generation times are minimal, indicating its efficiency in handling constraints like average and median.", "description": "Table 4 shows that the time required to construct the MDD database and generate constraint specific information is quite small. This indicates that our MDD database can be used to effectively and efficiently handle constraints such as average and median." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Time (in seconds) required for MDD construction and information generation.'\nProblem: 'Which algorithm has the smallest time for the Kosarak dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/26.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Time (in seconds) required for MDD construction and information generation." ], "reasoning": "By comparing the times for the Kosarak dataset, MPP1 has the smallest time of 47.", "question": "Which algorithm has the smallest time for the Kosarak dataset?", "answer": "So, the answer is {\"answer\": \"MPP1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMPP3 has the highest time for the MSNBC dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/26.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Time (in seconds) required for MDD construction and information generation." ], "reasoning": "By comparing the times for the MSNBC dataset, MPP3 has the highest time of 158.", "statement": "MPP3 has the highest time for the MSNBC dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparison between ESSR, R-Max, E3, and MBIE. The numbers reported for R-Max, E3, and MBIE were extracted from the histograms presented by Strehl08 (Strehl08). ESSR’s performance is the average over 100 runs. A 95% confidence interval is reported between parentheses. All numbers are reported in millions (i.e., ×106).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/27.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison between ESSR, R-Max, E3, and MBIE. The numbers reported for R-Max, E3, and MBIE were extracted from the histograms presented by Strehl08 (Strehl08). ESSR’s performance is the average over 100 runs. A 95% confidence interval is reported between parentheses. All numbers are reported in millions (i.e., ×106)." ], "reasoning": "The table shows that ESSR performs similarly to other algorithms with PAC-MDP guarantees, indicating its potential as a promising exploration bonus.", "description": "that performs as well as some theoretically sample-efficient approaches.. in the tabular case, perform as well as traditional approaches with PAC-MDP guarantees.. Table 2 depicts the performance of this algorithm, dubbed ESSR, as well as the performance of some algorithms with polynomial sample-complexity bounds.. These results clearly show that ESSR performs, on average, similarly to other algorithms with PAC-MDP guarantees, suggesting that the norm of the SSR is a promising exploration bonus." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparison between ESSR, R-Max, E3, and MBIE. The numbers reported for R-Max, E3, and MBIE were extracted from the histograms presented by Strehl08 (Strehl08). ESSR’s performance is the average over 100 runs. A 95% confidence interval is reported between parentheses. All numbers are reported in millions (i.e., ×106).'\nProblem: 'What is the performance of ESSR in the RiverSwim environment?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/27.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison between ESSR, R-Max, E3, and MBIE. The numbers reported for R-Max, E3, and MBIE were extracted from the histograms presented by Strehl08 (Strehl08). ESSR’s performance is the average over 100 runs. A 95% confidence interval is reported between parentheses. All numbers are reported in millions (i.e., ×106)." ], "reasoning": "The table lists the performance of various algorithms in the RiverSwim environment. ESSR has a performance score of 3.1 in this environment.", "question": "What is the performance of ESSR in the RiverSwim environment?", "answer": "So, the answer is {\"answer\": \"3.1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nESSR performs better than MBIE in the SixArms environment.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/27.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison between ESSR, R-Max, E3, and MBIE. The numbers reported for R-Max, E3, and MBIE were extracted from the histograms presented by Strehl08 (Strehl08). ESSR’s performance is the average over 100 runs. A 95% confidence interval is reported between parentheses. All numbers are reported in millions (i.e., ×106)." ], "reasoning": "The table shows that ESSR has a performance score of 7.3 in the SixArms environment, while MBIE has a performance score of 9.3. Since a lower score is better, ESSR performs better than MBIE in the SixArms environment.", "statement": "ESSR performs better than MBIE in the SixArms environment.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Performance of the proposed algorithm, DQNMMCe+SR, compared to various agents on the “hard exploration” subset of Atari 2600 games. The DQN results reported are from Machado18a (Machado18a) while the DQNMMC\\scriptsize{CTS}, DQNMMC\\scriptsize{PixelCNN} and RND results were obtained through personal communication with the authors of the corresponding papers. Burda19 did not evaluate RND in Freeway. When available, standard deviation is reported between parentheses. See text for details.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/28.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of the proposed algorithm, DQNMMCe+SR, compared to various agents on the “hard exploration” subset of Atari 2600 games. The DQN results reported are from Machado18a (Machado18a) while the DQNMMC\\scriptsize{CTS}, DQNMMC\\scriptsize{PixelCNN} and RND results were obtained through personal communication with the authors of the corresponding papers. Burda19 did not evaluate RND in Freeway. When available, standard deviation is reported between parentheses. See text for details." ], "reasoning": "The algorithm achieves state-of-the-art performance in hard exploration Atari 2600 games, outperforming DQN and RND, especially in games like Montezuma's Revenge.", "description": "it achieves state-of-the-art performance in Atari 2600 games when in a low sample-complexity regime.. achieves state-of-the-art performance in hard exploration Atari 2600 games when in a low sample-complexity regime.. Table 3 summarizes the results after 100 million frames.. CTS and DQNMMC e We can clearly see that our algorithm achieves scores much higher than those achieved by DQN, which struggles in games that pose hard exploration problems. When comparing our algorithm to DQNMMC PIXELCNN we observe that, on average, DQNMMC +SR at least matches the performance of these algorithms while being simpler by not requiring a density model. Instead, our algorithm requires the SR, which is domain-independent as it is already defined for every problem since it is a component of the value function estimates, as discussed in Section 2. Finally, DQNMMC +SR also outperforms RND (Burda et al. 2019) when it is trained for 100 million frames.4 e. e the comparison between DQNMMC +SR and Finally, DQNMMC shows that the provided exploration bonus has a big impact in the game MONTEZUMA'S REVENGE, which is probably known as the hardest game among those we used in our evaluation, and the only game where agents do not learn how to achieve scores greater than zero with random e. exploration. Interestingly, the change in architecture and the use of MMC leads to a big improvement in games such as GRAVITAR and VENTURE, which we cannot fully explain. However, because the change in architecture does not have any effect in MONTEZUMA'S REVENGE, it seems that the proposed exploration bonus is essential in games with very sparse rewards.. While the results depicted in Table 3 allow us to see the benefit of using an exploration bonus derived from the SR, they do not inform us about the impact of the auxiliary task in the results.. the comparisons in Table 3 and the results in this section suggest that the exploration bonus we introduced is essential for our approach to achieve state-of-the-art performance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Performance of the proposed algorithm, DQNMMCe+SR, compared to various agents on the “hard exploration” subset of Atari 2600 games. The DQN results reported are from Machado18a (Machado18a) while the DQNMMC\\scriptsize{CTS}, DQNMMC\\scriptsize{PixelCNN} and RND results were obtained through personal communication with the authors of the corresponding papers. Burda19 did not evaluate RND in Freeway. When available, standard deviation is reported between parentheses. See text for details.'\nProblem: 'Which algorithm achieves the highest score in Montezuma's Revenge?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/28.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of the proposed algorithm, DQNMMCe+SR, compared to various agents on the “hard exploration” subset of Atari 2600 games. The DQN results reported are from Machado18a (Machado18a) while the DQNMMC\\scriptsize{CTS}, DQNMMC\\scriptsize{PixelCNN} and RND results were obtained through personal communication with the authors of the corresponding papers. Burda19 did not evaluate RND in Freeway. When available, standard deviation is reported between parentheses. See text for details." ], "reasoning": "To find the highest score in Montezuma's Revenge, compare the scores of all algorithms listed in the table for this game. The highest score is 2941.9 achieved by DQNMMC\\scriptsize{CTS} 29.2.", "question": "Which algorithm achieves the highest score in Montezuma's Revenge?", "answer": "So, the answer is {\"answer\": \"DQNMMC\\scriptsize{CTS} 29.2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDQNMMC\\scriptsize{PixelCNN} 29.4 achieves the highest score in Private Eye.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/28.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of the proposed algorithm, DQNMMCe+SR, compared to various agents on the “hard exploration” subset of Atari 2600 games. The DQN results reported are from Machado18a (Machado18a) while the DQNMMC\\scriptsize{CTS}, DQNMMC\\scriptsize{PixelCNN} and RND results were obtained through personal communication with the authors of the corresponding papers. Burda19 did not evaluate RND in Freeway. When available, standard deviation is reported between parentheses. See text for details." ], "reasoning": "To verify this statement, compare the scores of all algorithms listed in the table for Private Eye. The highest score is 14386.0 achieved by DQNMMC\\scriptsize{PixelCNN} 29.4.", "statement": "DQNMMC\\scriptsize{PixelCNN} 29.4 achieves the highest score in Private Eye.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison between Sarsa and Sarsa+SR. A 95% confidence interval is reported between parentheses.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/29.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison between Sarsa and Sarsa+SR. A 95% confidence interval is reported between parentheses." ], "reasoning": "Sarsa+SR significantly outperforms Sarsa in both RiverSwim and SixArms tasks, with average returns of approximately 1.2 million and 1.05 million respectively.", "description": "The performance of each algorithm, averaged over 100 runs, is available in Table 1.. Sarsa obtains an average return of approximately 25,000 while Sarsa+SR obtains an approximate average return of 1.2 million." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison between Sarsa and Sarsa+SR. A 95% confidence interval is reported between parentheses.'\nProblem: 'What is the difference in average return between Sarsa and Sarsa+SR for the RiverSwim task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/29.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison between Sarsa and Sarsa+SR. A 95% confidence interval is reported between parentheses." ], "reasoning": "The average return for Sarsa in the RiverSwim task is 24,770, while for Sarsa+SR it is 1,213,544. The difference is calculated as 1,213,544 - 24,770.", "question": "What is the difference in average return between Sarsa and Sarsa+SR for the RiverSwim task?", "answer": "So, the answer is {\"answer\": \"1,188,774\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSarsa+SR achieves an average return of over 1 million in the SixArms task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/29.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison between Sarsa and Sarsa+SR. A 95% confidence interval is reported between parentheses." ], "reasoning": "The table shows that Sarsa+SR achieves an average return of 1,052,934 in the SixArms task, which is over 1 million.", "statement": "Sarsa+SR achieves an average return of over 1 million in the SixArms task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Performance of Sarsa+SR when using the ℓ1-norm and ℓ2-norm of the SR to generate the exploration bonus. A 95% confidence interval is reported between parentheses.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/30.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance of Sarsa+SR when using the ℓ1-norm and ℓ2-norm of the SR to generate the exploration bonus. A 95% confidence interval is reported between parentheses." ], "reasoning": "The results for Sarsa+SR using the (cid:96)2-norm of the SR are averaged over 100 runs, and the choice of norm (either (cid:96)1 or (cid:ell)2) does not significantly impact the results.", "description": "The results reported for Sarsa+SR when using the (cid:96)2-norm of the SR are the average over 100 runs. The actual numbers are available in Table 5. As before, it seems that it does not make much difference which norm of the SR we use ((cid:96)1-norm or the (cid:96)2-norm)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Performance of Sarsa+SR when using the ℓ1-norm and ℓ2-norm of the SR to generate the exploration bonus. A 95% confidence interval is reported between parentheses.'\nProblem: 'What is the average result for Sarsa+SR using the (cid:96)2-norm of the SR for the SixArms environment?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/30.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance of Sarsa+SR when using the ℓ1-norm and ℓ2-norm of the SR to generate the exploration bonus. A 95% confidence interval is reported between parentheses." ], "reasoning": "The table shows the results for different norms of the SR. For the SixArms environment, the average result using the (cid:ell)2-norm of the SR is 819,927.", "question": "What is the average result for Sarsa+SR using the (cid:ell)2-norm of the SR for the SixArms environment?", "answer": "So, the answer is {\"answer\": \"819,927\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average result for Sarsa+SR using the (cid:ell)2-norm of the SR for the RiverSwim environment is 1,213,544.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/30.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance of Sarsa+SR when using the ℓ1-norm and ℓ2-norm of the SR to generate the exploration bonus. A 95% confidence interval is reported between parentheses." ], "reasoning": "The table shows that the average result for Sarsa+SR using the (cid:ell)2-norm of the SR for the RiverSwim environment is 1,192,052, not 1,213,544.", "statement": "The average result for Sarsa+SR using the (cid:ell)2-norm of the SR for the RiverSwim environment is 1,213,544.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Detecting adversarial samples. We have two settings for targeted attacks. ‘First’ denotes that the first label is the target whereas ‘Next’ denotes the next label of the correct label is the target. FS stands feature squeezing; AS attribute substitution only; AP attribute preservation only; WKN non-witness weakening only; STN witness strengthening only; and AmI our final result. The bottom four rows denote detection accuracy of AmI using witnesses excluding some certain attribute (e.g., w/o Nose).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/31.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Detecting adversarial samples. We have two settings for targeted attacks. ‘First’ denotes that the first label is the target whereas ‘Next’ denotes the next label of the correct label is the target. FS stands feature squeezing; AS attribute substitution only; AP attribute preservation only; WKN non-witness weakening only; STN witness strengthening only; and AmI our final result. The bottom four rows denote detection accuracy of AmI using witnesses excluding some certain attribute (e.g., w/o Nose)." ], "reasoning": "AmI detects adversarial samples with over 90% accuracy for most attacks, while FS achieves a best result of 77% and has higher false positive rates.", "description": "From Table 3 it can be observed that AmI can effectively detect adversarial samples with more than 90% accuracy for most attacks. The same tests are conducted on feature squeezing (FS) . The best result achieved by FS is 77%. FS especially does not preform well on FGSM and BIM, which is consistent with the observation of Xu et al. .. The false positive rate (the FP column in Table 3) is 9.91% for AmI and 23.32% for FS on the VF dataset.. we conducted a few additional experiments: (1) we use only attribute substitution to extract witnesses and then build the attribute-steered model for detection with results shown in the AS row of Table 3; (2) we use only attribute preservation (AP); (3) we only weaken non-witnesses (WKN); and (4) we only strengthen witnesses (STN). Our results show that although AS or AP alone can detect adversarial samples with good accuracy, their false positive rates (see the FP column) are very high (i.e., 20.41% and 30.61%).. Specifically, we evaluate on four subsets of attribute witnesses extracted using bi-directional reasoning as shown in Table 3: (1) we exclude witnesses of left eye (w/o Left Eye); (2) exclude witnesses of right eye (w/o Right Eye); (3) exclude witnesses of nose (w/o Nose); and (4) exclude witnesses of mouth (w/o Mouth). We observe that the detection accuracy degrades a bit (less than 5% in most cases)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Detecting adversarial samples. We have two settings for targeted attacks. ‘First’ denotes that the first label is the target whereas ‘Next’ denotes the next label of the correct label is the target. FS stands feature squeezing; AS attribute substitution only; AP attribute preservation only; WKN non-witness weakening only; STN witness strengthening only; and AmI our final result. The bottom four rows denote detection accuracy of AmI using witnesses excluding some certain attribute (e.g., w/o Nose).'\nProblem: 'What is the false positive rate for the attribute preservation (AP) method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/31.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Detecting adversarial samples. We have two settings for targeted attacks. ‘First’ denotes that the first label is the target whereas ‘Next’ denotes the next label of the correct label is the target. FS stands feature squeezing; AS attribute substitution only; AP attribute preservation only; WKN non-witness weakening only; STN witness strengthening only; and AmI our final result. The bottom four rows denote detection accuracy of AmI using witnesses excluding some certain attribute (e.g., w/o Nose)." ], "reasoning": "The description mentions that the false positive rate for AP is very high, specifically 30.61%. This is confirmed by the FP column in the table.", "question": "What is the false positive rate for the attribute preservation (AP) method?", "answer": "So, the answer is {\"answer\": \"30.61%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe false positive rate for the attribute substitution (AS) method is lower than that of the attribute preservation (AP) method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/31.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Detecting adversarial samples. We have two settings for targeted attacks. ‘First’ denotes that the first label is the target whereas ‘Next’ denotes the next label of the correct label is the target. FS stands feature squeezing; AS attribute substitution only; AP attribute preservation only; WKN non-witness weakening only; STN witness strengthening only; and AmI our final result. The bottom four rows denote detection accuracy of AmI using witnesses excluding some certain attribute (e.g., w/o Nose)." ], "reasoning": "The table shows that the false positive rate for AS is 20.41%, while for AP it is 30.61%, indicating that AS has a lower false positive rate than AP.", "statement": "The false positive rate for the attribute substitution (AS) method is lower than that of the attribute preservation (AP) method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Comparison of testing error(%) and running time (s) for different learning approaches and training data sets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/32.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Comparison of testing error(%) and running time (s) for different learning approaches and training data sets." ], "reasoning": "The DMTL-ELM algorithm achieves almost the same testing error as centralized MTL-ELM but with faster running time, while FO-DMTL-ELM has the fastest running time among MTL methods.", "description": "The testing errors of Local ELM, MTL-ELM and DMTL-ELM and FO-DMTL-ELM in Table 1 are obtained with L = 300, where τt = 20 + dt, ζt = 40 for DMTL-ELM and τ(cid:48) = 40 t for FO-DMTL-ELM. = 30 + dt, ζ (cid:48) t. To. Table 1 demonstrates that the testing error achieved by MTL-ELM, DMTL-ELM and FO-DMTL-ELM can outperform other MTL methods over the tested data sets.. Meanwhile, the proposed DMTL-ELM algorithm can achieve almost the same testing error compared with centralized MTL-ELM. Though with decayed generalization performance with same iteration k, the running time of FO-DMTL-ELM is much faster than MTL-ELM and DMTL-ELM. Among all the tested methods, the Loal ELM has the shortest running time." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Comparison of testing error(%) and running time (s) for different learning approaches and training data sets.'\nProblem: 'Which method has the lowest testing error on the USPS dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/32.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Comparison of testing error(%) and running time (s) for different learning approaches and training data sets." ], "reasoning": "By comparing the testing errors for the USPS dataset, MTL-ELM has the lowest testing error of 3.49.", "question": "Which method has the lowest testing error on the USPS dataset?", "answer": "So, the answer is {\"answer\": \"MTL-ELM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe FO-DMTL-ELM method has the fastest running time on the MNIST dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/32.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Comparison of testing error(%) and running time (s) for different learning approaches and training data sets." ], "reasoning": "The table shows that Local ELM has the fastest running time of 0.004 on the MNIST dataset, while FO-DMTL-ELM has a running time of 19.7.", "statement": "The FO-DMTL-ELM method has the fastest running time on the MNIST dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: User study result on comparison with the automatic video colorization'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/33.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: User study result on comparison with the automatic video colorization" ], "reasoning": "Our approach is significantly more likely to be chosen as the 1st-rank result with a percentage of 50.66%, outperforming Larsson et al., Iizuka et al., and Zhang et al. in terms of average rank.", "description": "Table 6 shows details of the user study result on this task. Our approach is 50.66% more likely to be chosen as the 1st-rank result which significantly outperforms all three in terms of average rank: 1.98 ± 1.36 vs. 2.39 ± 1.03 for , 2.68 ± 0.93 for , and 2.95 ± 1.16 for ." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: User study result on comparison with the automatic video colorization'\nProblem: 'Which approach has the lowest average rank?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/33.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: User study result on comparison with the automatic video colorization" ], "reasoning": "By comparing the average ranks of all approaches, 'Ours' has the lowest average rank of 1.98 ± 1.36.", "question": "Which approach has the lowest average rank?", "answer": { "answer": "Ours" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nZhang et al. has the highest average rank.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/33.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: User study result on comparison with the automatic video colorization" ], "reasoning": "The table shows that Zhang et al. has an average rank of 2.95 ± 1.16, which is higher than Larsson et al., Iizuka et al., and Ours.", "statement": "Zhang et al. has the highest average rank.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: User study result on comparison with the video color propagation'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/34.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: User study result on comparison with the video color propagation" ], "reasoning": "Our method achieves the highest 1st-rank percentage at 79.67% and the highest average rank of 1.24 ± 0.156 compared to STN and VPN.", "description": "Table 7 shows these results. Again, our method achieves the highest 1st-rank percentage at 79.67%. Overall, it achieves the highest average rank: 1.24 ± 0.156 vs. 2.07 ± 0.33 for STN, and 2.69 ± 0.36 for VPN." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: User study result on comparison with the video color propagation'\nProblem: 'What is the Top1 percentage achieved by our method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/34.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: User study result on comparison with the video color propagation" ], "reasoning": "The table shows the Top1 percentage for STN, VPN, and our method. Our method achieves a Top1 percentage of 0.8.", "question": "What is the Top1 percentage achieved by our method?", "answer": { "answer": "0.8" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nVPN achieves a higher Top3 percentage than STN.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/34.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: User study result on comparison with the video color propagation" ], "reasoning": "The table shows that VPN has a Top3 percentage of 0.76, while STN has a Top3 percentage of 0.151. Therefore, VPN achieves a higher Top3 percentage than STN.", "statement": "VPN achieves a higher Top3 percentage than STN.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Accuracies for relation linking task.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/35.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracies for relation linking task." ], "reasoning": "MDP-Parser outperforms other approaches in terms of accuracy, improving EARL's performance by almost 5% on all datasets.", "description": "Table 2 summarizes the results in terms of accuracy. Despite the simplicity of the linking component in our approach, MDP-Parser achieves better results compared to the baselines, and again, improves the performance of EARL by almost 5% on all the datasets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Accuracies for relation linking task.'\nProblem: 'Which approach achieves the highest accuracy on the LC-QuAD dataset and by how much does it improve over EARL?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/35.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracies for relation linking task." ], "reasoning": "MDP-Parser achieves the highest accuracy on the LC-QuAD dataset with 0.45, which is 0.19 higher than EARL's 0.26.", "question": "Which approach achieves the highest accuracy on the LC-QuAD dataset and by how much does it improve over EARL?", "answer": "So, the answer is {\"answer\": \"0.45\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMDP-Parser achieves the highest accuracy on the QALD-6 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/35.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracies for relation linking task." ], "reasoning": "The table shows that MDP-Parser has an accuracy of 0.34 on the QALD-6 dataset, which is the highest among all approaches listed.", "statement": "MDP-Parser achieves the highest accuracy on the QALD-6 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Accuracies on LC-QuAD in lowercase'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/36.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracies on LC-QuAD in lowercase" ], "reasoning": "The performance of EARL and Falcon significantly decreases on the modified dataset, while MDP-Parser maintains almost the same performance for relation linking and shows a smaller decrease for entity linking.", "description": "Table 3 shows that the performance of both baselines remarkably falls down on the modified dataset. The accuracy of EARL drops by 60% and 10%, and Falcon by 29% and 23% for entity linking and relation linking tasks, respectively (cmp. Table 1 and Table2). On the other hand, MDP-Parser performs almost the same as before for relation linking (only 1% decrease). However, the accuracy of entity linking drops by 15%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Accuracies on LC-QuAD in lowercase'\nProblem: 'Which approach shows the smallest decrease in accuracy for relation linking on the modified dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/36.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracies on LC-QuAD in lowercase" ], "reasoning": "The description states that MDP-Parser performs almost the same as before for relation linking with only a 1% decrease, indicating it has the smallest decrease in accuracy.", "question": "Which approach shows the smallest decrease in accuracy for relation linking on the modified dataset?", "answer": { "answer": "MDP-Parser" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMDP-Parser's accuracy for entity linking drops by 15% on the modified dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/36.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracies on LC-QuAD in lowercase" ], "reasoning": "The description explicitly states that the accuracy of entity linking for MDP-Parser drops by 15%, but it is not shown in the table.", "statement": "MDP-Parser's accuracy for entity linking drops by 15% on the modified dataset.", "answer": { "answer": "not enough info" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Hyper-parameters in the Cartpole v0.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/37.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Hyper-parameters in the Cartpole v0." ], "reasoning": "The Cartpole-v0 environment uses relu activation for neural network output to select adaptive actions, with a fitness threshold of 499.5 rewards in 500 episode steps.", "description": "As a classical continuous control environment , the Cartpole-v0 environment is controlled by bringing to bear a force of +1 or −1 to the cart. A pendulum starts upright, and the goal is to prevent it from toppling over. An accumulated reward of +1 would be given before a terminated environment (e.g., falling 15 degrees from vertical, or a cart shifting more than 2.4 units from the center). As experimental settings, we select a sample size of 1000, and use relu activation for neural network output to select an adaptive action in Tab. II. To solve the problem, we conduct and fine-tune both NEAT and FS-NEAT as baseline results for accessing targeted accumulated rewards of 195.0 in 200 episode steps .. Here, we have improved the requirements of the fitness threshold (499.5 rewards in 500 episode steps) and normalized the fitness threshold as episode steps . See Tab. II. rewards. continuous control," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Hyper-parameters in the Cartpole v0.'\nProblem: 'What is the fitness threshold value used in the Cartpole-v0 environment?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/37.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Hyper-parameters in the Cartpole v0." ], "reasoning": "The description mentions the fitness threshold as 499.5 rewards in 500 episode steps, and the table lists the fitness threshold value as 0.999.", "question": "What is the fitness threshold value used in the Cartpole-v0 environment?", "answer": { "answer": "0.999" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Cartpole-v0 environment uses sigmoid activation for neural network output.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/37.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Hyper-parameters in the Cartpole v0." ], "reasoning": "The table lists the activation function as relu, not sigmoid.", "statement": "The Cartpole-v0 environment uses sigmoid activation for neural network output.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: Result statistics in the experiments of logic gates.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/38.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Result statistics in the experiments of logic gates." ], "reasoning": "The results indicate that as task difficulty increases, the proposed algorithm accelerates population evolution, with a notable difference in end generations between IMPLY and XOR tasks.", "description": "After restraining the influence of hyper-parameters, the tasks from Tab. V describe the influence of task complexity on evolutionary strategies.. The results show that with the increase in task difficulty, our algorithm can make the population evolve faster. In the IMPLY task, the difference between the average end generation is 1 to 2 generations. When the average of end generations in XOR tasks is counted, the gap between our proposed strategies and the baselines widens to nearly 20 generations. Additionally, the average node number in the final neural network and the task complexity seem to have a potentially positive correlation." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: Result statistics in the experiments of logic gates.'\nProblem: 'What is the average end generation difference between the proposed strategies and baselines in XOR tasks?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/38.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Result statistics in the experiments of logic gates." ], "reasoning": "The average end generation for XOR tasks using NEAT is 103.42, while for Bi-NEAT it is 84.15. The difference is 103.42 - 84.15 = 19.27 generations.", "question": "What is the average end generation difference between the proposed strategies and baselines in XOR tasks?", "answer": "So, the answer is {\"answer\": \"19.27\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average end generation for the IMPLY task using FS-NEAT is higher than using Bi-NEAT.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/38.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Result statistics in the experiments of logic gates." ], "reasoning": "The table shows that the average end generation for the IMPLY task using FS-NEAT is 6.35, while for Bi-NEAT it is 5.00. Therefore, the statement is correct.", "statement": "The average end generation for the IMPLY task using FS-NEAT is higher than using Bi-NEAT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VI: Result statistics in the complex experiments.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/39.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Result statistics in the complex experiments." ], "reasoning": "The Bi-NEAT and GS-NEAT methods show superior evolutionary speed and stability compared to baselines, requiring fewer generations and having smaller standard deviations.", "description": "The tasks in the continuous control and game environments Bi-NEAT and GS-NEAT still show amazing potential. See Tab. VI.. Unlike in the case of the logical experiments, the results show that the two proposed strategies are superior both in terms of evolutionary speed and stability. The enhanced evolutionary speed is reflected in the fact that the baselines require two to three times the average end generation as our strategies for the tested tasks. In addition, the smaller standard variance of end generation shows the evolutionary stability of our strategies." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VI: Result statistics in the complex experiments.'\nProblem: 'Which method has the lowest average generation for the CartPole v0 task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/39.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Result statistics in the complex experiments." ], "reasoning": "To find the method with the lowest average generation for the CartPole v0 task, compare the Avg.gen values for all methods listed under CartPole v0. The lowest value is 29.35 for Bi-NEAT.", "question": "Which method has the lowest average generation for the CartPole v0 task?", "answer": "So, the answer is {\"answer\": \"Bi-NEAT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe FS-NEAT method has a lower fall rate than NEAT for the LunarLander v2 task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/39.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Result statistics in the complex experiments." ], "reasoning": "The table shows that for the LunarLander v2 task, FS-NEAT has a fall rate of 3.3%, while NEAT has a fall rate of 4.9%. Therefore, FS-NEAT has a lower fall rate than NEAT.", "statement": "The FS-NEAT method has a lower fall rate than NEAT for the LunarLander v2 task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Performance of feature selection and extraction methods on MNIST dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/40.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Performance of feature selection and extraction methods on MNIST dataset." ], "reasoning": "The accuracy of the original data without any feature selection or extraction method is 53.50%.", "description": "As reported in Table II, the accuracy of original data without applying any feature selection or extraction method is 53.50%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Performance of feature selection and extraction methods on MNIST dataset.'\nProblem: 'What is the accuracy of the method with the highest number of features?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/40.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Performance of feature selection and extraction methods on MNIST dataset." ], "reasoning": "The method with the highest number of features is PSO with 403 features, and its accuracy is 59.42%.", "question": "What is the accuracy of the method with the highest number of features?", "answer": "{\"answer\": \"59.42%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe accuracy of the method using t-SNE is higher than the accuracy of the original data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/40.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Performance of feature selection and extraction methods on MNIST dataset." ], "reasoning": "The accuracy of the method using t-SNE is 89.62%, which is higher than the accuracy of the original data at 53.50%.", "statement": "The accuracy of the method using t-SNE is higher than the accuracy of the original data.", "answer": "{\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Sequences in decimal representation of one trained PRNG with seed=0 and B=80, alongside their average NIST value.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/41.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Sequences in decimal representation of one trained PRNG with seed=0 and B=80, alongside their average NIST value." ], "reasoning": "The NIST test suite computes the average value of eligible tests on generated PRNs sequences, which is used as a reward function ranging from 0 to 1.", "description": "NIST test suite is used to compute the average value of all eligible tests in the battery, run on the generated PRNs sequences.. This value is then used as a reward function. Please notice tsince test statistic values are probabilities according to statistical hypothesis definition, rewards belong to [0, 1].. In table 1 some generated sequences are shown in decimal representation. All the generated sequences are results of one trained PRNG with one seed state. Since any fixed length sequence can be considered the period of the generator and there are multiple sequences given by just one generator," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Sequences in decimal representation of one trained PRNG with seed=0 and B=80, alongside their average NIST value.'\nProblem: 'What is the average NIST value for Sequence -10?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/41.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Sequences in decimal representation of one trained PRNG with seed=0 and B=80, alongside their average NIST value." ], "reasoning": "The table shows the average NIST value for Sequence 22 in the last column of the row corresponding to Sequence 22.", "question": "What is the average NIST value for Sequence 22?", "answer": "So, the answer is {\"answer\": \"0.57\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average NIST value for Sequence 68 is 0.24.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/41.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Sequences in decimal representation of one trained PRNG with seed=0 and B=80, alongside their average NIST value." ], "reasoning": "The table shows the average NIST value for Sequence 68 in the last column of the row corresponding to Sequence 68, which is 0.24.", "statement": "The average NIST value for Sequence 68 is 0.24.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparative results (with only 1 imperfect demonstration). All results are measured in the original exact reward.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/42.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparative results (with only 1 imperfect demonstration). All results are measured in the original exact reward." ], "reasoning": "Our method achieves comparable performances with baselines on simple tasks and outperforms them on difficult tasks, converging faster and obtaining better final results.", "description": "In comparative evaluations, we carry out several RLfD baselines, including Pre-train (Silver et al. 2016) and POfD (Kang, Jie, and Feng 2018). In particular, we introduce another two baselines of penalty method3 with MMD as discrepancy measure, denoted by Penalty and Penalty + Ann., and the later one also employ an annealing strategy described in Sec. 4. We also run two non-RLfD baselines PPO and MMD-Imitation (denoted as MMD-IL) to verify the reward sparsification and the imperfect expert setting respectively. PPO will run with the sparse reward while MMD-IL will directly optimize the objective defined in (9) with provided imperfect demonstrations.. The results of our compar. ative evaluations are summarized in Table 1, which averaged 50 trials under the learned policies.. The results overall read that our method achieves comparable performances with the baselines on relatively simple tasks (such as MountainCar) and outperforms them with a large margin on difficult tasks (such as Hopper, Walker2d and Ant). During policy optimization, our method can converge faster than other RLfD counterparts as well as obtains better final results. Comparing with the strong baseline of Pre-train, we can see that although convergence efficiency of proposed method during the early phase of training may not have significant advantages, but as it continues, the performance of our method can be improved persistently like Hopper(+973.42) and Walker2d(+876.40), while Pre-train struggles on achieving higher return, which demonstrates that our method could benefit more from the exploration guidance offered by the soft constraint during the whole policy optimization procedure than by only imitating at the beginning.. On the other hand, we also find that our algorithm exhibits a more stable and efficient behavior over all the baselines using the penalty method. From the learning curve and numerical results, it can be seen that adopting penalty with imperfect demonstrations will induce a noisy and misleading gradient update, which will prevent the performances from improving further while our method with a soft constraint will not suffer from this. This essentially accounts for the performance gap between our method and all baselines with penalty departures. Moreover, the complex training strategies and auxiliary model in POfD also leads to unstable and. inefficient training across different tasks and environment specifications.. From the results of PPO and MMD-Imitation, the experiment settings of reward sparsification and imperfect demonstrations can be verified. As it illustrates, under sparse environmental feedback, pure PPO fails to find an optimal policy on most of the tested tasks, which indicates the impact of ineffective exploration. While with few imperfect demonstrations, MMD-Imitation also cannot learn promising policies. It suggests that combining the demonstrations and environmental feedback would be essential for the designated tasks. Furthermore, as similar MMD-Imitation update may happen in our method when the optimization just starts (mentioned in Sec. 3.3), these results also show how can our method benefit from the follow-up solving of the constrained optimization problem." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparative results (with only 1 imperfect demonstration). All results are measured in the original exact reward.'\nProblem: 'Which method achieved the highest score on the Walker2d task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/42.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparative results (with only 1 imperfect demonstration). All results are measured in the original exact reward." ], "reasoning": "By comparing the scores for the Walker2d task across all methods, we see that 'Ours' achieved the highest score of 3483.78±269.59.", "question": "Which method achieved the highest score on the Walker2d task?", "answer": "So, the answer is {\"answer\": \"Ours\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Pre-train method achieved the highest score on the DoublePendulum task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/42.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparative results (with only 1 imperfect demonstration). All results are measured in the original exact reward." ], "reasoning": "By examining the scores for the DoublePendulum task, we see that 'Ours' achieved the highest score of 9331.40±5.95, not the Pre-train method.", "statement": "The Pre-train method achieved the highest score on the DoublePendulum task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Number of rules for clustering methods and K-means clustering.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/43.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Number of rules for clustering methods and K-means clustering." ], "reasoning": "The number of rules for anomalous data points is always less than those for non-anomalous ones, and DT generates fewer rules than our algorithm except for anomalous data points in dataset 4.", "description": "Table 2 shows that the number of rules for anomalous data points is always inferior to those for non-anomalous ones.. Regarding hypothesis 2, in most of the cases DT generates less rules than our algorithm (with the exception of anomalous data points in dataset 4). However, results may be considered similar.. We also compared the results of our algorithm with other surrogate methods such as using a DT model trained over the same features and using as target variable the anomalous or nonanomalous labels. In most scenarios both methods yielded similar results, thus showing that our approach is a feasible technique for XAI for anomaly detection." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Number of rules for clustering methods and K-means clustering.'\nProblem: 'In which dataset does DT generate more rules than our algorithm for anomalous data points?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/43.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Number of rules for clustering methods and K-means clustering." ], "reasoning": "The table shows inliers with linear kernel achieve 49 in 4 dataset.", "question": "In which dataset does inliers with linear kernel achieve 49 data points?", "answer": { "answer": "4" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe number of rules for anomalous data points is always less than those for non-anomalous ones.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/43.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Number of rules for clustering methods and K-means clustering." ], "reasoning": "The table shows that the number of rules for outliers (anomalous data points) is always less than those for inliers (non-anomalous data points) across all datasets.", "statement": "The number of rules for anomalous data points is always less than those for non-anomalous ones.", "answer": { "answer": "not enough info" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Parking availability prediction error given by MAE and RMSE on Beijing and Shenzhen.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/44.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Parking availability prediction error given by MAE and RMSE on Beijing and Shenzhen." ], "reasoning": "SHARE significantly outperforms its variants CxtGNN and CAGNN, as well as the state-of-the-art approach DCRNN, in terms of MAE and RMSE on both BEIJING and SHENZHEN datasets.", "description": "we observe significant improvement by comparing SHARE with its variants (i.e., CxtGNN and CAGNN). For example, by adding the PA approximation module, CAGNN achieves (18.6%, 18.6%, 18.3%) lower MAE and (12.2%, 12.6%, 13.5%) lower RMSE than CxtGNN on BEIJING, respectively. By further adding the SCConv block, SHARE achieves (14.2%, 14.1%, 13.4%) lower MAE and (20%, 19.7%, 18.2%) lower RMSE than CAGNN on BEIJING.. Table 2 reports the overall results of our methods and all the compared baselines on two datasets with respect to MAE and RMSE.. Specifically, SHARE achieves (31.8%, 31.3%, 29.9%) and (30.9%, 31.5%, 30.9%) improvements beyond the state-ofthe-art approach (DCRNN) on MAE and RMSE on BEIJING for (15min, 30min, 45min) prediction, respectively. Similarity, the improvement of MAE and RMSE on SHENZHEN are (29.6%, 28.7%, 30.5%) and (28.8%, 28.8%, 28.4%).. we observe all graph convolution based models (i.e., STGCN, DCRNN and SHARE) outperform other deep learning based approaches (i.e., Google-Parking and Du-parking),. GBRT outperforms Google-parking, GRU, LR, and achieves a similar result with Du-parking,. One extra interesting finding is that both MAE and RMSE of all methods on SHENZHEN is relatively smaller than on BEIJING." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Parking availability prediction error given by MAE and RMSE on Beijing and Shenzhen.'\nProblem: 'What is the MAE improvement percentage of SHARE over DCRNN for the 15-minute prediction on the BEIJING dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/44.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Parking availability prediction error given by MAE and RMSE on Beijing and Shenzhen." ], "reasoning": "The MAE for DCRNN on the BEIJING dataset for the 15-minute prediction is 15.66, and for SHARE it is 10.68. The improvement percentage is calculated as ((15.66 - 10.68) / 15.66) * 100.", "question": "What is the MAE improvement percentage of SHARE over DCRNN for the 15-minute prediction on the BEIJING dataset?", "answer": "So, the answer is {\"answer\": \"31.8%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSHARE achieves the lowest RMSE on the SHENZHEN dataset for the 45-minute prediction.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/44.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Parking availability prediction error given by MAE and RMSE on Beijing and Shenzhen." ], "reasoning": "The RMSE for SHARE on the SHENZHEN dataset for the 45-minute prediction is 31.70, which is the lowest compared to all other algorithms listed in the table.", "statement": "SHARE achieves the lowest RMSE on the SHENZHEN dataset for the 45-minute prediction.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Means and standard deviations of TDD and baseline accuracies on text data using TextConvNet. All values are percentages. The first four baselines are used to train the same neural network as in the distillation experiments. The last two baselines are used to train a K-Nearest Neighbors classifier. Each result uses 10 GD steps aside from IMDB with k-means and TREC50 which had to be done with 2 GD steps due to GPU memory constraints and also insufficient training samples for some classes in TREC50. The second TREC50 row uses TDD with 5 GD steps with 4 images per class. Experiments with random initializations have their results listed in the form [mean ± standard deviation] and are based on the resulting performance of 200 randomly initialized networks.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/45.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Means and standard deviations of TDD and baseline accuracies on text data using TextConvNet. All values are percentages. The first four baselines are used to train the same neural network as in the distillation experiments. The last two baselines are used to train a K-Nearest Neighbors classifier. Each result uses 10 GD steps aside from IMDB with k-means and TREC50 which had to be done with 2 GD steps due to GPU memory constraints and also insufficient training samples for some classes in TREC50. The second TREC50 row uses TDD with 5 GD steps with 4 images per class. Experiments with random initializations have their results listed in the form [mean ± standard deviation] and are based on the resulting performance of 200 randomly initialized networks." ], "reasoning": "TDD shows varying distillation ratios and accuracies across different datasets and initialization methods, with IMDB and TREC6 performing better than TREC50 and TREC502.", "description": "The baseline results are shown in Table 3.. For example, TDD can produce 20 distilled sentences that train the CNN model up to a distillation ratio of 91.62% on the IMDB dataset. Even for far more difficult language tasks, TDD still has impressive results but with larger distilled datasets. For example, for the 50-class TREC50 task, it can learn 1000 sentences that train the model to a distillation ratio of 79.86%.. Some examples of TDD results are detailed in Table 3.. TDD can learn 20 images that train a network with random initialization up to a distillation ratio of 84.27% on IMDB, only slightly lower than in the fixed initialization case. Similarly, for TREC6 there is only a 2.07% difference between the 60-sample distillation ratio for fixed and random initializations; the accuracies were within a standard deviation of each other.. Only in the case of the much tougher TREC50 task, was there a large decrease in performance when working with random initializations.. All the mean and standard deviation results for random initializations in Table 3 are derived by testing with 200 randomly initialized networks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Means and standard deviations of TDD and baseline accuracies on text data using TextConvNet. All values are percentages. The first four baselines are used to train the same neural network as in the distillation experiments. The last two baselines are used to train a K-Nearest Neighbors classifier. Each result uses 10 GD steps aside from IMDB with k-means and TREC50 which had to be done with 2 GD steps due to GPU memory constraints and also insufficient training samples for some classes in TREC50. The second TREC50 row uses TDD with 5 GD steps with 4 images per class. Experiments with random initializations have their results listed in the form [mean ± standard deviation] and are based on the resulting performance of 200 randomly initialized networks.'\nProblem: 'What is the difference in TDD accuracy between fixed and random initialization for the TREC50 task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/45.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Means and standard deviations of TDD and baseline accuracies on text data using TextConvNet. All values are percentages. The first four baselines are used to train the same neural network as in the distillation experiments. The last two baselines are used to train a K-Nearest Neighbors classifier. Each result uses 10 GD steps aside from IMDB with k-means and TREC50 which had to be done with 2 GD steps due to GPU memory constraints and also insufficient training samples for some classes in TREC50. The second TREC50 row uses TDD with 5 GD steps with 4 images per class. Experiments with random initializations have their results listed in the form [mean ± standard deviation] and are based on the resulting performance of 200 randomly initialized networks." ], "reasoning": "The TDD accuracy for TREC50 with fixed initialization is 57.6, while with random initialization it is 11.0. The difference is 57.6 - 11.0.", "question": "What is the difference in TDD accuracy between fixed and random initialization for the TREC50 task?", "answer": { "answer": "46.6" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe TDD accuracy for the IMDB dataset is higher with fixed initialization than with random initialization.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/45.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Means and standard deviations of TDD and baseline accuracies on text data using TextConvNet. All values are percentages. The first four baselines are used to train the same neural network as in the distillation experiments. The last two baselines are used to train a K-Nearest Neighbors classifier. Each result uses 10 GD steps aside from IMDB with k-means and TREC50 which had to be done with 2 GD steps due to GPU memory constraints and also insufficient training samples for some classes in TREC50. The second TREC50 row uses TDD with 5 GD steps with 4 images per class. Experiments with random initializations have their results listed in the form [mean ± standard deviation] and are based on the resulting performance of 200 randomly initialized networks." ], "reasoning": "The table shows that the TDD accuracy for IMDB is 75.0 with fixed initialization and 73.4 with random initialization, confirming that the accuracy is higher with fixed initialization.", "statement": "The TDD accuracy for the IMDB dataset is higher with fixed initialization than with random initialization.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: The architecture of the CNN for learning. From the first to the fifth layers constituted a CAE. The sixth layer had the weights obtained from the Laplacian filter bank in which three filters were used.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/46.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The architecture of the CNN for learning. From the first to the fifth layers constituted a CAE. The sixth layer had the weights obtained from the Laplacian filter bank in which three filters were used." ], "reasoning": "The CNN architecture includes a CAE from the first to the fifth layer, with the fifth layer using tanh activation. The third layer's feature maps have half the resolution of the original images due to a stride of 2, while the fourth layer performs upsampling with a stride of 0.5. The sixth layer uses a Laplacian filter bank with three channels corresponding to subbands.", "description": "The architecture of the CNN for learning is shown in Tab.1.. From the first layer to the fifth layer constituted a CAE. Since the activation function of the fifth layer was tanh, the pixel values of the original images were scaled to [−1, 1]. The resolution of the feature maps of the third layer was the half of the resolution of the original images, because the stride of the third layer was 2. The stride of the fourth layer was 0.5 that means upsampling carried out by the bilinear interpolation. The sixth layer had the weights obtained from the Laplacian filter bank. Since the three filters with the scales σc = 0.8, 1.6 and 3.2 were used for the filter bank, the sixth layer had the three channels. corresponding to the subbands. Although the size of the Laplacian filter was basically decided by ⌈8σc⌉, the size was changed to an odd number by adding 1 if ⌈8σc⌉ was an even number." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: The architecture of the CNN for learning. From the first to the fifth layers constituted a CAE. The sixth layer had the weights obtained from the Laplacian filter bank in which three filters were used.'\nProblem: 'What is the stride of the layer that performs upsampling?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/46.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The architecture of the CNN for learning. From the first to the fifth layers constituted a CAE. The sixth layer had the weights obtained from the Laplacian filter bank in which three filters were used." ], "reasoning": "The table shows that the fourth layer with a stride of 0.5.", "question": "What is the stride of the layer corresponding to the fourth layer?", "answer": "So, the answer is {\"answer\": \"0.5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe third layer has a stride of 2 and uses ReLU activation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/46.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The architecture of the CNN for learning. From the first to the fifth layers constituted a CAE. The sixth layer had the weights obtained from the Laplacian filter bank in which three filters were used." ], "reasoning": "According to the table, the third layer has a stride of 2 and uses ReLU activation.", "statement": "The third layer has a stride of 2 and uses ReLU activation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Comparison with state-of-the-art visual navigation algorithms'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/47.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Comparison with state-of-the-art visual navigation algorithms" ], "reasoning": "MJOLNIR-o shows a significant improvement in Success Rate (SR) compared to the current state-of-the-art models, with approximately a 30% increase.", "description": "Table. and Figure 4a shows the performance of each of the tested models in terms of Success Rate (SR) and SPL. It can be seen that both of our models significantly outperform the current state-of-the-art. MJOLNIR-o has ≈ 30% increase in SR.. It is also important to note here, that the GCN network of , which does not include the context vector as its node feature, performs poorer than even the baseline and SAVN ." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Comparison with state-of-the-art visual navigation algorithms'\nProblem: 'Which model has the highest Success Rate (SR) for L>=1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/47.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Comparison with state-of-the-art visual navigation algorithms" ], "reasoning": "By examining the table, MJOLNIR-o has the highest Success Rate (SR) for L>=1 with a value of 65.3%.", "question": "Which model has the highest Success Rate (SR) for L>=1?", "answer": { "answer": "MJOLNIR-o" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe GCN network performs better than the baseline model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/47.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Comparison with state-of-the-art visual navigation algorithms" ], "reasoning": "The table does not provide any information about the GCN network, so it is not possible to determine its performance relative to the baseline model.", "statement": "The GCN network performs better than the baseline model.", "answer": { "answer": "not enough info" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Ablation study for MJOLNIR'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/48.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Ablation study for MJOLNIR" ], "reasoning": "The MJOLNIR-o model with sampled + env action outperforms the SAVN model in all metrics.", "description": "We compare our model with the SAVN model using this stopping criteria. The results show that we perform significantly better." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Ablation study for MJOLNIR'\nProblem: 'Which model and action combination has the highest L>=1 SR(%)?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/48.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Ablation study for MJOLNIR" ], "reasoning": "By comparing the L>=1 SR(%) values for all model and action combinations, the highest value is 83.1% for the MJOLNIR-o model with sampled + env action.", "question": "Which model and action combination has the highest L>=1 SR(%)?", "answer": "So, the answer is {\"answer\": \"MJOLNIR-o with sampled + env\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe SAVN model has a higher L>=5 SR(%) than the MJOLNIR-o model with sampled + env action.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/48.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Ablation study for MJOLNIR" ], "reasoning": "The table shows that the SAVN model has an L>=5 SR(%) of 37.87%, while the MJOLNIR-o model with sampled + env action has an L>=5 SR(%) of 71.6%. Therefore, the statement is refuted.", "statement": "The SAVN model has a higher L>=5 SR(%) than the MJOLNIR-o model with sampled + env action.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Mean F1-Score with the full VLN dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/49.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Mean F1-Score with the full VLN dataset" ], "reasoning": "TrueLearn outperforms all baseline models in terms of F1-Score, demonstrating its effectiveness while maintaining a transparent learner model.", "description": "We implement four baseline models to compare TrueLearn against: i) Na¨ıve persistence, which assumes a static behaviour for all users, i.e. if the learner is engaged, they will remain engaged and vice versa; ii) Na¨ıve majority, which predicts future engagement based solely on mean past engagement of users; iii) KT model (Multi-Skill KT) according to (Bishop, Winn, and Diethe 2015); and iv) Vanilla TrueSkill (Herbrich, Minka, and Graepel 2007).. The results in Table 1 show evidence that TrueLearn outperforms the baselines while retaining a transparent learner model." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Mean F1-Score with the full VLN dataset'\nProblem: 'Which algorithm has the highest F1-Score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/49.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Mean F1-Score with the full VLN dataset" ], "reasoning": "By comparing the F1-Scores of all algorithms listed in the table, TrueLearn has the highest F1-Score of 0.677.", "question": "Which algorithm has the highest F1-Score?", "answer": "So, the answer is {\"answer\": \"TrueLearn\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nVanilla TrueSkill has a higher F1-Score than Naïve persistence.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/49.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Mean F1-Score with the full VLN dataset" ], "reasoning": "The table shows that Vanilla TrueSkill has an F1-Score of 0.400, while Naïve persistence has an F1-Score of 0.629. Therefore, Vanilla TrueSkill does not have a higher F1-Score than Naïve persistence.", "statement": "Vanilla TrueSkill has a higher F1-Score than Naïve persistence.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Effect of Data Augmentation on the UTD-MHAD dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/50.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Effect of Data Augmentation on the UTD-MHAD dataset." ], "reasoning": "Data augmentation reduces the top-1 error rate consistently but has minimal effect on the top-3 error rate.", "description": "The results are summarized in Table I. As can be seen, data augmentation regularizes the model training and helps model avoid overfitting. As a result, when we. increase the training dataset by data augmentation, the top1 error rate is consistently reduced. We also notice that data augmentation does not have much effect on top-3 error rate, indicating that data augmentation mainly boosts correct answers from top-3 positions to top-1 positions." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Effect of Data Augmentation on the UTD-MHAD dataset.'\nProblem: 'What is the top-1 error rate when the augment size is 860?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/50.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Effect of Data Augmentation on the UTD-MHAD dataset." ], "reasoning": "To find the top-1 error rate for an augment size of 860, locate the row with augment size 860 and check the corresponding top-1 error rate value.", "question": "What is the top-1 error rate when the augment size is 860?", "answer": "So, the answer is {\"answer\": \"9.35\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIncreasing the augment size from 0 to 1720 reduces the top-1 error rate by more than 1.5%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/50.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Effect of Data Augmentation on the UTD-MHAD dataset." ], "reasoning": "The top-1 error rate at augment size 0 is 10.94%, and at augment size 1720, it is 9.05%. The reduction is 10.94% - 9.05% = 1.89%, which is more than 1.5%.", "statement": "Increasing the augment size from 0 to 1720 reduces the top-1 error rate by more than 1.5%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Sliced Wasserstein Score'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/51.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Sliced Wasserstein Score" ], "reasoning": "Adding cycle loss and semantic consistency loss resulted in higher Sliced Wasserstein Scores, while style embedding increased the score slightly but allowed control over features like hair and eye color.", "description": "We measure those benefits both quantitatively using Sliced Wasserstein Score proposed in .. In our experiments, we observed that having UNet encourages the network to find more local correspondences. Without UNet, the network failed to preserve correspondence between semantic parts and there were common error patterns such as the face direction becoming mirrored after translation – which is technically allowed by all our loss terms but is judged as being unnatural by human. Note that similar error patterns are observed in our experiment with MUNIT 1, which does not use UNet. Adding cycle loss and semantic consistency loss both resulted in higher Sliced Wasserstein Score and better output. Adding Style embedding increased the Sliced Wasserstein Score by a little, but it gave the user the ability to control some features such as hair color and eye color. However we argue that those features should perhaps belong to the content and the style embeddings failed to catch the more subtle yet important style information, such as eye-to-face ratio, texture of the hair, etc., that varies from painter to painter. We thus made the style embedding optional and did not use that for the final results." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Sliced Wasserstein Score'\nProblem: 'Which model had the highest Sliced Wasserstein Score for the 16 dimension?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/51.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Sliced Wasserstein Score" ], "reasoning": "To find the model with the highest Sliced Wasserstein Score for the 16 dimension, look at the scores in the '16' column. The highest score is 82.89, which corresponds to the 'No UNet' model.", "question": "Which model had the highest Sliced Wasserstein Score for the 16 dimension?", "answer": "So, the answer is {\"answer\": \"No UNet\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model without semantic consistency loss had the lowest average Sliced Wasserstein Score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/51.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Sliced Wasserstein Score" ], "reasoning": "To verify this statement, compare the average Sliced Wasserstein Scores of all models. The 'No Lsem' model has an average score of 18.38, which is higher than the 'With Style Embedding' model (15.16) and the 'No Style Embedding' model (14.84). Therefore, the statement is refuted.", "statement": "The model without semantic consistency loss had the lowest average Sliced Wasserstein Score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Human to Cat Translation'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/52.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human to Cat Translation" ], "reasoning": "The model translates human faces to cat faces using 200k human images from the CELEBA dataset and 10k cat faces from the CAT dataset, with the same network and training setup as in section 4.1.", "description": "In order to show the general applicability of our model, here we show our results on the task of translating human faces to cat faces. For human face we collected 200k images from the CELEBA dataset. We extracted around 10k cat faces from the CAT dataset by cropping the cat faces using. the eye and ear positions. The network, the CELEBA dataset, and training setup is the same as in 4.1." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Human to Cat Translation'\nProblem: 'How many cat faces were extracted from the CAT dataset for the task of translating human faces to cat faces?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/52.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human to Cat Translation" ], "reasoning": "The description mentions that around 10k cat faces were extracted from the CAT dataset by cropping the cat faces using the eye and ear positions.", "question": "How many cat faces were extracted from the CAT dataset for the task of translating human faces to cat faces?", "answer": "So, the answer is {\"answer\": \"10k\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model used 200k human images from the CELEBA dataset for translating human faces to cat faces.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/52.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human to Cat Translation" ], "reasoning": "The description states that 200k images of human faces were collected from the CELEBA dataset for the task of translating human faces to cat faces.", "statement": "The model used 200k human images from the CELEBA dataset for translating human faces to cat faces.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Mortality downstream task ablation, and comparison with baselines. Results reported are average of 5 runs on a test split of 15% and the std are reported in parenthesis.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/53.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Mortality downstream task ablation, and comparison with baselines. Results reported are average of 5 runs on a test split of 15% and the std are reported in parenthesis." ], "reasoning": "The PSV model benefits from both code and signal representations, as shown by higher performance metrics in the downstream tasks.", "description": "The results for both downstream tasks are summarized in Table 2&3.. In the tables, the ablation of the different components of the PSV model is done by first pretraining the model in an unsupervised fashion and freezing the pre-trained network for downstream tasks. Empirically, from both tables, it is evident the network benefits from both code and signal representations on the defined tasks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Mortality downstream task ablation, and comparison with baselines. Results reported are average of 5 runs on a test split of 15% and the std are reported in parenthesis.'\nProblem: 'Which PSV model configuration achieved the highest 1h-24h PR-AUC score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/53.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Mortality downstream task ablation, and comparison with baselines. Results reported are average of 5 runs on a test split of 15% and the std are reported in parenthesis." ], "reasoning": "By examining the table, the PSV (Semi-supervised) configuration achieved the highest 1h-24h PR-AUC score of 65.40.", "question": "Which PSV model configuration achieved the highest 1h-24h PR-AUC score?", "answer": "So, the answer is {\"answer\": \"PSV (Semi-supervised)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe PSV (Code) configuration outperformed the PSV (Signal) configuration in the 24h-720h PR-AUC metric.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/53.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Mortality downstream task ablation, and comparison with baselines. Results reported are average of 5 runs on a test split of 15% and the std are reported in parenthesis." ], "reasoning": "The table shows that the PSV (Code) configuration has a 24h-720h PR-AUC score of 31.32, while the PSV (Signal) configuration has a score of 31.32. Therefore, the statement is refuted.", "statement": "The PSV (Code) configuration outperformed the PSV (Signal) configuration in the 24h-720h PR-AUC metric.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Readmission downstream task ablation, and comparison with baselines. Results reported are average of 5 runs on a test split of 15% and the std are reported in parenthesis.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/54.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Readmission downstream task ablation, and comparison with baselines. Results reported are average of 5 runs on a test split of 15% and the std are reported in parenthesis." ], "reasoning": "The PSV model benefits from both code and signal representations, as shown by the higher performance metrics in the tables.", "description": "The results for both downstream tasks are summarized in Table 2&3.. In the tables, the ablation of the different components of the PSV model is done by first pretraining the model in an unsupervised fashion and freezing the pre-trained network for downstream tasks. Empirically, from both tables, it is evident the network benefits from both code and signal representations on the defined tasks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Readmission downstream task ablation, and comparison with baselines. Results reported are average of 5 runs on a test split of 15% and the std are reported in parenthesis.'\nProblem: 'Which PSV model configuration achieved the highest 1h-24h PR-AUC score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/54.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Readmission downstream task ablation, and comparison with baselines. Results reported are average of 5 runs on a test split of 15% and the std are reported in parenthesis." ], "reasoning": "By comparing the 1h-24h PR-AUC scores in the table, the PSV (Semi-supervised) configuration achieved the highest score of 69.02.", "question": "Which PSV model configuration achieved the highest 1h-24h PR-AUC score?", "answer": "So, the answer is {\"answer\": \"PSV (Semi-supervised)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Seq2Seq (Semi-supervised) model outperformed the Transformer model in the 24h-720h ROC metric.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/54.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Readmission downstream task ablation, and comparison with baselines. Results reported are average of 5 runs on a test split of 15% and the std are reported in parenthesis." ], "reasoning": "The table shows that the Seq2Seq (Semi-supervised) model has a 24h-720h ROC score of 52.18, while the Transformer model has a score of 59.45, indicating that the Transformer model outperformed the Seq2Seq (Semi-supervised) model in this metric.", "statement": "The Seq2Seq (Semi-supervised) model outperformed the Transformer model in the 24h-720h ROC metric.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table II: Results of the 2016 Competition: Track 1. ‘Frags’ is the number of opponent kills decreased by the number of suicides of the agent. ‘F/D’ denotes Frags/Death. ‘Deaths’ include suicides.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/55.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table II: Results of the 2016 Competition: Track 1. ‘Frags’ is the number of opponent kills decreased by the number of suicides of the agent. ‘F/D’ denotes Frags/Death. ‘Deaths’ include suicides." ], "reasoning": "F1 was the best bot with the highest frags and the least suicides, while Arnold had the highest F/D ratio and the least deaths.", "description": "The results of the competition are shown in Tables II and III, for Track 1 and 2, respectively.. The bots in 7 out of 9 submissions were competent enough to systematically eliminate the opponents. Among them, four bots stand out by scoring more than 300 frags: F1, Arnold, Clyde and TUHO. The difference between the second (Arnold) and the third (Clyde) place was minuscule (413 vs. 393 frags) and it is questionable whether the order remained the same if games were repeated. There is no doubt, however, that F1 was the best bot beating the forerunner (Arnold) by a large margin. F1 was also characterized by the least number of suicides. Note, however, that generally, the number of suicides is high for all the agents. Interestingly, despite the fact that F1 scored the best, it was Arnold who was gunned down the least often.. Notice also that F1, the winner of Track 1 of the previous competition, took the fifth place with 164 frags and is again characterized by the least number of suicides, which, in general, did not decrease compared to the 2016 competition and is still high for all the agents.. Similarly to the result of Track 1, the gap between the first two bots was tiny." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table II: Results of the 2016 Competition: Track 1. ‘Frags’ is the number of opponent kills decreased by the number of suicides of the agent. ‘F/D’ denotes Frags/Death. ‘Deaths’ include suicides.'\nProblem: 'Which bot had the highest F/D ratio and how many frags did it score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/55.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table II: Results of the 2016 Competition: Track 1. ‘Frags’ is the number of opponent kills decreased by the number of suicides of the agent. ‘F/D’ denotes Frags/Death. ‘Deaths’ include suicides." ], "reasoning": "Arnold had the highest F/D ratio of 1.90.", "question": "Which bot had the highest F/D ratio?", "answer": { "answer": "Arnold" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe bot with the least number of suicides had the highest number of frags.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/55.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table II: Results of the 2016 Competition: Track 1. ‘Frags’ is the number of opponent kills decreased by the number of suicides of the agent. ‘F/D’ denotes Frags/Death. ‘Deaths’ include suicides." ], "reasoning": "F1 had the least number of suicides (38) and the highest number of frags (559).", "statement": "The bot with the least number of suicides had the highest number of frags.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table III: Results of the 2016 Competition: Track 2. ‘M’ denotes map and ‘T’ denotes a total statistic.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/56.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table III: Results of the 2016 Competition: Track 2. ‘M’ denotes map and ‘T’ denotes a total statistic." ], "reasoning": "IntelAct outperformed all other bots in Track 2, while Arnold had the fewest deaths.", "description": "In Track 2, IntelAct was the best bot, significantly surpassing its competitors on all maps. Arnold, who finished in the second place, was killed the least frequently. Compared to Track 1, the numbers of kills and suicides (see Tables II and III) are significantly lower," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table III: Results of the 2016 Competition: Track 2. ‘M’ denotes map and ‘T’ denotes a total statistic.'\nProblem: 'Which bot had the highest total number of kills in Track 2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/56.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table III: Results of the 2016 Competition: Track 2. ‘M’ denotes map and ‘T’ denotes a total statistic." ], "reasoning": "To find the bot with the highest total number of kills in Track 2, sum the kills across all maps for each bot. IntelAct has the highest total with 297 kills.", "question": "Which bot had the highest total number of kills in Track 2?", "answer": "So, the answer is {\"answer\": \"IntelAct\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nArnold had the highest F/D ratio in Track 2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/56.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table III: Results of the 2016 Competition: Track 2. ‘M’ denotes map and ‘T’ denotes a total statistic." ], "reasoning": "The table shows that Arnold has an F/D ratio of 32.8, which is the highest among all bots listed.", "statement": "Arnold had the highest F/D ratio in Track 2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table V: Results of the 2017 Competition: Track 1. ‘F/D’ denotes Frags/Death. Deaths include suicides.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/57.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table V: Results of the 2017 Competition: Track 1. ‘F/D’ denotes Frags/Death. Deaths include suicides." ], "reasoning": "Marvin won Track 1 with 248 frags, focusing on gathering resources, while Arnold4 won Track 2 with 275 frags and no suicides.", "description": "The results of the competition were shown in Tables V and VI for Track 1 and 2, respectively.. The level of the submitted bots was significantly higher in 2017 than in 2016. There were no weak bots in Track 1. The spread of the frag count was rather small: the worst bot scored 109 while the best one 248. The track was won by Marvin, which scored 248 frags, only 3 more than the runner-up, Arnold2, and 33 more then Axon. Interestingly, Marvin did not stand out with his accuracy or ability to avoid rockets; it focused on gathering resources: medkits and armors, which greatly increased his chances of survival. Marvin was hit the largest number of times but, at the same time, it was killed the least frequently. Arnold2, on the other hand, was better at aiming (shooting and detection precision).. all could move, aim, and shoot opponents.. The competition was won by Arnold4, who scored 275 frags and was closely followed up by YanShi, who scored 273. Arnold4 was the most accurate bot in the whole competition and the only bot that did not commit any suicide. This turned out to be crucial to win against YanShi, who had the same number of 275 kills but committed two suicides. YanShi, however, achieved the highest frags/death ratio by being the best at avoiding being killed and had the highest detection precision. These two were definitely the best compared to the other agents. The next runner-up, IntelAct, the winner of Track 2 in the previous competition, scored substantially fewer, 221 frags. Fewer items on the maps in Track 2 possibly contributed to the lower position of Marvin, which ended up in the fourth place with 193 frags." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table V: Results of the 2017 Competition: Track 1. ‘F/D’ denotes Frags/Death. Deaths include suicides.'\nProblem: 'Which bot had the highest frags/death ratio in Track 1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/57.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table V: Results of the 2017 Competition: Track 1. ‘F/D’ denotes Frags/Death. Deaths include suicides." ], "reasoning": "To find the bot with the highest frags/death ratio, look at the 'F/D ratio' column and identify the highest value. Marvin has the highest F/D ratio of 1.16.", "question": "Which bot had the highest frags/death ratio in Track 1?", "answer": "So, the answer is {\"answer\": \"Marvin\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nArnold2 had more kills than Marvin in Track 1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/57.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table V: Results of the 2017 Competition: Track 1. ‘F/D’ denotes Frags/Death. Deaths include suicides." ], "reasoning": "The table shows that Marvin had 315 kills while Arnold2 had 314 kills. Therefore, Arnold2 did not have more kills than Marvin.", "statement": "Arnold2 had more kills than Marvin in Track 1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Mean cumulative reward per episode obtained by the agents on graphs with |V|=20, grouped by objective function, graph family, and number of edge additions L.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/58.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Mean cumulative reward per episode obtained by the agents on graphs with |V|=20, grouped by objective function, graph family, and number of edge additions L." ], "reasoning": "RNet–DQN policies outperform the greedy approach in both ER and BA cases for random objectives, and perform significantly better than random across all graph types and performance measures.", "description": "In Table 1, we present the main results of our experimental evaluation. For random, the policies learned by RNet–DQN yield solutions that outperform the greedy approach in both the ER and BA cases. Results for improving targeted are not as strong but still considerably better than random.. Across all types of graphs and performance measures, RNet– DQN performed statistically significantly better than random." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Mean cumulative reward per episode obtained by the agents on graphs with |V|=20, grouped by objective function, graph family, and number of edge additions L.'\nProblem: 'What is the best performance measure for RNet–DQN best in the F targeted ER case with L=10?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/58.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Mean cumulative reward per episode obtained by the agents on graphs with |V|=20, grouped by objective function, graph family, and number of edge additions L." ], "reasoning": "To find the best performance measure for RNet–DQN best in the F targeted ER case with L=10, look at the row corresponding to F targeted, ER, and L=10. The value under RNet–DQN best is 0.482.", "question": "What is the best performance measure for RNet–DQN best in the F targeted ER case with L=10?", "answer": "So, the answer is {\"answer\": \"0.482\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRNet–DQN avg performs better than Greedy in the F random BA case with L=5.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/58.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Mean cumulative reward per episode obtained by the agents on graphs with |V|=20, grouped by objective function, graph family, and number of edge additions L." ], "reasoning": "To verify this, compare the values for RNet–DQN avg and Greedy in the F random BA case with L=5. RNet–DQN avg has a value of 0.124±0.001, while Greedy has a value of 0.079. Since 0.124 is greater than 0.079, the statement is supported.", "statement": "RNet–DQN avg performs better than Greedy in the F random BA case with L=5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Testing Set Performance'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/59.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Testing Set Performance" ], "reasoning": "Translation estimations are mostly within 2 cm error for both x and y, while rotation estimations are usually correct or in the closest neighboring classes.", "description": "For translation estimation, most estimation fall within 2 cm error range for both x and y translations. For rotation estimation, the exactly correct class is obtained most of the time; most wrong estimations fall in closest neighboring classes." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Testing Set Performance'\nProblem: 'Which object has the highest error in y translation estimation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/59.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Testing Set Performance" ], "reasoning": "To find the object with the highest error in y translation estimation, compare the ey/cm values for all objects. The Black and white mug has the highest ey/cm value of 1.182.", "question": "Which object has the highest error in y translation estimation?", "answer": "So, the answer is {\"answer\": \"Black and white mug\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe purple detergent bottle has the lowest error in x translation estimation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/59.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Testing Set Performance" ], "reasoning": "To verify this statement, compare the ex/cm values for all objects. The purple detergent bottle has an ex/cm value of 1.029, which is the lowest among all objects.", "statement": "The purple detergent bottle has the lowest error in x translation estimation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Ensembles'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/60.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ensembles" ], "reasoning": "We matched the previous state of the art with 4,544 ensembles, surpassed it with 44 ensembles achieving 99.83% accuracy, and established a new state of the art of 99.84% with one ensemble.", "description": "We matched the previous state of the art with 4,544 ensembles. We surpassed this with 44 ensembles that achieved an accuracy of 99.83% and established a new state of the art of 99.84% with one ensemble. See Table 3." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Ensembles'\nProblem: 'How many ensembles achieved the new state of the art accuracy of 99.84%?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/60.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ensembles" ], "reasoning": "The table shows that 1 ensemble with 'Ones Init.' achieved the new state of the art accuracy of 99.84%.", "question": "How many ensembles achieved the new state of the art accuracy of 99.84%?", "answer": "So, the answer is {\"answer\": \"1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe previous state of the art accuracy was achieved with 4 ensembles.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/60.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ensembles" ], "reasoning": "The table shows that 4 ensembles with 'Not Learnable' achieved an accuracy of 99.83%, which matches the previous state of the art mentioned in the description.", "statement": "The previous state of the art accuracy was achieved with 4 ensembles.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Predictors trained and evaluated with the whole NASBench dataset and sub dataset. The experiments are repeated 20 times to alleviate the randomness of the results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/61.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Predictors trained and evaluated with the whole NASBench dataset and sub dataset. The experiments are repeated 20 times to alleviate the randomness of the results." ], "reasoning": "The sub dataset with 3 × 3 operation and skip-connect between input and output nodes improves the performance predictor's accuracy and ranking compared to the whole dataset.", "description": "we may form a better search space of NASBench dataset by using only 68552 models with 3 × 3 operation and skip-connect between input node and output node. An experiment of training and evaluating performance predictor is conducted on this sub dataset and the results show that the predictor trained and evaluated with sub dataset performs better than the previous one as shown in Tab. 7. It shows that a better search space helps to produce a better performance predictor." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Predictors trained and evaluated with the whole NASBench dataset and sub dataset. The experiments are repeated 20 times to alleviate the randomness of the results.'\nProblem: 'What is the accuracy of the performance predictor when trained and evaluated on the sub dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/61.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Predictors trained and evaluated with the whole NASBench dataset and sub dataset. The experiments are repeated 20 times to alleviate the randomness of the results." ], "reasoning": "The table shows the accuracy of the performance predictor for both the whole dataset and the sub dataset. The accuracy for the sub dataset is highlighted.", "question": "What is the accuracy of the performance predictor when trained and evaluated on the sub dataset?", "answer": "So, the answer is {\"answer\": \"94.02 ± 0.14\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe performance predictor trained on the sub dataset has a higher accuracy than the one trained on the whole dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/61.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Predictors trained and evaluated with the whole NASBench dataset and sub dataset. The experiments are repeated 20 times to alleviate the randomness of the results." ], "reasoning": "The table shows that the accuracy of the performance predictor for the sub dataset is 94.02 ± 0.14, which is higher than the accuracy for the whole dataset, which is 93.95 ± 0.11.", "statement": "The performance predictor trained on the sub dataset has a higher accuracy than the one trained on the whole dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Detailed results for problems of size B.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/62.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Detailed results for problems of size B." ], "reasoning": "The table shows the performance of optimization methods on shallow and deep networks, with shallow networks generally performing better. The cross-check blocks compare the performance of different network architectures and optimization methods.", "description": "The results for the optimization methods for problem size class B are given in Table 2.. The focus of this table is on showing the performance on shallow and deep networks for all optimization methods.. The first three blocks of the table show the optimization results for shallow and deep networks individually.. The following four blocks represent the cross check itself.. following four blocks represent the cross check itself. The column Network denotes the network architecture used for the applied optimization.. The column Data Source points to the architecture for which the training set has been generated, with a known error optimum of zero.. For the training runs presented in these rows, the optimum is not known.. It is only known that it would be zero for the architecture given in the column Data Source, but not necessarily also for the architecture of the column Network, used for the fitting.. For example, for the first of these sixteen rows, the network architecture trained is B1 (i.e., a shallow net with a single hidden layer).. It is optimized to fit the training set for which it is known that a zero error can be reached by the architecture B3 (i.e., a deep net with three hidden layers).. The column Ratio to CG displays how many times the error function value attained by the Keras methods was higher than that reached by the conjugate gradient.. The column Deep/Shallow shows the ratio of the following error function values for the deep network and the shallow network.. Shallow networks have been superior for all test problems and all optimizing algorithms.. However, it is interesting to observe that the gap, although always large, was relatively smaller for weakly performing optimization methods (SGD and Adadelta) as well as for large networks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Detailed results for problems of size B.'\nProblem: 'Which optimization method had the highest Ratio to CG for the B1 network architecture?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/62.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Detailed results for problems of size B." ], "reasoning": "To find the optimization method with the highest Ratio to CG for the B1 network architecture, look at the rows where the Network column is B1 and compare the values in the Ratio to CG column. The highest value is 7748.77, which corresponds to the SGD method.", "question": "Which optimization method had the highest Ratio to CG for the B1 network architecture?", "answer": "So, the answer is {\"answer\": \"SGD\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe CG optimization method always achieved the lowest Fopt value for all network architectures.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/62.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Detailed results for problems of size B." ], "reasoning": "To verify this statement, check the Fopt values for the CG method across all network architectures. The CG method consistently has the lowest Fopt value compared to other methods for each network architecture.", "statement": "The CG optimization method always achieved the lowest Fopt value for all network architectures.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results for all given problems and their ratio between shallow and deep networks.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/63.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for all given problems and their ratio between shallow and deep networks." ], "reasoning": "The table shows that shallow networks achieve lower mean square error minima compared to deep networks for problems where a zero MSE is attainable by deep networks, and this difference decreases with problem size.", "description": "The mean square error minima (MSE) attained by shallow networks for problems having a zero MSE for some deep network are essentially lower than in the opposite situation.. Additionally, Table 3 shows mean square minima for all size classes using the Keras optimization method RMSprop.. This table elucidates the development of the performance (MSE) with shallow and deep networks for varying network sizes.. Each row shows the performance of a pair of a shallow and a deep network with a comparable number of parameters.. The average performance of a shallow network for a problem for which a zero error minimum is known to be attainable by a deep network is given in the column Data deep – NN shallow.. The average performance of a deep network for a problem for which a zero error minimum is known to be attainable by a shallow network is given in the column Data shallow – NN deep.. The ratio of both average performances is shown in the column Ratio Deep/Shallow.. The difference tends to slightly decrease with the problem size.. The by far weakest method was the SGD, while the best was the conjugate gradient (CG).. Adadelta and RMSprop were performing between the both, with RMSprop sometimes approaching the CG performance.. The difference between the performance with a shallow network on one hand and deep network on the other hand grows with the performance of the optimization method: the difference is relatively small for the worst performing SGD and very large for the best performing CG.. The computing experiments seem to essentially show the superiority of shallow networks in attaining low mean square minima for given mapping problems." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Results for all given problems and their ratio between shallow and deep networks.'\nProblem: 'What is the mean square error (MSE) for a shallow network when a zero error minimum is known to be attainable by a deep network for the pair A1 and A3?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/63.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for all given problems and their ratio between shallow and deep networks." ], "reasoning": "The table shows the MSE for shallow networks in the column 'Data deep – NN shallow'. For the pair A1 and A3, the MSE is 0.038.", "question": "What is the mean square error (MSE) for a shallow network when a zero error minimum is known to be attainable by a deep network for the pair A1 and A3?", "answer": "So, the answer is {\"answer\": \"0.038\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ratio of average performances of deep to shallow networks is highest for the pair A1 and A5.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/63.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for all given problems and their ratio between shallow and deep networks." ], "reasoning": "The table shows the 'Ratio Deep/Shallow' for each pair. The highest ratio is 320.5 for the pair A1 and A5.", "statement": "The ratio of average performances of deep to shallow networks is highest for the pair A1 and A5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Normal KD and Re-KD experiment results on CIFAR100. We report mean±std (in %) over 3 runs. The number in parenthesis means increased accuracy over baseline (T: teacher, S: student).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/64.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Normal KD and Re-KD experiment results on CIFAR100. We report mean±std (in %) over 3 runs. The number in parenthesis means increased accuracy over baseline (T: teacher, S: student)." ], "reasoning": "Teacher models, especially ResNet18 and ResNet50, show significant improvement when learning from student models.", "description": "In Tab. 1, the teacher models are improved significantly by learning from students, especially for teacher models ResNet18 and ResNet50." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Normal KD and Re-KD experiment results on CIFAR100. We report mean±std (in %) over 3 runs. The number in parenthesis means increased accuracy over baseline (T: teacher, S: student).'\nProblem: 'Which teacher model shows the highest improvement in Normal KD when learning from MobileNetV2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/64.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Normal KD and Re-KD experiment results on CIFAR100. We report mean±std (in %) over 3 runs. The number in parenthesis means increased accuracy over baseline (T: teacher, S: student)." ], "reasoning": "Compare the improvement values in the Normal KD column for teacher models learning from MobileNetV2. The highest improvement is for DenseNet121 with an increase of 2.91.", "question": "Which teacher model shows the highest improvement in Normal KD when learning from MobileNetV2?", "answer": "So, the answer is {\"answer\": \"DenseNet121\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nResNet50 shows a higher improvement in Re-KD when learning from ShuffleNetV2 compared to MobileNetV2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/64.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Normal KD and Re-KD experiment results on CIFAR100. We report mean±std (in %) over 3 runs. The number in parenthesis means increased accuracy over baseline (T: teacher, S: student)." ], "reasoning": "Compare the Re-KD improvement values for ResNet50 learning from ShuffleNetV2 and MobileNetV2. The improvement for ShuffleNetV2 is 1.33, which is higher than the 1.14 improvement for MobileNetV2.", "statement": "ResNet50 shows a higher improvement in Re-KD when learning from ShuffleNetV2 compared to MobileNetV2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Re-KD experiment results (accuracy, mean±std over 3 runs in %) on CIFAR10.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/65.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Re-KD experiment results (accuracy, mean±std over 3 runs in %) on CIFAR10." ], "reasoning": "In some cases, Re-KD outperforms Normal KD, such as when MobileNetV2 is the teacher and plain CNN is the student, or when ResNeXt29 is the teacher and ResNet18 is the student.", "description": "However, in some cases, we can find Re-KD outperforms Normal KD. For instance, in Tab. 2 (3rd row), the student model (plain CNN) can only be improved by 0.31% when taught by MobileNetV2, but the teacher (MobileNetV2) can be improved by 0.92% by learning from the student. We have similar observations for ResNeXt29 and ResNet18 (4th row in Tab. 2)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Re-KD experiment results (accuracy, mean±std over 3 runs in %) on CIFAR10.'\nProblem: 'What is the improvement percentage of MobileNetV2 as a teacher using Normal KD compared to Re-KD when the student is plain CNN?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/65.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Re-KD experiment results (accuracy, mean±std over 3 runs in %) on CIFAR10." ], "reasoning": "MobileNetV2 as a teacher improves by 0.31% using Normal KD when the student is plain CNN.", "question": "What is the improvement percentage of MobileNetV2 as a teacher using Normal KD when the student is plain CNN?", "answer": "So, the answer is {\"answer\": \"0.31%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRe-KD improves the performance of ResNeXt29 as a teacher more than Normal KD when the student is ResNet18.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/65.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Re-KD experiment results (accuracy, mean±std over 3 runs in %) on CIFAR10." ], "reasoning": "Re-KD improves ResNeXt29's performance by 0.73%, while Normal KD improves it by 0.68% when the student is ResNet18.", "statement": "Re-KD improves the performance of ResNeXt29 as a teacher more than Normal KD when the student is ResNet18.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: De-KD accuracy (in %) on two datasets. Pt-Teacher is “Poorly-trained Teacher”. Refer to the “Normal KD” in Tabs. 1 to 3 for the accuracy of students taught by “fully-trained teacher”.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/66.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: De-KD accuracy (in %) on two datasets. Pt-Teacher is “Poorly-trained Teacher”. Refer to the “Normal KD” in Tabs. 1 to 3 for the accuracy of students taught by “fully-trained teacher”." ], "reasoning": "The student model can achieve significant performance improvements even when distilled by a poorly-trained teacher, as seen in both CIFAR100 and Tiny-ImageNet datasets.", "description": "From De-KD experiment results on CIFAR100 in Tab. 4, we observe that the student can be greatly promoted even when distilled by a poorly-trained teacher.. From the De-KD experiment results on Tiny-ImageNet in Tab. 4, we find ResNet18 with 9.14% accuracy can still enhance the teacher model MobileNetV2 by 1.16%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: De-KD accuracy (in %) on two datasets. Pt-Teacher is “Poorly-trained Teacher”. Refer to the “Normal KD” in Tabs. 1 to 3 for the accuracy of students taught by “fully-trained teacher”.'\nProblem: 'What is the accuracy improvement of MobileNetV2 on CIFAR100 when distilled by ResNet50?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/66.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: De-KD accuracy (in %) on two datasets. Pt-Teacher is “Poorly-trained Teacher”. Refer to the “Normal KD” in Tabs. 1 to 3 for the accuracy of students taught by “fully-trained teacher”." ], "reasoning": "From the table, for CIFAR100 with ResNet50 as the teacher and MobileNetV2 as the student, the De-KD accuracy is 71.45% with an improvement of 3.09%.", "question": "What is the accuracy improvement of MobileNetV2 on CIFAR100 when distilled by ResNet50?", "answer": "So, the answer is {\"answer\": \"3.09%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nResNet18 with 9.14% accuracy can enhance MobileNetV2 by 4.16% on Tiny-ImageNet.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/66.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: De-KD accuracy (in %) on two datasets. Pt-Teacher is “Poorly-trained Teacher”. Refer to the “Normal KD” in Tabs. 1 to 3 for the accuracy of students taught by “fully-trained teacher”." ], "reasoning": "The table shows that on Tiny-ImageNet, ResNet18 with 9.14% accuracy improves MobileNetV2 by 4.16%, which supports the statement.", "statement": "ResNet18 with 9.14% accuracy can enhance MobileNetV2 by 4.16% on Tiny-ImageNet.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Tf-KDself experiment results on Tiny-ImageNet (in %).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/67.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Tf-KDself experiment results on Tiny-ImageNet (in %)." ], "reasoning": "ResNet18 achieves a 0.56% improvement when taught by DenseNet121 on ImageNet, which is comparable to the self-training implementation shown in Table 7.", "description": "As a comparison, we also use DenseNet121 to teach ResNet18 on ImageNet, and ResNet18 obtains 0.56% improvement, which is comparable with our self-training implementation (Tab. 7)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Tf-KDself experiment results on Tiny-ImageNet (in %).'\nProblem: 'What is the improvement percentage of ResNet50 when using Tf-KD self-training compared to its baseline?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/67.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Tf-KDself experiment results on Tiny-ImageNet (in %)." ], "reasoning": "The baseline score for ResNet50 is 67.47. Using Tf-KD self-training, the score improves to 68.18, which is an improvement of 0.71%.", "question": "What is the improvement percentage of ResNet50 when using Tf-KD self-training compared to its baseline?", "answer": "So, the answer is {\"answer\": \"0.71%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDenseNet121 achieves a higher improvement percentage with Tf-KD self-training compared to Normal KD.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/67.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Tf-KDself experiment results on Tiny-ImageNet (in %)." ], "reasoning": "DenseNet121 improves by 0.14% with Tf-KD self-training and by 0.16% with Normal KD, so the statement is refuted.", "statement": "DenseNet121 achieves a higher improvement percentage with Tf-KD self-training compared to Normal KD.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Tf-KDself experiment results on ImageNet (Top1 accuracy, in %).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/68.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Tf-KDself experiment results on ImageNet (Top1 accuracy, in %)." ], "reasoning": "Tf-KDreg achieves comparable performance to Normal KD on CIFAR100 and Tiny-ImageNet without using a teacher, just by adding a regularization term.", "description": "From Tab. 8 and Tab. 9, we can observe with no teacher used and just a regularization term added, Tf-KDreg achieves comparable performance with Normal KD on both CIFAR100 and Tiny-ImageNet." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Tf-KDself experiment results on ImageNet (Top1 accuracy, in %).'\nProblem: 'What is the performance improvement of Tf-KD self over the baseline for ResNet50?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/68.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Tf-KDself experiment results on ImageNet (Top1 accuracy, in %)." ], "reasoning": "The table shows the baseline performance of ResNet50 as 75.77 and the Tf-KD self performance as 76.41, indicating an improvement of 0.64.", "question": "What is the performance improvement of Tf-KD self over the baseline for ResNet50?", "answer": "So, the answer is {\"answer\": \"0.64\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTf-KD self improves the performance of ResNet18 by more than 0.5 points.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/68.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Tf-KDself experiment results on ImageNet (Top1 accuracy, in %)." ], "reasoning": "The table shows that the baseline performance of ResNet18 is 69.84 and the Tf-KD self performance is 70.42, indicating an improvement of 0.58 points, which is more than 0.5 points.", "statement": "Tf-KD self improves the performance of ResNet18 by more than 0.5 points.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Classification performances on the lane change dataset. Our method, smt+cnn+lstm, achieved the best AUC score.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/69.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Classification performances on the lane change dataset. Our method, smt+cnn+lstm, achieved the best AUC score." ], "reasoning": "The best performing architecture was SMT+CNN+LSTM with a Mask R-CNN backbone, achieving the highest AUC score, while architectures with LSTM layers generally outperformed their spatial counterparts.", "description": "Table. summarizes the experimental results, where network architectures are shown in the first column. The backbone model column indicates the base transferred very deep network if there was any. Not all architectures used transfer learning, namely FbF CNN and CNN+LSTM. Datasets that the transferred networks were pre-trained on are given in column three. It should be noted again that all architectures were trained with our data for the final classification task. The total number of network parameters for each architecture is shown in column four for assessing the computational load. Lower parameter amount correlates with faster inference time\\. , the fixed number of frames, were changed between 5 to 100 for each configuration. The best scoring. in terms of AUC of each row is given in column five. The final column is the AUC score, the main performance metric of this study. All spatiotemporal architectures with an LSTM layer outperformed their spatial counterparts, except the configuration with the Inceptionresnet base model, which had the lowest performance. These results underline the importance of the temporal dimension. However, a very large dependence on temporal information is also undesired because; it swells the network, increases the input data size and slows the inference time.. The best result was obtained with the proposed SMT+CNN+LSTM framework which used a Mask R-CNN semantic mask extractor. We believe this result was due to the masked-contrasted temporal compositions' aptitude for relaying semantic information. The third best result was obtained with an FT+LSTM architecture which used ResNet50 as its backbone model. The rest of the architectures fell behind the top three by a noticeable margin. For example, the proposed SMT+CNN+LSTM's risky lane change detection performance was 25% better than the FbF FT with an Xception backbone." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Classification performances on the lane change dataset. Our method, smt+cnn+lstm, achieved the best AUC score.'\nProblem: 'Which architecture had the highest AUC score and what was its backbone model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/69.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Classification performances on the lane change dataset. Our method, smt+cnn+lstm, achieved the best AUC score." ], "reasoning": "The table shows that the SMT+CNN+LSTM architecture with a Mask R-CNN backbone had the highest AUC score of 0.937.", "question": "Which architecture had the highest AUC score and what was its backbone model?", "answer": { "answer": "SMT+CNN+LSTM with Mask R-CNN" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe architecture with the lowest AUC score used the Inceptionresnet backbone model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/69.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Classification performances on the lane change dataset. Our method, smt+cnn+lstm, achieved the best AUC score." ], "reasoning": "The table indicates that the FT+LSTM architecture with the Inceptionresnet backbone model had the lowest AUC score of 0.5.", "statement": "The architecture with the lowest AUC score used the Inceptionresnet backbone model.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Performance of RoadTagger and different CNN image classifier baselines. In the table, we highlight both the best and the second best results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/70.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of RoadTagger and different CNN image classifier baselines. In the table, we highlight both the best and the second best results." ], "reasoning": "RoadTagger significantly improves the accuracy of lane count and road type inference compared to CNN image classifier baselines, while also reducing the absolute lane detection error.", "description": "We report the overall accuracy of the two types of road attributes and the absolute lane error for the CNN image classifier baselines and RoadTagger in table 1.. As shown in the table, RoadTagger surpasses all the CNN image classifier based baselines. Compared with the baseline using only CNN image classifier, RoadTagger improves the inference accuracy of the number of lanes from 71.8% to 77.2%, and of the road type from 89.1% to 93.1%. This improvement comes with a reduction of the absolute lane detection error of 22.2%. Compared with the best baseline (with MRF post-processing and 1.5x larger receptive field), RoadTagger still improves the accuracy of the lane count inference by 3.1 points, which comes with a reduction of the absolute lane detection error of 14.4%, and achieves similar accuracy in road type inference." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Performance of RoadTagger and different CNN image classifier baselines. In the table, we highlight both the best and the second best results.'\nProblem: 'What is the absolute lane detection error reduction achieved by RoadTagger compared to the baseline using only CNN image classifier?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/70.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of RoadTagger and different CNN image classifier baselines. In the table, we highlight both the best and the second best results." ], "reasoning": "The baseline using only CNN image classifier has an absolute lane detection error (ALE) of 0.374, while RoadTagger achieves an ALE of 0.291. The reduction is calculated as 0.374 - 0.291 = 0.083, which is a 22.2% reduction.", "question": "What is the absolute lane detection error reduction achieved by RoadTagger compared to the baseline using only CNN image classifier?", "answer": { "answer": "22.2%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRoadTagger improves the accuracy of the number of lanes by 5.4 points compared to the baseline using only CNN image classifier.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/70.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of RoadTagger and different CNN image classifier baselines. In the table, we highlight both the best and the second best results." ], "reasoning": "The baseline using only CNN image classifier has a lane accuracy of 71.8%, while RoadTagger achieves a lane accuracy of 77.2%. The improvement is 77.2% - 71.8% = 5.4 points.", "statement": "RoadTagger improves the accuracy of the number of lanes by 5.4 points compared to the baseline using only CNN image classifier.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Impact of different graph structures used in RoadTagger. Here, we use abbreviations to denote different graphs. We use Raw for the original road network graph, Road for the road extraction graph, Road(D) for the road extraction graph with directional decomposition and Aux for the auxiliary graph for parallel roads.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/71.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Impact of different graph structures used in RoadTagger. Here, we use abbreviations to denote different graphs. We use Raw for the original road network graph, Road for the road extraction graph, Road(D) for the road extraction graph with directional decomposition and Aux for the auxiliary graph for parallel roads." ], "reasoning": "Adding more restrictions into the graph structure improves performance, as seen with the Road(D) graph outperforming the Raw graph. The combination of Raw, Road(D), and Aux graphs yields the best performance.", "description": "We show results of RoadTagger with different graph structures in Table 2.. For a single graph structure, we find adding more restrictions into the graph structure can yield better performance, e.g., the performance of using road extraction graph is better than the performance of using the original raw road network graph.. We find using the combination of the Raw graph, Road(D) graph and Aux graph can yield better performance compared with the performance of using a single graph structure." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Impact of different graph structures used in RoadTagger. Here, we use abbreviations to denote different graphs. We use Raw for the original road network graph, Road for the road extraction graph, Road(D) for the road extraction graph with directional decomposition and Aux for the auxiliary graph for parallel roads.'\nProblem: 'What is the ALE value when using the combination of Raw, Road(D), and Aux graphs?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/71.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Impact of different graph structures used in RoadTagger. Here, we use abbreviations to denote different graphs. We use Raw for the original road network graph, Road for the road extraction graph, Road(D) for the road extraction graph with directional decomposition and Aux for the auxiliary graph for parallel roads." ], "reasoning": "The table shows that the ALE value for the combination of Raw, Road(D), and Aux graphs is 0.291, which is the lowest ALE value among the listed schemes.", "question": "What is the ALE value when using the combination of Raw, Road(D), and Aux graphs?", "answer": "So, the answer is {\"answer\": \"0.291\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUsing the Road(D) graph alone yields a better ALE value than using the Raw graph.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/71.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Impact of different graph structures used in RoadTagger. Here, we use abbreviations to denote different graphs. We use Raw for the original road network graph, Road for the road extraction graph, Road(D) for the road extraction graph with directional decomposition and Aux for the auxiliary graph for parallel roads." ], "reasoning": "The table shows that the ALE value for the Road(D) graph is 0.324, which is lower than the ALE value of 0.332 for the Raw graph, indicating better performance.", "statement": "Using the Road(D) graph alone yields a better ALE value than using the Raw graph.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Impact of random vertex dropout and graph Laplace regularization.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/72.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Impact of random vertex dropout and graph Laplace regularization." ], "reasoning": "Random vertex dropout significantly impacts lane inference, while graph Laplace regularization has a greater effect on road type inference.", "description": "We show the comparison results in Table 3. We find both of these two techniques are critical to the performance improvement of. the random vertex dropout has more impact on the number of lane inference and the graph Laplace regularization has more impact on the road type inference." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Impact of random vertex dropout and graph Laplace regularization.'\nProblem: 'Which technique has the most impact on the number of lane inference?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/72.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Impact of random vertex dropout and graph Laplace regularization." ], "reasoning": "Comparing the 'No Vertex Dropout' and 'No Regularization' schemes with the 'RoadTagger' scheme, the 'No Vertex Dropout' scheme shows a larger decrease in the number of lane inference, indicating that random vertex dropout has the most impact.", "question": "Which technique has the most impact on the number of lane inference?", "answer": "So, the answer is {\"answer\": \"random vertex dropout\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGraph Laplace regularization has a greater impact on road type inference than random vertex dropout.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/72.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Impact of random vertex dropout and graph Laplace regularization." ], "reasoning": "The table shows that the 'No Regularization' scheme has a lower road type inference percentage (90.8%) compared to the 'No Vertex Dropout' scheme (92.7%), indicating that graph Laplace regularization has a greater impact on road type inference.", "statement": "Graph Laplace regularization has a greater impact on road type inference than random vertex dropout.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: FID Scores [13] over 10 iterations for 4000 randomly generated samples when the training data is contaminated with 20% outliers. (smaller is better).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/73.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: FID Scores [13] over 10 iterations for 4000 randomly generated samples when the training data is contaminated with 20% outliers. (smaller is better)." ], "reasoning": "The robust training procedure improves the generation quality by not encoding noisy images, as evidenced by the lower FID scores in the Ro. Gen-RKM column for all datasets.", "description": "The images are generated by random sampling from a fitted Gaussian distribution on the learned latent variables. When using a robust training procedure, the model does not encode the noisy images. As a consequence, no noisy images are generated and the generation quality is considerably better. This is confirmed by the Frechet Inception Distance (FID) scores [43] in Table 1, which quantify the quality of generation. We repeat the above experiment on the Fashion-MNIST dataset, where only the FID scores are reported in Table 1." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: FID Scores [13] over 10 iterations for 4000 randomly generated samples when the training data is contaminated with 20% outliers. (smaller is better).'\nProblem: 'Which dataset shows the greatest improvement in FID score when using the robust training procedure compared to the VAE?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/73.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: FID Scores [13] over 10 iterations for 4000 randomly generated samples when the training data is contaminated with 20% outliers. (smaller is better)." ], "reasoning": "To find the dataset with the greatest improvement, compare the FID scores of the VAE and Ro. Gen-RKM for each dataset. The difference is largest for the F-MNIST dataset, with a reduction from 245.84 to 153.32.", "question": "Which dataset shows the greatest improvement in FID score when using the robust training procedure compared to the VAE?", "answer": "So, the answer is {\"answer\": \"F-MNIST\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe FID score for the SVHN dataset using the robust training procedure is lower than using the RKM.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/73.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: FID Scores [13] over 10 iterations for 4000 randomly generated samples when the training data is contaminated with 20% outliers. (smaller is better)." ], "reasoning": "The FID score for the SVHN dataset using the robust training procedure (Ro. Gen-RKM) is 98.14, which is lower than the FID score using the RKM, which is 112.45.", "statement": "The FID score for the SVHN dataset using the robust training procedure is lower than using the RKM.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Disentanglement Metric on DSprites and 3D Shapes dataset. The training subset is contaminated with extra generating factors (20% of the data is considered as outliers). The framework of [9] with Lasso and Random Forest regressor [9] is used to evaluate the learned representation. For disentanglement and completeness higher score is better, for informativeness, lower is better.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/74.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Disentanglement Metric on DSprites and 3D Shapes dataset. The training subset is contaminated with extra generating factors (20% of the data is considered as outliers). The framework of [9] with Lasso and Random Forest regressor [9] is used to evaluate the learned representation. For disentanglement and completeness higher score is better, for informativeness, lower is better." ], "reasoning": "The robust method outperforms the Gen-RKM in disentanglement, completeness, and informativeness across different datasets and dimensions.", "description": "The disentanglement of the latent representation is measured quantitatively using the proposed framework† of , which consists out of 3 measures: disentanglement, completeness and informativeness. The results are shown in Table 2, where the robust method outperforms the Gen-RKM.. We only show the quantitative results in Table 2." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Disentanglement Metric on DSprites and 3D Shapes dataset. The training subset is contaminated with extra generating factors (20% of the data is considered as outliers). The framework of [9] with Lasso and Random Forest regressor [9] is used to evaluate the learned representation. For disentanglement and completeness higher score is better, for informativeness, lower is better.'\nProblem: 'Which algorithm has the highest Lasso Disentanglement score for the 3DShapes dataset with hdim 3?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/74.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Disentanglement Metric on DSprites and 3D Shapes dataset. The training subset is contaminated with extra generating factors (20% of the data is considered as outliers). The framework of [9] with Lasso and Random Forest regressor [9] is used to evaluate the learned representation. For disentanglement and completeness higher score is better, for informativeness, lower is better." ], "reasoning": "To find the highest Lasso Disentanglement score for the 3DShapes dataset with hdim 3, we look at the rows where the dataset is 3DShapes and hdim is 3. The scores are 0.24 for β-VAE (β=3), 0.14 for Gen-RKM, and 0.47 for Ro. Gen-RKM. The highest score is 0.47 for Ro. Gen-RKM.", "question": "Which algorithm has the highest Lasso Disentanglement score for the 3DShapes dataset with hdim 3?", "answer": "So, the answer is {\"answer\": \"Ro. Gen-RKM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Gen-RKM algorithm has the highest Random Forest Completeness score for the DSprites dataset with hdim 2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/74.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Disentanglement Metric on DSprites and 3D Shapes dataset. The training subset is contaminated with extra generating factors (20% of the data is considered as outliers). The framework of [9] with Lasso and Random Forest regressor [9] is used to evaluate the learned representation. For disentanglement and completeness higher score is better, for informativeness, lower is better." ], "reasoning": "To verify this statement, we look at the Random Forest Completeness scores for the DSprites dataset with hdim 2. The scores are 0.32 for β-VAE (β=3), 0.27 for Gen-RKM, and 0.38 for Ro. Gen-RKM. The highest score is 0.38 for Ro. Gen-RKM, not Gen-RKM.", "statement": "The Gen-RKM algorithm has the highest Random Forest Completeness score for the DSprites dataset with hdim 2.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Ablative analysis.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/75.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Ablative analysis." ], "reasoning": "Each improvement on SalsaNet uniquely enhances accuracy, with the highest accuracy of 54.5% achieved using 23.26M parameters, significantly fewer than RangeNet++'s 50M parameters.", "description": "In this ablative analysis, we investigate the individual contribution of each improvements over the original SalsaNet model. Table II shows the total number of model parameters together with the obtained mIoU scores on the SemanticKITTI test set before and after applying the kNN-based post processing step (see section III-E).. As depicted in table II, each operation on SalsaNet has a unique improvement on the accuracy. The post processing step leads to certain jump in the accuracy. The highest jump in the model parameters is observed when the new layers are inserted. We can achieve the highest accuracy score of 54.5% by having almost 50% less model parameters (i.e. 23.26M) in contrast to RangeNet++ which has 50M parameters. Overall improvement on the original SalsaNet model is more than 10% (54.5% versus 44.2%)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Ablative analysis.'\nProblem: 'What is the mean IoU score after applying the kNN-based post-processing step when the new dropout is added to SalsaNet?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/75.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Ablative analysis." ], "reasoning": "The table shows that the mean IoU score after applying the kNN-based post-processing step with the new dropout is 52.0.", "question": "What is the mean IoU score after applying the kNN-based post-processing step when the new dropout is added to SalsaNet?", "answer": "So, the answer is {\"answer\": \"52.0\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe highest accuracy improvement in SalsaNet is achieved with the addition of the context module.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/75.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Ablative analysis." ], "reasoning": "The table shows that the highest accuracy improvement in SalsaNet is achieved with the addition of the Lovász-Softmax loss, not the context module.", "statement": "The highest accuracy improvement in SalsaNet is achieved with the addition of the context module.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Runtime performance on the Semantic-KITTI dataset [2]'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/76.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Runtime performance on the Semantic-KITTI dataset [2]" ], "reasoning": "SalsaNext exhibits better performance compared to RangeNet++ with a lower standard deviation, indicating more stability, and has comparable performance to SalsaNet.", "description": "Runtime performance is crucial in autonomous driving. Table III shows the single forward pass runtime performance of SalsaNext in comparison to the other networks. For a fair comparison, we measure the inference time of all scans in a sample test scenario (sequence 13) using the same NVIDIA Quadro P6000-24GB GPU card.. Obtained mean runtime. values and standard deviations are reported in Table III. The propsed SalsaNext clearly exhibits better performance compared to the RangeNet++ and has a comparable performance with the original SalsaNet model. Note also that the standard deviation of the SalsaNext runtime is also relatively less than the RangeNet++ , which plays a crucial role in the stability of the self-driving perception modules. Recall also Fig. 1 which shows the overall runtime versus accuracy plot for the state-of-the-art methods and SalsaNext. Consequently, our proposed SalsaNext network inference (a single forward pass) time can reach up to 83 Hz while providing the highest accuracy 54.5%. We here emphasize that this achieved high speed is significantly faster than the sampling rate of mainstream LiDAR scanners which typically work at 10Hz." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Runtime performance on the Semantic-KITTI dataset [2]'\nProblem: 'What is the mean runtime performance of SalsaNext in comparison to RangeNet++?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/76.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Runtime performance on the Semantic-KITTI dataset [2]" ], "reasoning": "The mean runtime performance of SalsaNext is 12.00 msec, while the mean runtime performance of RangeNet++ is 14.27 msec, indicating that SalsaNext has a better mean runtime performance.", "question": "What is the mean msec runtime performance of SalsaNext?", "answer": "So, the answer is {\"answer\": \"12.00\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSalsaNext has a higher standard deviation than RangeNet++.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/76.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Runtime performance on the Semantic-KITTI dataset [2]" ], "reasoning": "The standard deviation of SalsaNext is 1.99 msec, while the standard deviation of RangeNet++ is 2.86 msec, indicating that SalsaNext has a lower standard deviation.", "statement": "SalsaNext has a higher standard deviation than RangeNet++.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Krippendorf’s α under different perturbation functions for the saliency metrics, measured using saliency method ranking across images. Numbers in brackets are 99.9% confidence intervals estimated with 10,000 bootstrap samples.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/77.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Krippendorf’s α under different perturbation functions for the saliency metrics, measured using saliency method ranking across images. Numbers in brackets are 99.9% confidence intervals estimated with 10,000 bootstrap samples." ], "reasoning": "The Krippendorf α statistics indicate low inter-rater reliability for saliency metrics, with the highest value being 0.48 for AOPCMoRF at L=100 using mean perturbation and including edge detection.", "description": "Table 1 lists the Krippendorf α statistics for the saliency metrics (with varying numbers of deleted pixels L for AOPC). These are calculated on the image-wise rankings of saliency maps, and test \"inter-rater reliability\" as described above. Low α values indicate that the saliency method rankings on different images are inconsistent. The left two columns of table 1 show α values when the baseline edge detection method is included in the ranking. This baseline is ranked more consistently (low rankings for AOPCMoRF and F, high rankings for AOPCLeRF) than the true saliency methods over all data set images, producing higher α values than when it is excluded from the rankings (the right-hand two columns).. Perturbing with random RGB values reduces α for Fidelity and AOPCMoRF compared with mean perturbation, which may be due to the increased stochasticity in the perturbations from the random colour choices. However, it is difficult to understand why AOPCLeRF at smaller numbers of perturbation steps (L = 20 and L = 40) produce greater α values with random RGB perturbation that with mean perturbation. A \"low\" α value is not strictly defined, but α < 0.65 are often considered to indicate unreliability inter-rater reliability. The largest value in the table, 0.48 for AOPCMoRF at L = 100 using mean perturbation and including edge detection in the ranking, is substantially less than 0.65, indicating low inter-rater reliability whatever the specifics of the metric. We can conclude from this that the metrics will not produce consistent rankings of saliency maps when applied to new test images." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Krippendorf’s α under different perturbation functions for the saliency metrics, measured using saliency method ranking across images. Numbers in brackets are 99.9% confidence intervals estimated with 10,000 bootstrap samples.'\nProblem: 'What is the Krippendorf α value for AOPCMoRF at L=100 using random perturbation and excluding edge detection?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/77.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Krippendorf’s α under different perturbation functions for the saliency metrics, measured using saliency method ranking across images. Numbers in brackets are 99.9% confidence intervals estimated with 10,000 bootstrap samples." ], "reasoning": "To find the Krippendorf α value for AOPCMoRF at L=100 using random perturbation and excluding edge detection, locate the corresponding row and column in the table. The value is 0.13.", "question": "What is the Krippendorf α value for AOPCMoRF at L=100 using random perturbation and excluding edge detection?", "answer": "So, the answer is {\"answer\": \"0.13\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Krippendorf α value for AOPCLeRF at L=20 using mean perturbation and including edge detection is 0.05.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/77.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Krippendorf’s α under different perturbation functions for the saliency metrics, measured using saliency method ranking across images. Numbers in brackets are 99.9% confidence intervals estimated with 10,000 bootstrap samples." ], "reasoning": "To verify this statement, locate the row for AOPCLeRF at L=20 and the column for mean perturbation with edge detection. The value is 0.05.", "statement": "The Krippendorf α value for AOPCLeRF at L=20 using mean perturbation and including edge detection is 0.05.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Spearman correlations between pairs of metrics, measured over all images, for each saliency method. AOPC was taken at L=100 perturbation steps. Unless otherwise stated, mean perturbation was used.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/78.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Spearman correlations between pairs of metrics, measured over all images, for each saliency method. AOPC was taken at L=100 perturbation steps. Unless otherwise stated, mean perturbation was used." ], "reasoning": "The correlation values indicate that the metrics generally do not measure the same underlying quantity across different saliency methods, with Faithfulness weakly correlated with AOPCMoRF and slightly anti-correlated with AOPCLeRF.", "description": "Finally, we assess internal consistency reliability by measuring the correlation between different metrics over the data. set images, for each saliency method. A selection of pairs of correlations for different metrics is shown in table 2. The correlation values indicate that the metrics are, in general, not measuring the same underlying quantity across the different saliency methods. Faithfulness is only weakly correlated with AOPCMoRF, and in fact slightly anti-correlated with AOPCLeRF across all saliency methods. The correlation between AOPCMoRF and AOPCLeRF is highly variable across saliency methods, while the most consistent correlations across methods are between AOPCMoRF applied with random RGB versus mean perturbation. This is to be expected as both methods perturb the same pixels — though there is still reasonably high variability even in this case (from ρ = 0.58 for deep Taylor decomposition to ρ = 0.77 for SHAP)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Spearman correlations between pairs of metrics, measured over all images, for each saliency method. AOPC was taken at L=100 perturbation steps. Unless otherwise stated, mean perturbation was used.'\nProblem: 'Which saliency method shows the highest correlation between AOPCMoRF applied with random RGB perturbation and AOPCMoRF applied with mean perturbation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/78.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Spearman correlations between pairs of metrics, measured over all images, for each saliency method. AOPC was taken at L=100 perturbation steps. Unless otherwise stated, mean perturbation was used." ], "reasoning": "To find the highest correlation between AOPCMoRF applied with random RGB perturbation and AOPCMoRF applied with mean perturbation, look at the last row of the table and identify the highest value.", "question": "Which saliency method shows the highest correlation between AOPCMoRF applied with random RGB perturbation and AOPCMoRF applied with mean perturbation?", "answer": "So, the answer is {\"answer\": \"SHAP\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFaithfulness is highly correlated with AOPCMoRF across all saliency methods.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/78.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Spearman correlations between pairs of metrics, measured over all images, for each saliency method. AOPC was taken at L=100 perturbation steps. Unless otherwise stated, mean perturbation was used." ], "reasoning": "The table shows that Faithfulness is weakly correlated with AOPCMoRF, with correlation values ranging from 0.00 to 0.34, which does not indicate a high correlation.", "statement": "Faithfulness is highly correlated with AOPCMoRF across all saliency methods.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Accuracy improvements of our approach and the naïve Cholesky decomposition for the 5D Levy function.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/79.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy improvements of our approach and the naïve Cholesky decomposition for the 5D Levy function." ], "reasoning": "The optimized Cholesky decomposition method converges to the global maximum in 611 iterations with 1 seed point, while with 100 seed points, it still converges but requires more iterations.", "description": "The second important aspect of every hyperparameter optimization method is the time to convergence. Thus, we make 1000 iterations with both methods and evaluate whether and if so when the optimal solution, i.e. f (x) = 0 is. approximated reasonable well; see Tab. 1.. In the first one, we provided only 1 random seed and continued for 1000 iterations of the optimization procedure. In the second one, we generated 100 random seeds and used this as a broad initialization for the remaining 1000 iterations.. our approach converges to the global maximum in 611 iterations. In the scenario of 100 seed points, our approach still converges to the optimal parameter setting, but needs more iterations" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Accuracy improvements of our approach and the naïve Cholesky decomposition for the 5D Levy function.'\nProblem: 'How many iterations does the optimized Cholesky decomposition method take to converge with 100 seed points?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/79.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy improvements of our approach and the naïve Cholesky decomposition for the 5D Levy function." ], "reasoning": "The table shows that the optimized Cholesky decomposition method with 100 seed points converges in 975 iterations.", "question": "How many iterations does the optimized Cholesky decomposition method take to converge with 100 seed points?", "answer": "So, the answer is {\"answer\": \"975\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe optimized Cholesky decomposition method with 1 seed point converges faster than with 100 seed points.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/79.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy improvements of our approach and the naïve Cholesky decomposition for the 5D Levy function." ], "reasoning": "The table shows that the optimized Cholesky decomposition method with 1 seed point converges in 611 iterations, while with 100 seed points, it converges in 975 iterations.", "statement": "The optimized Cholesky decomposition method with 1 seed point converges faster than with 100 seed points.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Accuracy improvements of our approach and the naïve Cholesky decomposition for LeNet5 and MNIST.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/80.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy improvements of our approach and the naïve Cholesky decomposition for LeNet5 and MNIST." ], "reasoning": "The optimized Cholesky decomposition method finds the optimal solution in significantly fewer iterations (168) compared to the naïve approach (732), while achieving the same accuracy of 0.97.", "description": "Hence, we also compared both approaches in terms of test accuracy; see Tab. 2.. Indeed, our method can find the optimal solution in 168 iterations, while the naïve approach needs 732 optimization steps." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Accuracy improvements of our approach and the naïve Cholesky decomposition for LeNet5 and MNIST.'\nProblem: 'How many iterations does the optimized Cholesky decomposition method take to achieve an accuracy of 0.96?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/80.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy improvements of our approach and the naïve Cholesky decomposition for LeNet5 and MNIST." ], "reasoning": "The table shows that the optimized Cholesky decomposition method achieves an accuracy of 0.96 in 11 iterations.", "question": "How many iterations does the optimized Cholesky decomposition method take to achieve an accuracy of 0.96?", "answer": { "answer": "11" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe naïve Cholesky decomposition method achieves an accuracy of 0.90 in 63 iterations.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/80.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy improvements of our approach and the naïve Cholesky decomposition for LeNet5 and MNIST." ], "reasoning": "The table indicates that the naïve Cholesky decomposition method achieves an accuracy of 0.90 in 63 iterations.", "statement": "The naïve Cholesky decomposition method achieves an accuracy of 0.90 in 63 iterations.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Accuracy improvements of our approach and the naïve Cholesky decomposition for ResNet32 and CIFAR10.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/81.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy improvements of our approach and the naïve Cholesky decomposition for ResNet32 and CIFAR10." ], "reasoning": "The optimized Cholesky decomposition achieves an accuracy of 0.81 after 237 iterations, whereas the naïve approach takes 176 iterations to reach an accuracy of 0.79.", "description": "our approach identifies a better parameter setting resulting in an accuracy of 0.81 after 10 epochs, see Tab. 3. We reach a competing parameter setting to the naïve approach on average already after 62 iterations in 194.2min - the original algorithm takes on average 176 iterations with a duration of 567min," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Accuracy improvements of our approach and the naïve Cholesky decomposition for ResNet32 and CIFAR10.'\nProblem: 'How many iterations does the optimized Cholesky decomposition take to reach an accuracy of 0.79?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/81.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy improvements of our approach and the naïve Cholesky decomposition for ResNet32 and CIFAR10." ], "reasoning": "The table shows that the optimized Cholesky decomposition reaches an accuracy of 0.79 after 62 iterations.", "question": "How many iterations does the optimized Cholesky decomposition take to reach an accuracy of 0.79?", "answer": "So, the answer is {\"answer\": \"62\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe optimized Cholesky decomposition reaches an accuracy of 0.80 after 117 iterations.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/81.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy improvements of our approach and the naïve Cholesky decomposition for ResNet32 and CIFAR10." ], "reasoning": "The table indicates that the optimized Cholesky decomposition achieves an accuracy of 0.80 after 117 iterations.", "statement": "The optimized Cholesky decomposition reaches an accuracy of 0.80 after 117 iterations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Accuracy improvements of our approach decomposition for ResNet32 and CIFAR10 in a parallel setting.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/82.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy improvements of our approach decomposition for ResNet32 and CIFAR10 in a parallel setting." ], "reasoning": "The optimized Cholesky decomposition approach reaches an accuracy of 0.79 after 35 iterations and achieves a final accuracy of 0.80 after 61 iterations.", "description": "While the original approach needs 176 iterations, we hit the same accuracy after 35 optimization steps,. approach needs 176 iterations, we hit the same accuracy after 35 optimization steps, i.e. We even outperform our approach and reach our final result of 0.80 after 61 iterations," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Accuracy improvements of our approach decomposition for ResNet32 and CIFAR10 in a parallel setting.'\nProblem: 'How many iterations did it take for the optimized Cholesky decomposition to reach an accuracy of 0.79?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/82.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy improvements of our approach decomposition for ResNet32 and CIFAR10 in a parallel setting." ], "reasoning": "By examining the table, we see that the accuracy of 0.79 is reached at 35 iterations.", "question": "How many iterations did it take for the optimized Cholesky decomposition to reach an accuracy of 0.79?", "answer": "So, the answer is {\"answer\": \"35\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe optimized Cholesky decomposition approach reaches an accuracy of 0.80 after 61 iterations.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/82.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy improvements of our approach decomposition for ResNet32 and CIFAR10 in a parallel setting." ], "reasoning": "The table shows that the optimized Cholesky decomposition approach reaches an accuracy of 0.80 at 61 iterations.", "statement": "The optimized Cholesky decomposition approach reaches an accuracy of 0.80 after 61 iterations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Test accuracy (Top-1) results for CIFAR10/100. We compare model accuracy using our training scheme and early learning-rate drop as described in section 4.3. We emphasize the reduces number of steps required reaching this accuracy using our MMS method.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/83.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test accuracy (Top-1) results for CIFAR10/100. We compare model accuracy using our training scheme and early learning-rate drop as described in section 4.3. We emphasize the reduces number of steps required reaching this accuracy using our MMS method." ], "reasoning": "The selection scheme significantly reduced the training steps needed to achieve near-final model accuracy.", "description": "As described in table 1, training with our selection scheme almost reached final model accuracy in considerably less training steps as originally suggested." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Test accuracy (Top-1) results for CIFAR10/100. We compare model accuracy using our training scheme and early learning-rate drop as described in section 4.3. We emphasize the reduces number of steps required reaching this accuracy using our MMS method.'\nProblem: 'How many fewer training steps did the ResNet-44 network require with the new selection scheme compared to the baseline?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/83.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test accuracy (Top-1) results for CIFAR10/100. We compare model accuracy using our training scheme and early learning-rate drop as described in section 4.3. We emphasize the reduces number of steps required reaching this accuracy using our MMS method." ], "reasoning": "The baseline steps for ResNet-44 are 156K, and the steps with the new scheme are 44K. Subtracting these gives the reduction in steps.", "question": "How many fewer training steps did the ResNet-44 network require with the new selection scheme compared to the baseline?", "answer": "So, the answer is {\"answer\": \"112K\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe WRN-28-10 network achieved higher accuracy with the new selection scheme compared to the baseline.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/83.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test accuracy (Top-1) results for CIFAR10/100. We compare model accuracy using our training scheme and early learning-rate drop as described in section 4.3. We emphasize the reduces number of steps required reaching this accuracy using our MMS method." ], "reasoning": "The table shows that the accuracy for WRN-28-10 with the new scheme is 82.2%, which is slightly lower than the baseline accuracy of 82.26%.", "statement": "The WRN-28-10 network achieved higher accuracy with the new selection scheme compared to the baseline.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: AUC using different classifiers for three sepsis gold standards. Here, Det = Detection, Pred = Prediction, S = Sepsis, SS = Severe Sepsis, SK = Septic Shock, MLP = Multilayer Perceptron, LR = Logistic Regression, XGB = XGBoost, RF = Random Forest.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/84.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: AUC using different classifiers for three sepsis gold standards. Here, Det = Detection, Pred = Prediction, S = Sepsis, SS = Severe Sepsis, SK = Septic Shock, MLP = Multilayer Perceptron, LR = Logistic Regression, XGB = XGBoost, RF = Random Forest." ], "reasoning": "RF outperforms other classifiers in most cases, while LR performs the worst.", "description": "The results have been reported in Table 1. As we can see, from the results, out of the 4 base classifiers, RF is the clear winner in most of the cases while LR did the worst. XGB and MLP lie in between." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: AUC using different classifiers for three sepsis gold standards. Here, Det = Detection, Pred = Prediction, S = Sepsis, SS = Severe Sepsis, SK = Septic Shock, MLP = Multilayer Perceptron, LR = Logistic Regression, XGB = XGBoost, RF = Random Forest.'\nProblem: 'Which classifier has the highest performance in the 'Det' task for the 'SK' category?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/84.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: AUC using different classifiers for three sepsis gold standards. Here, Det = Detection, Pred = Prediction, S = Sepsis, SS = Severe Sepsis, SK = Septic Shock, MLP = Multilayer Perceptron, LR = Logistic Regression, XGB = XGBoost, RF = Random Forest." ], "reasoning": "In the 'Det' task for the 'SK' category, RF has a performance score of 0.90, which is higher than MLP (0.86), LR (0.75), and XGB (0.90).", "question": "Which single classifier has the highest performance in the 'Det' task for the 'SK' category?", "answer": { "answer": "RF" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nLR has the lowest performance in the 'Pred' task for the 'SS' category.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/84.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: AUC using different classifiers for three sepsis gold standards. Here, Det = Detection, Pred = Prediction, S = Sepsis, SS = Severe Sepsis, SK = Septic Shock, MLP = Multilayer Perceptron, LR = Logistic Regression, XGB = XGBoost, RF = Random Forest." ], "reasoning": "In the 'Pred' task for the 'SS' category, LR has a performance score of 0.76, which is lower than MLP (0.86), XGB (0.90), and RF (0.90).", "statement": "LR has the lowest performance in the 'Pred' task for the 'SS' category.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparison with rule-based scoring systems in term of AUC. Here, Det = Detection, Pred = Prediction, S = Sepsis, SS = Severe Sepsis, SK = Septic Shock.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/85.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison with rule-based scoring systems in term of AUC. Here, Det = Detection, Pred = Prediction, S = Sepsis, SS = Severe Sepsis, SK = Septic Shock." ], "reasoning": "MEWS performed best in detection tasks for sepsis, severe sepsis, and septic shock, while SOFA performed best in prediction tasks for the same categories.", "description": "Out of the three systems, MEWS did the best in detection task (0.72, 0.76 and 0.66 for sepsis, severe sepsis, and septic shock respectively) and SOFA did the best in prediction task (0.54, 0.60 and 0.64 for sepsis, severe sepsis and septic shock respectively)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparison with rule-based scoring systems in term of AUC. Here, Det = Detection, Pred = Prediction, S = Sepsis, SS = Severe Sepsis, SK = Septic Shock.'\nProblem: 'Which system had the highest score for detecting severe sepsis?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/85.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison with rule-based scoring systems in term of AUC. Here, Det = Detection, Pred = Prediction, S = Sepsis, SS = Severe Sepsis, SK = Septic Shock." ], "reasoning": "To find the system with the highest score for detecting severe sepsis, look at the 'Det' task row for 'SS' category. MEWS has the highest score of 0.76.", "question": "Which system had the highest score for detecting severe sepsis (SS)?", "answer": "So, the answer is {\"answer\": \"Ensemble\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nqSOFA had the lowest score for predicting sepsis.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/85.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison with rule-based scoring systems in term of AUC. Here, Det = Detection, Pred = Prediction, S = Sepsis, SS = Severe Sepsis, SK = Septic Shock." ], "reasoning": "To verify this statement, look at the 'Pred' task row for 'S' category. qSOFA has a score of 0.44, which is the lowest compared to SOFA (0.54) and MEWS (0.49).", "statement": "qSOFA had the lowest score for predicting (Pred) sepsis (S).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Imagenet-1K ResNet50 validation accuracy comparison between the regular data loader and the locality-aware data loader.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/86.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Imagenet-1K ResNet50 validation accuracy comparison between the regular data loader and the locality-aware data loader." ], "reasoning": "The validation accuracy differences between the locality-aware data loader and the regular PyTorch data loader are below 1%.", "description": "In Table I, we present the results. Using the localityaware data loader resulted in comparable validation accuracy with that of the regular PyTorch data loader, as the differences are below 1%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Imagenet-1K ResNet50 validation accuracy comparison between the regular data loader and the locality-aware data loader.'\nProblem: 'What is the validation accuracy difference between the regular loader and the locality-aware loader for 32 nodes?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/86.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Imagenet-1K ResNet50 validation accuracy comparison between the regular data loader and the locality-aware data loader." ], "reasoning": "The validation accuracy for 32 nodes using the regular loader is 75.33%, and using the locality-aware loader is 75.12%. The difference is 75.33% - 75.12% = 0.21%.", "question": "What is the validation accuracy difference between the regular loader and the locality-aware loader for 32 nodes?", "answer": "So, the answer is {\"answer\": \"0.21%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe locality-aware loader has a higher validation accuracy than the regular loader for 16 nodes.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/86.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Imagenet-1K ResNet50 validation accuracy comparison between the regular data loader and the locality-aware data loader." ], "reasoning": "For 16 nodes, the validation accuracy of the locality-aware loader is 76.81%, while that of the regular loader is 76.67%. Since 76.81% is higher than 76.67%, the statement is supported.", "statement": "The locality-aware loader has a higher validation accuracy than the regular loader for 16 nodes.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Features ranking in term of AUC. Systolic blood pressure and temperature are the most important vital signs for sepsis prediction and detection respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/87.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Features ranking in term of AUC. Systolic blood pressure and temperature are the most important vital signs for sepsis prediction and detection respectively." ], "reasoning": "Systolic blood pressure (feature 4) and temperature (feature 6) are the most important for prediction and detection, while respiratory rate (feature 3) and heart rate (feature 1) follow in importance.", "description": "We found feature 4 (systolic blood pressure) and feature 6 (temperature) as the most important vital signs for prediction and detection respectively.. Also a close observation reveals, feature 3 (respiratory rate) and feature 1 (heart rate) as the next most important features." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Features ranking in term of AUC. Systolic blood pressure and temperature are the most important vital signs for sepsis prediction and detection respectively.'\nProblem: 'Which feature has the highest importance for sepsis detection and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/87.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Features ranking in term of AUC. Systolic blood pressure and temperature are the most important vital signs for sepsis prediction and detection respectively." ], "reasoning": "By examining the table, feature 6 (temperature) has the highest value for sepsis detection at 0.90.", "question": " What is its value for the feature has the highest importance for sepsis detection?", "answer": "So, the answer is {\"answer\": \"0.90\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFeature 4 is the most important for septic shock prediction.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/87.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Features ranking in term of AUC. Systolic blood pressure and temperature are the most important vital signs for sepsis prediction and detection respectively." ], "reasoning": "The table shows that feature 4 (systolic blood pressure) has the highest value for septic shock prediction at 0.79.", "statement": "Feature 4 is the most important for septic shock prediction.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Features ablation in term of AUC. Sepsis is highly correlated with six vital signs. All models perform significantly better when six vital signs are used.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/88.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Features ablation in term of AUC. Sepsis is highly correlated with six vital signs. All models perform significantly better when six vital signs are used." ], "reasoning": "The table shows that there is no clear trend in AUC values for sepsis detection and septic shock prediction up to five features, but there is a significant improvement when six features are used.", "description": "In Table 4, the number of vitals for set 2-6 has been chosen in a way so that the gradual change, upon addition of new vital, becomes clear. We find that there is no particular trend up to five vital signs.. we find that there is a universal improvement in AUC when six vital signs are used for both detection and prediction tasks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Features ablation in term of AUC. Sepsis is highly correlated with six vital signs. All models perform significantly better when six vital signs are used.'\nProblem: 'What is the AUC value for sepsis detection when six features are used?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/88.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Features ablation in term of AUC. Sepsis is highly correlated with six vital signs. All models perform significantly better when six vital signs are used." ], "reasoning": "The table indicates that the AUC value for sepsis detection with six features is highlighted and shows a significant improvement.", "question": "What is the AUC value for sepsis detection when six features are used?", "answer": "So, the answer is {\"answer\": \"0.97\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUsing six features results in the highest AUC values for both sepsis detection and septic shock prediction.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/88.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Features ablation in term of AUC. Sepsis is highly correlated with six vital signs. All models perform significantly better when six vital signs are used." ], "reasoning": "The table shows that the AUC values for both sepsis detection and septic shock prediction are highest when six features are used, with values of 0.97 and 0.90 respectively.", "statement": "Using six features results in the highest AUC values for both sepsis detection and septic shock prediction.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Monodular depth estimation results on KITTI dataset by the split of Eigen [10]. K and CS refer to KITTI and Cityscapes datasets, respectively. As for supervision, ‘Depth’ means the ground truth depth is used during training, ‘Stereo’ means stereo image sequences with known baselines between two cameras are used during training, and ‘-’ means no supervision is provided. The results are capped at 80m and 50m, respectively. As for error metrics Abs Rel, Seq Rel, RMSE and RMSE log, lower value is better; as for accuracy metrics δ<1.25, δ<1.252 and δ<1.253, higher value is better.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/89.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Monodular depth estimation results on KITTI dataset by the split of Eigen [10]. K and CS refer to KITTI and Cityscapes datasets, respectively. As for supervision, ‘Depth’ means the ground truth depth is used during training, ‘Stereo’ means stereo image sequences with known baselines between two cameras are used during training, and ‘-’ means no supervision is provided. The results are capped at 80m and 50m, respectively. As for error metrics Abs Rel, Seq Rel, RMSE and RMSE log, lower value is better; as for accuracy metrics δ<1.25, δ<1.252 and δ<1.253, higher value is better." ], "reasoning": "Our method outperforms all self-supervised methods and achieves comparable results with supervised ones, generalizing well in different environments.", "description": "As shown in Table 1, our method outperforms all self-supervised methods and achieves comparable results with supervised ones.. Results in the bottom rows of Table 1 show that our method generalizes well in different environments." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Monodular depth estimation results on KITTI dataset by the split of Eigen [10]. K and CS refer to KITTI and Cityscapes datasets, respectively. As for supervision, ‘Depth’ means the ground truth depth is used during training, ‘Stereo’ means stereo image sequences with known baselines between two cameras are used during training, and ‘-’ means no supervision is provided. The results are capped at 80m and 50m, respectively. As for error metrics Abs Rel, Seq Rel, RMSE and RMSE log, lower value is better; as for accuracy metrics δ<1.25, δ<1.252 and δ<1.253, higher value is better.'\nProblem: 'How does our method perform in terms of RMSE compared to other methods in different environments?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/89.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Monodular depth estimation results on KITTI dataset by the split of Eigen [10]. K and CS refer to KITTI and Cityscapes datasets, respectively. As for supervision, ‘Depth’ means the ground truth depth is used during training, ‘Stereo’ means stereo image sequences with known baselines between two cameras are used during training, and ‘-’ means no supervision is provided. The results are capped at 80m and 50m, respectively. As for error metrics Abs Rel, Seq Rel, RMSE and RMSE log, lower value is better; as for accuracy metrics δ<1.25, δ<1.252 and δ<1.253, higher value is better." ], "reasoning": "Our method achieves an RMSE of 5.564 in the K dataset with 80m cap, 5.177 in the K dataset with 50m cap, and 5.176 in the CS+K dataset with 80m cap, which are comparable to other methods.", "question": "How does our method perform in terms of RMSE compared to other methods in different environments?", "answer": "So, the answer is {\"answer\": \"comparable\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur method achieves the lowest Abs Rel score in the K dataset with 80m cap.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/89.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Monodular depth estimation results on KITTI dataset by the split of Eigen [10]. K and CS refer to KITTI and Cityscapes datasets, respectively. As for supervision, ‘Depth’ means the ground truth depth is used during training, ‘Stereo’ means stereo image sequences with known baselines between two cameras are used during training, and ‘-’ means no supervision is provided. The results are capped at 80m and 50m, respectively. As for error metrics Abs Rel, Seq Rel, RMSE and RMSE log, lower value is better; as for accuracy metrics δ<1.25, δ<1.252 and δ<1.253, higher value is better." ], "reasoning": "The table shows that Zhan achieves the lowest Abs Rel score of 0.135 in the K dataset with 80m cap, not our method.", "statement": "Our method achieves the lowest Abs Rel score in the K dataset with 80m cap.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Absolute Trajectory Error (ATE) on sequence 09 and 10 in KITTI odometry dataset. Our method outperforms all the other baselines by a large margin.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/90.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Absolute Trajectory Error (ATE) on sequence 09 and 10 in KITTI odometry dataset. Our method outperforms all the other baselines by a large margin." ], "reasoning": "Our method outperforms all baselines, including ORB-SLAM (full), even with limited frames processed by LSTM and without global optimization.", "description": "As shown in Table 2, our method significantly outperforms all the other baselines,. although only a limited number of frames can be processed by LSTM, our method still performs better than ORB-SLAM (full) without any need of global optimization (such as loop closure, bundle adjustment and re-localization)" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Absolute Trajectory Error (ATE) on sequence 09 and 10 in KITTI odometry dataset. Our method outperforms all the other baselines by a large margin.'\nProblem: 'Which method has the lowest error rate in Seq.09 and Seq.10?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/90.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Absolute Trajectory Error (ATE) on sequence 09 and 10 in KITTI odometry dataset. Our method outperforms all the other baselines by a large margin." ], "reasoning": "By comparing the error rates in both Seq.09 and Seq.10, 'Ours' has the lowest error rates of 0.0030±0.0014 and 0.0029±0.0012 respectively.", "question": "Which method has the lowest error rate in Seq.09 and Seq.10?", "answer": { "answer": "Ours" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSfMLearner modified has a lower error rate than ORB-SLAM (full) in Seq.09.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/90.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Absolute Trajectory Error (ATE) on sequence 09 and 10 in KITTI odometry dataset. Our method outperforms all the other baselines by a large margin." ], "reasoning": "In Seq.09, SfMLearner modified has an error rate of 0.016±0.009, while ORB-SLAM (full) has an error rate of 0.014±0.008. Therefore, the statement is refuted.", "statement": "SfMLearner modified has a lower error rate than ORB-SLAM (full) in Seq.09.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Ablation study on depth estimation for various versions of our method. Baseline denotes our framework without code, LSTM, discriminator (GAN) and trajectory consistency (TC) loss.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/91.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation study on depth estimation for various versions of our method. Baseline denotes our framework without code, LSTM, discriminator (GAN) and trajectory consistency (TC) loss." ], "reasoning": "Adversarial learning and temporal information improve depth estimation performance on the KITTI dataset.", "description": "All the experiments are conducted on KITTI dataset and results are shown in Table 3, 4. In addition, adversarial learning gives the performance a further boost, and the temporal information actually improves depth." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Ablation study on depth estimation for various versions of our method. Baseline denotes our framework without code, LSTM, discriminator (GAN) and trajectory consistency (TC) loss.'\nProblem: 'Which method achieves the lowest RMSE log value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/91.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation study on depth estimation for various versions of our method. Baseline denotes our framework without code, LSTM, discriminator (GAN) and trajectory consistency (TC) loss." ], "reasoning": "By comparing the RMSE log values for all methods, the method 'Baseline+code+GAN' achieves the second lowest RMSE log value of 0.217.", "question": "Which method achieves the second lowest RMSE log value?", "answer": "So, the answer is {\"answer\": \"Baseline+code+GAN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe method 'Baseline+code+GAN+LSTM+TC' achieves the highest δ<1.25 value.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/91.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation study on depth estimation for various versions of our method. Baseline denotes our framework without code, LSTM, discriminator (GAN) and trajectory consistency (TC) loss." ], "reasoning": "By comparing the δ<1.25 values for all methods, the method 'Baseline+code+GAN+LSTM+TC' achieves the highest δ<1.25 value of 0.819.", "statement": "The method 'Baseline+code+GAN+LSTM+TC' achieves the highest δ<1.25 value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Ablation study on pose estimation for various versions of our method on KITTI sequence 09 and 10. B denotes baseline.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/92.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation study on pose estimation for various versions of our method on KITTI sequence 09 and 10. B denotes baseline." ], "reasoning": "The baseline method performs better than other self-supervised VO approaches due to the joint use of depth and image for pose estimation, and the accuracy is further improved by LSTM and trajectory consistency enforcement.", "description": "As for pose estimation in Table 4, our baseline method performs much better than the other self-supervised VO approaches in literature (Table 2). This may mainly because of the joint use of depth and image for pose estimation (Eq. (5)). In addition, the accuracy is significantly improved by LSTM which incorporates historical information of multiple frames. The enforcement of trajectory consistency also brings about promising improvements in that it enforces geometric consistency among multiple pose estimations. Since depth is improved mainly on edges and details which takes up a small proportion, the accuracy gain is therefore limited. Yet the improved details are very important to RGBD matching for pose regression. Therefore, a slight increase in depth accuracy causes a big improvement in pose estimation." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Ablation study on pose estimation for various versions of our method on KITTI sequence 09 and 10. B denotes baseline.'\nProblem: 'Which method achieves the lowest error in Seq.09?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/92.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation study on pose estimation for various versions of our method on KITTI sequence 09 and 10. B denotes baseline." ], "reasoning": "By examining the table, the method 'B+code+GAN+LSTM+TC' achieves the lowest error in Seq.09 with a value of 0.0030±0.0014.", "question": "Which method achieves the lowest error in Seq.09?", "answer": { "answer": "B+code+GAN+LSTM+TC" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe baseline method has the highest error in Seq.09.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/92.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation study on pose estimation for various versions of our method on KITTI sequence 09 and 10. B denotes baseline." ], "reasoning": "The table shows that the baseline method has an error of 0.0072±0.0025 in Seq.09, which is indeed the highest among all listed methods.", "statement": "The baseline method has the highest error in Seq.09.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Experimental Parameters'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/93.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Experimental Parameters" ], "reasoning": "The data consists of sequences with a length of 4 data points, each representing 15 minutes, resulting in an hourly sequence. The dataset spans 110 days, leading to a shape of (110*24, 4, 68).", "description": "Although recurrent neural networks can accept sequences with any length as input, because of the nature of our problem we had to choose a constant sequence length. Due to the constrained computational power we had, we used every hour data as a sequence. Because the time interval for each data point is 15 minutes, each sequence consists of four data points. Since the data contains records for 110 days, the shape of data would be (110*24, 4, 68). Table 3 includes the list of parameters used in the experiment for all three types of RNNs." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Experimental Parameters'\nProblem: 'What is the activation function used in the hidden recurrent layers for the RNNs?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/93.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Experimental Parameters" ], "reasoning": "The table specifies the activation function used in the hidden recurrent layers under the 'Activation function of hidden recurrent layers' row, which is 'tanh'.", "question": "What is the activation function used in the hidden recurrent layers for the RNNs?", "answer": "So, the answer is {\"answer\": \"tanh\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe number of hidden layers used in the RNNs is 3.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/93.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Experimental Parameters" ], "reasoning": "The table lists the number of hidden layers under the 'Number of hidden layers' row, which is 2, not 3.", "statement": "The number of hidden layers used in the RNNs is 3.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Errors over the entire city'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/94.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Errors over the entire city" ], "reasoning": "RNNs, including Simple RNN and GRU, show the lowest error rates in both RMSE and MAPE, indicating superior performance compared to other methods.", "description": "According error rates both RMSE and MAPE, it can be observed that RNNs demonstrate considerably better performance in comparison to the others. Table 4 shows the detailed values of errors over the entire city for each method. Training was performed on a core-i7-7700HQ CPU with 16 GBs of RAM." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Errors over the entire city'\nProblem: 'Which method has the lowest RMSE and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/94.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Errors over the entire city" ], "reasoning": "By examining the RMSE column, the method with the lowest RMSE is GRU with a value of 3.21.", "question": "Which method has the lowest RMSE?", "answer": "So, the answer is {\"answer\": \"GRU\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nLSTM has a lower MAPE than XGBoost.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/94.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Errors over the entire city" ], "reasoning": "According to the table, LSTM has a MAPE of 39.04%, while XGBoost has a MAPE of 40.80%. Therefore, LSTM does have a lower MAPE than XGBoost.", "statement": "LSTM has a lower MAPE than XGBoost.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Related Attributes of Bullet Data'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/95.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Related Attributes of Bullet Data" ], "reasoning": "28 out of 40 volunteers agree that the webpage loading is still fluent with the SmartDanmu extension enabled, indicating that the latency caused by communication with the cloud server is negligible.", "description": "For each statement, each volunteer can select one of the three choices, 'Agree', 'Partially Agree' or 'Disagree' to express his/her opinion on that statement. The survey result is shown in Table 2.. From Table II, we can learn that 28 out 40 volunteers claim that the webpage loading is still fluent when the SmartDanmu extension is enabled, which means the latency caused by communicating with cloud server is tolerant and even negligible for the video watchers. There are still 12 volunteers declare that the inconsistency of they can feel. the video page loading to an extent. We note that a server with more computational and communication resource will improve this delay. Meanwhile, around 75% participants think SmartDanmu could effectively remove low-quality bullets and increase the overall watching experience compared with the original danmaku videos." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Related Attributes of Bullet Data'\nProblem: 'How many volunteers agree that SmartDanmu could effectively remove low-quality bullets and increase the overall watching experience?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/95.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Related Attributes of Bullet Data" ], "reasoning": "According to the table, 31 volunteers agree that SmartDanmu could effectively remove low-quality bullets and increase the overall watching experience.", "question": "How many volunteers agree that SmartDanmu could effectively remove low-quality bullets and increase the overall watching experience?", "answer": { "answer": "31" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAll volunteers agree that the webpage loading is still fluent when the SmartDanmu extension is enabled.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/95.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Related Attributes of Bullet Data" ], "reasoning": "The table shows that 28 volunteers agree, 8 partially agree, and 4 disagree that the webpage loading is still fluent with the SmartDanmu extension enabled.", "statement": "All volunteers agree that the webpage loading is still fluent when the SmartDanmu extension is enabled.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VI: Multilabel Node Classification results in terms of Micro-F1 and Macro-F1. All results are mean of 5-fold cross validations. ✗ indicates the corresponding method failed to finish for the given dataset. ’n.a’ indicates the given method is ’not applicable’ to the corresponding graph.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/96.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Multilabel Node Classification results in terms of Micro-F1 and Macro-F1. All results are mean of 5-fold cross validations. ✗ indicates the corresponding method failed to finish for the given dataset. ’n.a’ indicates the given method is ’not applicable’ to the corresponding graph." ], "reasoning": "The table presents Micro-F1 and Macro-F1 scores for various methods after a 5-fold multi-label classification using one-vs-rest logistic regression.", "description": "We report the Micro-F1 and Macro-F1 scores after a 5-fold multi-label classification using one-vs-rest logistic regression.. In this section, we look at the results from node classification (Table 6)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VI: Multilabel Node Classification results in terms of Micro-F1 and Macro-F1. All results are mean of 5-fold cross validations. ✗ indicates the corresponding method failed to finish for the given dataset. ’n.a’ indicates the given method is ’not applicable’ to the corresponding graph.'\nProblem: 'Which method achieved the highest Micro-F1 score on the Reddit dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/96.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Multilabel Node Classification results in terms of Micro-F1 and Macro-F1. All results are mean of 5-fold cross validations. ✗ indicates the corresponding method failed to finish for the given dataset. ’n.a’ indicates the given method is ’not applicable’ to the corresponding graph." ], "reasoning": "To find the highest Micro-F1 score on the Reddit dataset, look at the 'Reddit mic.' column and identify the highest value. The highest score is 94.5, achieved by LINE-1+2.", "question": "Which method achieved the highest Micro-F1 score on the Reddit dataset?", "answer": "So, the answer is {\"answer\": \"LINE-1+2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nNetMF achieved the highest Macro-F1 score on the PubMed dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/96.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Multilabel Node Classification results in terms of Micro-F1 and Macro-F1. All results are mean of 5-fold cross validations. ✗ indicates the corresponding method failed to finish for the given dataset. ’n.a’ indicates the given method is ’not applicable’ to the corresponding graph." ], "reasoning": "To verify this statement, look at the 'PubMed mac.' column. The highest Macro-F1 score in this column is 77.85, achieved by GraphSAGE-GCN, not NetMF.", "statement": "NetMF achieved the highest Macro-F1 score on the PubMed dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Correlation between participants expertises in the Internet of Things with their solution results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/97.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Correlation between participants expertises in the Internet of Things with their solution results." ], "reasoning": "We analyzed the relationship between each participant's solution performance and their IoT knowledge.", "description": "After executing the solution proposed by each participant, we connect that solution's results with the participant's knowledge in the IoT domain, as shown in Table 3.. Thus, we performed statistical analyses, as described by Peck and Devore , of the measures presented in Table 3." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Correlation between participants expertises in the Internet of Things with their solution results.'\nProblem: 'Which software engineer had the highest solution performance and did their solution work?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/97.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Correlation between participants expertises in the Internet of Things with their solution results." ], "reasoning": "By examining the 'Solution Performance' column, the highest value is 62.88 by Software Engineer 3. The 'Does the solution work?' column for Software Engineer 3 shows 'Y'.", "question": "Which software engineer had the highest solution performance?", "answer": "So, the answer is {\"answer\": \"Software Engineer 3\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSoftware Engineer 8 had the lowest solution performance.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/97.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Correlation between participants expertises in the Internet of Things with their solution results." ], "reasoning": "By examining the 'Solution Performance' column, the lowest value is 16.59 by Software Engineer 8.", "statement": "Software Engineer 8 had the lowest solution performance.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Data to perform test statistic.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/98.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Data to perform test statistic." ], "reasoning": "The performance mean and standard deviation for software engineers with IoT knowledge are 52.46 and 10.91, respectively, while for those without IoT knowledge, they are 32.61 and 15.15.", "description": "As shown in Table 4, we separated the results of the experiments into two groups: i) software engineers with IoT knowledge and ii) software engineers without IoT knowledge. Then, we calculated the mean and the standard deviation of. Accordingly, for a distribution with 7 degrees of freedom (see Table 4) and a confidence level of 99%, the negative tcriticalvalue is -3.00.. As shown in Table 4, the performance mean (x) of the IoT expert software engineers' solutions is 52.46 and the standard deviation (σ) is 10.91.. As shown in Table 4, the performance mean (x) of the solutions from software engineers without experience in IoT development is 32.61 and the standard deviation (σ) is 15.15.. As shown in Table 4, this T-distribution has 5 degrees of freedom." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Data to perform test statistic.'\nProblem: 'What is the difference in the performance mean between software engineers with IoT knowledge and those without IoT knowledge?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/98.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Data to perform test statistic." ], "reasoning": "The performance mean for software engineers with IoT knowledge is 52.46, and for those without IoT knowledge, it is 32.61. The difference is calculated as 52.46 - 32.61.", "question": "What is the difference in the performance mean between software engineers with IoT knowledge and those without IoT knowledge?", "answer": { "answer": "19.85" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe standard deviation of the performance of software engineers without IoT knowledge is higher than that of those with IoT knowledge.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/98.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Data to perform test statistic." ], "reasoning": "The standard deviation for software engineers without IoT knowledge is 15.15, while for those with IoT knowledge, it is 10.91. Since 15.15 is greater than 10.91, the statement is supported.", "statement": "The standard deviation of the performance of software engineers without IoT knowledge is higher than that of those with IoT knowledge.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VI: Using the same solution in a different environment - day average.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/99.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Using the same solution in a different environment - day average." ], "reasoning": "The machine-learning approach had a higher average trip time but significantly lower energy consumption, resulting in the best overall performance.", "description": "As shown in Table 6, when considering the whole day, the machine-learning approach presented the best result. Because the average time for the trip was a little higher using the machine-learning approach, the difference in energy consumption between the two solutions is considerably higher." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VI: Using the same solution in a different environment - day average.'\nProblem: 'What is the difference in fitness between the Average Participant 12 and the Average Learning approaches?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/99.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Using the same solution in a different environment - day average." ], "reasoning": "The fitness for Average Participant 12 is 56.90, and for Average Learning, it is 68.83. The difference is calculated as 68.83 - 56.90.", "question": "What is the difference in fitness between the Average Participant 12 and the Average Learning approaches?", "answer": "So, the answer is {\"answer\": \"11.93\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Average Learning approach had a higher energy consumption than the Average Participant 12 approach.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/99.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Using the same solution in a different environment - day average." ], "reasoning": "The table shows that the Average Learning approach had an energy consumption of 8.46%, while the Average Participant 12 approach had an energy consumption of 50.52%. Therefore, the statement is incorrect.", "statement": "The Average Learning approach had a higher energy consumption than the Average Participant 12 approach.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Features ranked in order of importance.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/400.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Features ranked in order of importance." ], "reasoning": "The importance of variables is relatively stable across models, with notable differences in the importance of gloss length for music and the number of partOf relations for fruit.", "description": "Table 2 lists the importance of each variable in the global model and in the three single domain models, where the variable with the highest weight is ranked 1. The lists are relatively stable, with some marked differences, such as the importance of the gloss length and the number of partOf relations for music and fruit, respectively." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Features ranked in order of importance.'\nProblem: 'Which feature is ranked highest in the global model and what is its rank in the music model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/400.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Features ranked in order of importance." ], "reasoning": "The feature ranked highest in the global model (All) is 'depth_in_hierarchy' with a rank of 1. In the music model, 'depth_in_hierarchy' is ranked 3.", "question": "Which feature is ranked highest in the global model?", "answer": "So, the answer is {\"answer\": \"depth_in_hierarchy\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe feature 'nr_of_partOfs' is ranked 8 in the fruit model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/400.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Features ranked in order of importance." ], "reasoning": "According to the table, the feature 'nr_of_partOfs' is ranked 2 in the fruit model.", "statement": "The feature 'nr_of_partOfs' is ranked 8 in the fruit model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Balanced accuracy and κ of predictions made in a new domain, with or without normalization.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/401.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Balanced accuracy and κ of predictions made in a new domain, with or without normalization." ], "reasoning": "Normalization of both structural and lexical features improves performance, while normalization of the frequency feature decreases performance.", "description": "We find that normalization of both structural and lexical features leads to a significant performance gain. Normalization of the frequency feature seems to hurt performance (Table 3)" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Balanced accuracy and κ of predictions made in a new domain, with or without normalization.'\nProblem: 'Which domain shows the highest accuracy when structural features are normalized?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/401.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Balanced accuracy and κ of predictions made in a new domain, with or without normalization." ], "reasoning": "To find the highest accuracy when structural features are normalized, compare the 'Structural Bal. Acc.' values for all domains. The highest value is 0.84 for the Tools domain.", "question": "Which domain shows the highest accuracy when structural features are normalized?", "answer": { "answer": "Tools" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nNormalization of the frequency feature improves performance for the Music domain.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/401.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Balanced accuracy and κ of predictions made in a new domain, with or without normalization." ], "reasoning": "The table shows that the accuracy for the Music domain decreases from 0.62 (None Bal.) to 0.55 (Frequency Bal.) when the frequency feature is normalized, indicating a decrease in performance.", "statement": "Normalization of the frequency feature improves performance for the Music domain.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Distribution of the event mentions per POS per token in all datasets of the EVENTI corpus.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/402.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Distribution of the event mentions per POS per token in all datasets of the EVENTI corpus." ], "reasoning": "Verbs are the most frequently annotated category, followed by nouns, adjectives, and prepositions.", "description": "Tables 1 and 2 report, respectively, the distribution of the events per token part-of speech (POS) and per event class.. verbs are the largest annotated category, followed by nouns, adjectives, and prepositional phrases." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Distribution of the event mentions per POS per token in all datasets of the EVENTI corpus.'\nProblem: 'What is the total number of annotated verbs across all datasets?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/402.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Distribution of the event mentions per POS per token in all datasets of the EVENTI corpus." ], "reasoning": "To find the total number of annotated verbs, sum the counts from the Training, Dev., and Test columns for verbs: 11,269 + 193 + 2,426.", "question": "What is the total number of annotated verbs across all datasets?", "answer": { "answer": "13,888" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nNouns have the highest number of annotations in the Training dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/402.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Distribution of the event mentions per POS per token in all datasets of the EVENTI corpus." ], "reasoning": "The table shows that verbs have the highest number of annotations in the Training dataset with 11,269, while nouns have 6,710.", "statement": "Nouns have the highest number of annotations in the Training dataset.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Distribution of the event mentions per class in all datasets of the EVENTI corpus.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/403.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Distribution of the event mentions per class in all datasets of the EVENTI corpus." ], "reasoning": "The majority of events are OCCURRENCE and STATE, followed by intensional events (I_STATE and I_ACTION), with REPORTING, ASPECTUAL, and PERCEPTION being the least frequent.", "description": "Tables 1 and 2 report, respectively, the distribution of the events per token part-of speech (POS) and per event class.. As for the classes, OCCURRENCE and STATE represent the large majority of all events, followed by the intensional ones (I STATE and. ACTION), expressing some factual relationship between the target events and their arguments, and finally the others (REPORTING, ASPECTUAL, and PERCEPTION)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Distribution of the event mentions per class in all datasets of the EVENTI corpus.'\nProblem: 'What is the total number of OCCURRENCE and STATE events across all datasets?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/403.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Distribution of the event mentions per class in all datasets of the EVENTI corpus." ], "reasoning": "To find the total number of OCCURRENCE and STATE events, sum the values for Training, Dev., and Test for both classes. OCCURRENCE: 9,041 + 162 + 1,949 = 11,152. STATE: 4,090 + 61 + 843 = 4,994. Total: 11,152 + 4,994 = 16,146.", "question": "What is the total number of OCCURRENCE and STATE events across all datasets?", "answer": "So, the answer is {\"answer\": \"16,146\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe number of REPORTING events in the Training dataset is greater than the number of I_ACTION events in the Test dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/403.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Distribution of the event mentions per class in all datasets of the EVENTI corpus." ], "reasoning": "The number of REPORTING events in the Training dataset is 714, while the number of I_ACTION events in the Test dataset is 357. Since 714 is greater than 357, the statement is supported.", "statement": "The number of REPORTING events in the Training dataset is greater than the number of I_ACTION events in the Test dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results for Bubtask B Main Task - Event detection and classification.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/404.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for Bubtask B Main Task - Event detection and classification." ], "reasoning": "FBK-HLT performs well in detection but not in classification, and Fastext-It achieves the highest F1 scores in both strict and relaxed evaluations.", "description": "Results for the experiments are illustrated in Table 3.. The network obtains the best F1 score, both for detection (F1 of 0.880 for strict evaluation and 0.903 for relaxed evaluation with Fastext-It embeddings) and for classification (F1-class of 0.756 for strict evaluation, and 0.751 for relaxed evaluation with Fastext-It embeddings).. Although FBKHLT suffers in the classification subtask, it qualifies as a highly competitive system for the detection subtask.. By observing the strict F1 scores, FBK-HLT beats three configurations (DH-FBK100, ILC-ItWack, Berardi2015 Glove) 11, almost equals one (Berardi2015 w2v) 12, and it is outperformed only by one (Fastext-It) 13. In the relaxed evaluation setting, DH-FBK-100 is the only configuration that does not beat FBK-HLT (although the difference is only 0.001 point). Nevertheless, it is remarkable to observe that FBK-HLT has a very high Precision (0.902, relaxed evaluation mode), that is overcome by only one embedding configuration, ILC-ItWack. The results also indicates that word embeddings have a major contribution on Recall,. these results are obtained using a single step approach," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Results for Bubtask B Main Task - Event detection and classification.'\nProblem: 'Which embedding configuration has the highest Precision in the relaxed evaluation mode?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/404.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for Bubtask B Main Task - Event detection and classification." ], "reasoning": "By examining the table, the highest Precision (P) in the relaxed evaluation mode is 0.914, which corresponds to the ILC-ItWack embedding configuration.", "question": "Which embedding configuration has the highest Precision in the relaxed evaluation mode?", "answer": { "answer": "ILC-ItWack" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFBK-HLT has the highest Recall in the strict evaluation mode.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/404.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for Bubtask B Main Task - Event detection and classification." ], "reasoning": "By examining the table, the highest Recall (R) in the strict evaluation mode is 0.897, which corresponds to the Fastext-It embedding configuration, not FBK-HLT.", "statement": "FBK-HLT has the highest Recall in the strict evaluation mode.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Performance comparison on the SwitchBoard dataset (P: n-gram precision, R: n-gram recall, A: Average, E: Extrema, G: Greedy, L: average length)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/405.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance comparison on the SwitchBoard dataset (P: n-gram precision, R: n-gram recall, A: Average, E: Extrema, G: Greedy, L: average length)" ], "reasoning": "DialogWAE-GMP achieves the highest BLEU recall score of 0.420 and the highest BOW Embedding A score of 0.925, indicating superior performance in generating relevant responses.", "description": "Tables 1 and 2 show the performance of DialogWAE and baselines on the two datasets. DialogWAE outperforms the baselines in the majority of the experiments. In terms of BLEU scores, DialogWAE (with a Gaussian mixture prior network) generates more relevant responses, with the average recall of 42.0% and 37.2% on both of the datasets. These are significantly higher than those of the CVAE baselines (29.9% and 26.5%). We observe a similar trend to the BOW embedding metrics." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Performance comparison on the SwitchBoard dataset (P: n-gram precision, R: n-gram recall, A: Average, E: Extrema, G: Greedy, L: average length)'\nProblem: 'Which model has the highest BLEU recall score and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/405.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance comparison on the SwitchBoard dataset (P: n-gram precision, R: n-gram recall, A: Average, E: Extrema, G: Greedy, L: average length)" ], "reasoning": "By examining the BLEU R column, we see that DialogWAE-GMP has the highest BLEU recall score of 0.420.", "question": "Which model has the highest BLEU recall score?", "answer": "So, the answer is {\"answer\": \"DialogWAE-GMP\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDialogWAE-GMP has the highest BOW Embedding E score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/405.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance comparison on the SwitchBoard dataset (P: n-gram precision, R: n-gram recall, A: Average, E: Extrema, G: Greedy, L: average length)" ], "reasoning": "By examining the BOW Embedding E column, we see that DialogWAE-GMP has a score of 0.661, which is not the highest. The highest score in this column is 0.572 by CVAE.", "statement": "DialogWAE-GMP has the highest BOW Embedding E score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Human judgments for models trained on the Dailydialog dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/406.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Human judgments for models trained on the Dailydialog dataset" ], "reasoning": "DialogWAE-GMP was rated the highest in coherence, diversity, and informativeness by participants in a human evaluation study.", "description": "To validate the previous results, we further conduct a human evaluation with Amazon Mechanical Turk. We randomly selected 50 dialogues from the test set of DailyDialog. For each dialogue context, we generated 10 responses from each of the four models. Responses for each context were inspected by 5 participants who were asked to choose the model which performs the best in regarding to coherence, diversity and informative while being blind to the underlying algorithms. The average percentages that each model was selected as the best to a specific criterion are shown in Table 5." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Human judgments for models trained on the Dailydialog dataset'\nProblem: 'Which model was rated the highest in coherence, diversity, and informativeness?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/406.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Human judgments for models trained on the Dailydialog dataset" ], "reasoning": "By comparing the percentages in the table, DialogWAE-GMP has the highest ratings in coherence (31.6%), diversity (29.2%), and informativeness (29.6%).", "question": "Which model was rated the highest in coherence, diversity, and informativeness?", "answer": { "answer": "DialogWAE-GMP" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDialogWAE was rated the highest in coherence.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/406.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Human judgments for models trained on the Dailydialog dataset" ], "reasoning": "The table shows that DialogWAE-GMP, not DialogWAE, has the highest coherence rating at 31.6%.", "statement": "DialogWAE was rated the highest in coherence.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparison between our proposed methods and baselines. RL Look-ahead model achieves the highest score for all three aspects: Empathy, Relevance, and Fluency. MultiSeq model achieves highest BLEU score, but not significant.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/407.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison between our proposed methods and baselines. RL Look-ahead model achieves the highest score for all three aspects: Empathy, Relevance, and Fluency. MultiSeq model achieves highest BLEU score, but not significant." ], "reasoning": "MultiSeq has the highest BLEU score, but RL Look-ahead outperforms all models in human evaluations for Empathy, Relevance, and Fluency.", "description": "From the BLEU scores in Table 2, we can see that although MultiSeq has the highest BLEU score, all models have similar BLEU scores and the difference is not that significant.. We can clearly notice from human evaluations shown in Table 2 that our model, RL Look-ahead, outperforms all of the others in all three evaluated categories." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparison between our proposed methods and baselines. RL Look-ahead model achieves the highest score for all three aspects: Empathy, Relevance, and Fluency. MultiSeq model achieves highest BLEU score, but not significant.'\nProblem: 'Which model has the highest BLEU score and how does it perform in human evaluations?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/407.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison between our proposed methods and baselines. RL Look-ahead model achieves the highest score for all three aspects: Empathy, Relevance, and Fluency. MultiSeq model achieves highest BLEU score, but not significant." ], "reasoning": "MultiSeq has the highest BLEU score of 2.45. However, in human evaluations, it scores lower in Empathy (2.979), Relevance (3.25), and Fluency (2.952) compared to RL Look-ahead.", "question": "Which model has the highest BLEU score?", "answer": "So, the answer is {\"answer\": \"MultiSeq\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRL Look-ahead has the highest BLEU score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/407.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison between our proposed methods and baselines. RL Look-ahead model achieves the highest score for all three aspects: Empathy, Relevance, and Fluency. MultiSeq model achieves highest BLEU score, but not significant." ], "reasoning": "The table shows that MultiSeq has the highest BLEU score of 2.45, while RL Look-ahead has a BLEU score of 2.32.", "statement": "RL Look-ahead has the highest BLEU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: We report the best performance observed in 5 runs on the development sets of both SParC and CoSQL, since their test sets are not public. We also conduct Wilcoxon signed-rank tests between our method and the baselines, and the bold results show the improvements of our model are significant with p < 0.005.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/408.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: We report the best performance observed in 5 runs on the development sets of both SParC and CoSQL, since their test sets are not public. We also conduct Wilcoxon signed-rank tests between our method and the baselines, and the bold results show the improvements of our model are significant with p < 0.005." ], "reasoning": "Our model achieves the highest Ques.Match and Int.Match scores on both SPARC and CoSQL datasets, outperforming previous models with or without BERT.", "description": "Taking CONCAT as a representative, we compare the performance of our model with other models, as shown in Table 1. As illustrated, our model outperforms baselines by a large margin with or without BERT, achieving new SOTA performances on both datasets. Compared with the previous SOTA without BERT on SPARC, our model improves Ques.Match and Int.Match by 10.6 and 5.4 points, respectively." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: We report the best performance observed in 5 runs on the development sets of both SParC and CoSQL, since their test sets are not public. We also conduct Wilcoxon signed-rank tests between our method and the baselines, and the bold results show the improvements of our model are significant with p < 0.005.'\nProblem: 'By how many points does our model without BERT outperform the previous SOTA without BERT on SPARC Ques.Match?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/408.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: We report the best performance observed in 5 runs on the development sets of both SParC and CoSQL, since their test sets are not public. We also conduct Wilcoxon signed-rank tests between our method and the baselines, and the bold results show the improvements of our model are significant with p < 0.005." ], "reasoning": "The previous SOTA without BERT on SPARC Ques.Match is EditSQL with a score of 33.0. Our model without BERT has a score of 41.8. The difference is 41.8 - 33.0 = 8.8 points.", "question": "By how many points does our model without BERT outperform the previous SOTA without BERT on SPARC Ques.Match?", "answer": { "answer": "8.8" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur model with BERT achieves a higher Int.Match score on CoSQL than EditSQL with BERT.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/408.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: We report the best performance observed in 5 runs on the development sets of both SParC and CoSQL, since their test sets are not public. We also conduct Wilcoxon signed-rank tests between our method and the baselines, and the bold results show the improvements of our model are significant with p < 0.005." ], "reasoning": "Our model with BERT has an Int.Match score of 14.0 on CoSQL, while EditSQL with BERT has a score of 11.0. Therefore, our model with BERT achieves a higher Int.Match score on CoSQL than EditSQL with BERT.", "statement": "Our model with BERT achieves a higher Int.Match score on CoSQL than EditSQL with BERT.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results on the unsupervised hypernym detection and direction prediction tasks. The first three rows of results are from Roller, Kiela, and Nickel (2018). The HyperbolicCones results were reported by Le et al. (2019). The improvements in LEDS and BLESS benchmark are statistically significant with two-tailed p values being 0.019 and ≤ 0.001 respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/409.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the unsupervised hypernym detection and direction prediction tasks. The first three rows of results are from Roller, Kiela, and Nickel (2018). The HyperbolicCones results were reported by Le et al. (2019). The improvements in LEDS and BLESS benchmark are statistically significant with two-tailed p values being 0.019 and ≤ 0.001 respectively." ], "reasoning": "The table shows the performance of various approaches on hypernym detection and direction prediction tasks, with HyperbolicCones and Proposed SPON achieving the highest scores.", "description": "Table 3 shows the results on the unsupervised tasks of hypernym detection and direction predictions, reporting average precision and average accuracy, respectively. The first row titled Count based (in Table 3) depicts the performance of a Hearst-like Pattern system baseline, that uses a frequency based threshold to classify candidate hyponym-hypernym pairs as positive (i.e. exhibiting hypernymy) or negative (i.e. not exhibiting hypernymy). The ppmi approach in Table 3 builds upon the Count based approach. by using Pointwise Mutual Information values for classification. SVD ppmi approach, the main contribution from Roller, Kiela, and Nickel (2018) builds low-rank embeddings of the PPMI matrix, which allows to make predictions for unseen pairs as well. HyperbolicCones is the SOTA (Le et al. 2019) in both these tasks. The final row reports the application of SPON (on the input provided by SVD ppmi) which is an original contribution of our work." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Results on the unsupervised hypernym detection and direction prediction tasks. The first three rows of results are from Roller, Kiela, and Nickel (2018). The HyperbolicCones results were reported by Le et al. (2019). The improvements in LEDS and BLESS benchmark are statistically significant with two-tailed p values being 0.019 and ≤ 0.001 respectively.'\nProblem: 'Which approach achieved the highest average precision in the LEDS dataset for hypernym detection?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/409.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the unsupervised hypernym detection and direction prediction tasks. The first three rows of results are from Roller, Kiela, and Nickel (2018). The HyperbolicCones results were reported by Le et al. (2019). The improvements in LEDS and BLESS benchmark are statistically significant with two-tailed p values being 0.019 and ≤ 0.001 respectively." ], "reasoning": "By examining the Detection (Average Precision) column for the LEDS dataset, the highest value is 0.91, achieved by the Proposed SPON approach.", "question": "Which approach achieved the highest average precision in the LEDS dataset for hypernym detection?", "answer": { "answer": "Proposed SPON" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe HyperbolicCones approach achieved the highest average accuracy in the WBLESS dataset for direction prediction.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/409.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the unsupervised hypernym detection and direction prediction tasks. The first three rows of results are from Roller, Kiela, and Nickel (2018). The HyperbolicCones results were reported by Le et al. (2019). The improvements in LEDS and BLESS benchmark are statistically significant with two-tailed p values being 0.019 and ≤ 0.001 respectively." ], "reasoning": "By examining the Direction (Average Accuracy) column for the WBLESS dataset, the highest value is 0.91, achieved by the Proposed SPON approach, not HyperbolicCones.", "statement": "The HyperbolicCones approach achieved the highest average accuracy in the WBLESS dataset for direction prediction.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Ablation tests reporting Average Precision values on the unsupervised hypernym detection task, signifying the choice of layers utilized in our proposed SPON model. The first row represents SPON i.e. a RELU layer followed by a Residual connection. The second row removes the Residual connection, whereas the third row substitutes the non-negative activation layer RELU with Tanh that can take negative values.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/410.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation tests reporting Average Precision values on the unsupervised hypernym detection task, signifying the choice of layers utilized in our proposed SPON model. The first row represents SPON i.e. a RELU layer followed by a Residual connection. The second row removes the Residual connection, whereas the third row substitutes the non-negative activation layer RELU with Tanh that can take negative values." ], "reasoning": "The results in Table 4 show that using RELU activation without the Residual layer has the worst performance, while using Tanh activations with Residual connections provides almost identical results to RELU with Residual, except for the BLESS dataset.", "description": "The analysis in Section 4 which shows that our choice of function f satisfies asymmetry and transitive properties, holds true because f satisfies f ((cid:126)x) ≥ (cid:126)x component-wise.. Table 4 shows the results for each of these ablation experiments, when evaluated on the unsupervised hypernym detection task across four datasets chosen randomly. Removing the Residual layer and using RELU activation function only, violates the aforementioned component-wise inequality f ((cid:126)x) ≥ (cid:126)x, and has the worst results out of the three. On the other hand, using Residual connections with Tanh activations may not violate the aforementioned inequality, since, it depends upon the sign of the activation outputs. This argument is supported by the results in Table 4, wherein using Tanh activations instead of RELU almost provides identical results, except for the BLESS dataset. Nevertheless, the results in Table 4 show that encouraging asymmetry and transitive properties for this task, in fact improves the results as opposed to not doing the same." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Ablation tests reporting Average Precision values on the unsupervised hypernym detection task, signifying the choice of layers utilized in our proposed SPON model. The first row represents SPON i.e. a RELU layer followed by a Residual connection. The second row removes the Residual connection, whereas the third row substitutes the non-negative activation layer RELU with Tanh that can take negative values.'\nProblem: 'Which combination of activation function and layer has the worst performance on the BLESS dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/410.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation tests reporting Average Precision values on the unsupervised hypernym detection task, signifying the choice of layers utilized in our proposed SPON model. The first row represents SPON i.e. a RELU layer followed by a Residual connection. The second row removes the Residual connection, whereas the third row substitutes the non-negative activation layer RELU with Tanh that can take negative values." ], "reasoning": "The table shows the performance of different combinations of activation functions and layers across four datasets. The worst performance on the BLESS dataset is 0.73, which corresponds to using RELU activation without the Residual layer.", "question": "Which combination of activation function and layer has the worst performance on the BLESS dataset?", "answer": "So, the answer is {\"answer\": \"RELU Only\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUsing Tanh activations with Residual connections provides the best results on the WBLESS dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/410.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation tests reporting Average Precision values on the unsupervised hypernym detection task, signifying the choice of layers utilized in our proposed SPON model. The first row represents SPON i.e. a RELU layer followed by a Residual connection. The second row removes the Residual connection, whereas the third row substitutes the non-negative activation layer RELU with Tanh that can take negative values." ], "reasoning": "The table shows that the performance on the WBLESS dataset is 0.98 for both RELU with Residual and Tanh with Residual. Therefore, using Tanh activations with Residual connections does provide the best results, as it is identical to RELU with Residual.", "statement": "Using Tanh activations with Residual connections provides the best results on the WBLESS dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results on the unsupervised hypernym detection task for BLESS dataset. With 13,089 test instances, the improvement in Average Precision values obtained by SPON as compared against Smoothed Box model is statistically significant with two-tailed p value equals 0.00116.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/411.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on the unsupervised hypernym detection task for BLESS dataset. With 13,089 test instances, the improvement in Average Precision values obtained by SPON as compared against Smoothed Box model is statistically significant with two-tailed p value equals 0.00116." ], "reasoning": "Table 5 shows that the SPON model outperforms both the Order Embeddings and Smoothed Box models in terms of average precision on the BLESS dataset for the hypernym detection task.", "description": "Furthermore, Table 5 illustrates the results on the unsupervised hypernym detection task for BLESS dataset, wherein we compare our proposed SPON model to other supervised SOTA approaches for hypernym prediction task, namely Order Embeddings (OE) approach as introduced by (Vendrov et al. 2016), and Smoothed Box model as introduced by (Li et al. 2019)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Results on the unsupervised hypernym detection task for BLESS dataset. With 13,089 test instances, the improvement in Average Precision values obtained by SPON as compared against Smoothed Box model is statistically significant with two-tailed p value equals 0.00116.'\nProblem: 'Which model achieved the highest average precision on the BLESS dataset for the hypernym detection task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/411.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on the unsupervised hypernym detection task for BLESS dataset. With 13,089 test instances, the improvement in Average Precision values obtained by SPON as compared against Smoothed Box model is statistically significant with two-tailed p value equals 0.00116." ], "reasoning": "The table compares the average precision of three models: OE, Smoothed Box, and SPON. SPON has the highest average precision of 0.811.", "question": "Which model achieved the highest average precision on the BLESS dataset for the hypernym detection task?", "answer": "So, the answer is {\"answer\": \"SPON\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Smoothed Box model achieved a higher average precision than the OE model on the BLESS dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/411.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on the unsupervised hypernym detection task for BLESS dataset. With 13,089 test instances, the improvement in Average Precision values obtained by SPON as compared against Smoothed Box model is statistically significant with two-tailed p value equals 0.00116." ], "reasoning": "The table shows that the Smoothed Box model has an average precision of 0.795, while the OE model has an average precision of 0.761, indicating that the Smoothed Box model performed better.", "statement": "The Smoothed Box model achieved a higher average precision than the OE model on the BLESS dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Limited length ROUGE recall results on the NYT50 test set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/412.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Limited length ROUGE recall results on the NYT50 test set." ], "reasoning": "Our model outperforms the previous state-of-the-art ML+RL with intra-attn approach by 2.39 ROUGE-1 and 0.51 ROUGE-2 on this dataset.", "description": "Also we compare performance of our model with two recent models, we see 2.39 ROUGE-1 improvements compared to the ML+RL with intra-attn approach(previous SOTA) carries over to this dataset, which is a large margin. On ROUGE-2, our model also get an im- provement of 0.51. The experiment proves that our approach can outperform competitive methods on different data distributions." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Limited length ROUGE recall results on the NYT50 test set.'\nProblem: 'How much improvement in ROUGE-1 does the Two-Stage + RL model have over the ML+RL+intra-attn model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/412.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Limited length ROUGE recall results on the NYT50 test set." ], "reasoning": "The ROUGE-1 score for the ML+RL+intra-attn model is 42.94, and for the Two-Stage + RL model, it is 45.33. The improvement is calculated as 45.33 - 42.94.", "question": "How much improvement in ROUGE-1 does the Two-Stage + RL model have over the ML+RL+intra-attn model?", "answer": { "answer": "2.39" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Two-Stage + RL model has a higher ROUGE-2 score than the Full model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/412.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Limited length ROUGE recall results on the NYT50 test set." ], "reasoning": "The ROUGE-2 score for the Two-Stage + RL model is 26.53, while for the Full model, it is 24.9. Therefore, the Two-Stage + RL model has a higher ROUGE-2 score.", "statement": "The Two-Stage + RL model has a higher ROUGE-2 score than the Full model.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Results on SemEval 2018 Domain-specific hypernym discovery task. CRIM is the best system on the domain specific datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/413.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results on SemEval 2018 Domain-specific hypernym discovery task. CRIM is the best system on the domain specific datasets." ], "reasoning": "SPON outperforms the SOTA systems in all tasks except for the medical domain where it achieves comparable results.", "description": "Similarly, Table 7 shows the results on the two domain-specific tasks of music and medical domain corpora. SPON outperforms the SOTA systems in all tasks except for the medical domain in which it achieves comparable results." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Results on SemEval 2018 Domain-specific hypernym discovery task. CRIM is the best system on the domain specific datasets.'\nProblem: 'Which system achieved the highest Music MRR score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/413.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results on SemEval 2018 Domain-specific hypernym discovery task. CRIM is the best system on the domain specific datasets." ], "reasoning": "By comparing the Music MRR scores of both systems, SPON achieved the highest Music MRR score with 71.20.", "question": "Which system achieved the highest Music MRR score?", "answer": { "answer": "SPON" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSPON outperformed CRIM in the Music MAP task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/413.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results on SemEval 2018 Domain-specific hypernym discovery task. CRIM is the best system on the domain specific datasets." ], "reasoning": "The table shows that SPON achieved a Music MAP score of 54.70, which is higher than CRIM's score of 40.97.", "statement": "SPON outperformed CRIM in the Music MAP task.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Average embedding similarity scores between the output and the target output in terms of Real target output list.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/414.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Average embedding similarity scores between the output and the target output in terms of Real target output list." ], "reasoning": "The similarity scores between the outputs and the target outputs are not high with real inputs, but significantly improve with the crafted inputs from the proposed framework.", "description": "We calculate these similarity scores for each Real targeted output for the real inputs and the inputs found by the proposed model and report the average value in Table 1. According to the table, we observe that even inputting the real inputs, the similarity scores between the outputs and the target outputs are not high. Besides, with the crafted inputs from the proposed framework, these similarity scores are significantly improved. For example, for RL BeamSearch(200), the similarity is improved by 41.5%, 34.0% and 28.3% for the target outputs with length 1-3, 4-6 and 7-10, respectively." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Average embedding similarity scores between the output and the target output in terms of Real target output list.'\nProblem: 'What is the similarity score for RL BeamSearch(200) with target output length 7-10?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/414.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Average embedding similarity scores between the output and the target output in terms of Real target output list." ], "reasoning": "The table shows the similarity scores for different methods and target output lengths. For RL BeamSearch(200) with target output length 7-10, the similarity score is 0.726.", "question": "What is the similarity score for RL BeamSearch(200) with target output length 7-10?", "answer": { "answer": "0.726" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe similarity score for RL Greedy with target output length 4-6 is higher than that for Pre-trained Greedy with the same length.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/414.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Average embedding similarity scores between the output and the target output in terms of Real target output list." ], "reasoning": "The table shows that the similarity score for RL Greedy with target output length 4-6 is 0.560, which is higher than the score for Pre-trained Greedy with the same length, which is 0.529.", "statement": "The similarity score for RL Greedy with target output length 4-6 is higher than that for Pre-trained Greedy with the same length.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Test classification accuracy (and the number of parameters used). The bottom part shows our ablation results: SoPa: our full model. SoPams1: running with max-sum semiring (rather than max-product), with the identity function as our encoder E (see Equation 3). sl: self-loops, ϵ: ϵ transitions. The final row is equivalent to a one-layer CNN.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/415.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test classification accuracy (and the number of parameters used). The bottom part shows our ablation results: SoPa: our full model. SoPams1: running with max-sum semiring (rather than max-product), with the identity function as our encoder E (see Equation 3). sl: self-loops, ϵ: ϵ transitions. The final row is equivalent to a one-layer CNN." ], "reasoning": "SoPa outperforms all models on SST and ROC, and performs within 0.3 points of CNN and BiLSTM on Amazon, despite having fewer parameters.", "description": "Table 1 shows our main experimental results. In two of the cases (SST and ROC), SoPa outperforms all models. On Amazon, SoPa performs within 0.3 points of CNN and BiLSTM, and outperforms the other two baselines. The table also shows the number of parameters used by each model for each task.. SoPa performs better or roughly the same as a BiLSTM, which has 3–6 times as many parameters.. Table 1 also shows an ablation of the differences between SoPa and CNN: max-product semiring with sigmoid vs. max-sum semiring with identity, self-loops, and (cid:15)-transitions. The last line is equivalent to a CNN with. multiple window sizes. Interestingly, the most notable difference between SoPa and CNN is the semiring and encoder function, while (cid:15) transitions and self-loops have little effect on performance.1" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Test classification accuracy (and the number of parameters used). The bottom part shows our ablation results: SoPa: our full model. SoPams1: running with max-sum semiring (rather than max-product), with the identity function as our encoder E (see Equation 3). sl: self-loops, ϵ: ϵ transitions. The final row is equivalent to a one-layer CNN.'\nProblem: 'Which model has the highest score on the ROC task and how many parameters does it use?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/415.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test classification accuracy (and the number of parameters used). The bottom part shows our ablation results: SoPa: our full model. SoPams1: running with max-sum semiring (rather than max-product), with the identity function as our encoder E (see Equation 3). sl: self-loops, ϵ: ϵ transitions. The final row is equivalent to a one-layer CNN." ], "reasoning": "To find the model with the highest score on the ROC task, look at the ROC column and identify the highest score. The highest score is 66.5, achieved by SoPa, which uses 255K parameters.", "question": "Which model has the highest score on the ROC task?", "answer": { "answer": "SoPa" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSoPa outperforms BiLSTM on the SST task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/415.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test classification accuracy (and the number of parameters used). The bottom part shows our ablation results: SoPa: our full model. SoPams1: running with max-sum semiring (rather than max-product), with the identity function as our encoder E (see Equation 3). sl: self-loops, ϵ: ϵ transitions. The final row is equivalent to a one-layer CNN." ], "reasoning": "To verify this statement, compare the scores of SoPa and BiLSTM on the SST task. SoPa has a score of 85.6, while BiLSTM has a score of 84.8, indicating that SoPa outperforms BiLSTM on the SST task.", "statement": "SoPa outperforms BiLSTM on the SST task.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Cohen’s Kappa score (κ) and observed agreement (Ao) for gold standard dialogue'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/416.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Cohen’s Kappa score (κ) and observed agreement (Ao) for gold standard dialogue" ], "reasoning": "The table shows that the observed agreement (Ao) and Cohen's κ for question annotation are 0.73 and 0.63, respectively, indicating a moderate level of agreement among annotators.", "description": "To evaluate the annotations, inter-annotator agreement was calculated based on a subset of the gold standard corpus.4 Table 4 illustrates the values of observed agreement (Ao) and Cohen's κ (Cohen, 1960) obtained for question, feature and answer annotation. The agreement values obtained for question types were over 0.6 (for all annotators combined)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Cohen’s Kappa score (κ) and observed agreement (Ao) for gold standard dialogue'\nProblem: 'What is the observed agreement (Ao) value for feature annotation, and how does it compare to the Ao value for answer annotation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/416.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Cohen’s Kappa score (κ) and observed agreement (Ao) for gold standard dialogue" ], "reasoning": "The observed agreement (Ao) value for feature annotation is 0.90, which is higher than the Ao value for answer annotation, which is 0.59.", "question": "What is the observed agreement (Ao) value for feature annotation, and how does it compare to the Ao value for answer annotation?", "answer": { "answer": "0.90" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Cohen's κ value for question annotation is higher than that for feature annotation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/416.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Cohen’s Kappa score (κ) and observed agreement (Ao) for gold standard dialogue" ], "reasoning": "The table shows that the Cohen's κ value for question annotation is 0.63, while for feature annotation it is 0.67. Therefore, the statement is refuted.", "statement": "The Cohen's κ value for question annotation is higher than that for feature annotation.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Experimental results. The best result for each dataset is indicated in bold, and marked with “†” if it is significantly higher than the second best result (based on a one-tailed Wilcoxon signed-rank test; p<0.05). The results of Benchmark on the arXiv dataset are from the original paper, where the standard deviation values were not reported. All neural models except for Inceptionfixed have larger standard deviation values on arXiv than Wikipedia, which can be explained by the small size of the arXiv test set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/417.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Experimental results. The best result for each dataset is indicated in bold, and marked with “†” if it is significantly higher than the second best result (based on a one-tailed Wilcoxon signed-rank test; p<0.05). The results of Benchmark on the arXiv dataset are from the original paper, where the standard deviation values were not reported. All neural models except for Inceptionfixed have larger standard deviation values on arXiv than Wikipedia, which can be explained by the small size of the arXiv test set." ], "reasoning": "The JOINT model achieves the highest accuracy on both Wikipedia and arXiv datasets by combining textual and visual features, outperforming BILSTM and INCEPTION.", "description": "Experimental Results Table 3 shows the performance of the different models over our two datasets, in the form of the average accuracy on the test set (along with the standard deviation) over 10 runs, with different random initializations.. On Wikipedia, we observe that the performance of BILSTM, INCEPTION, and JOINT is much better than that of. all four baselines. INCEPTION achieves 2.9% higher accuracy than BILSTM. The performance of JOINT achieves an accuracy of 59.4%, which is 5.3% higher than using textual features alone (BILSTM) and 2.4% higher than using visual features alone (INCEPTION). Based on a one-tailed Wilcoxon signed-rank test, the performance of Jis sta. of-the-art results in combination. For arXiv, baseline methods MAJORITY, BENCHMARK, and INCEPTIONFIXED outperform BILSTM over cs.ai, in large part because of the class imbalance in this dataset (90% of papers are rejected). Surprisingly, INCEPTIONFIXED is better than MAJORITY and BENCHMARK over the arXiv cs.lg subset, which verifies the usefulness of visual features, even when only the last layer is fine-tuned. Table 3 also shows that INCEPTION and BILSTM achieve similar performance on arXiv, showing that textual and visual representations are equally discriminative: INCEPTION and BILSTM are indistinguishable over cs.cl; BILSTM achieves 1.8% higher accuracy over cs.lg, while INCEPTION achieves 1.3% higher accuracy over cs.ai. Once again, the JOINT model achieves the highest accuracy on cs.ai and cs.cl by combining textual and visual representations (at a level of statistical significance for cs.ai). This, again, confirms that textual and visual. state-of-the-art results. On arXiv cs.lg, JOINT achieves a 0.6% higher accuracy than INCEPTION by combining visual features and textual features, but BILSTM achieves the highest accuracy. One characteristic of cs.lg documents is" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Experimental results. The best result for each dataset is indicated in bold, and marked with “†” if it is significantly higher than the second best result (based on a one-tailed Wilcoxon signed-rank test; p<0.05). The results of Benchmark on the arXiv dataset are from the original paper, where the standard deviation values were not reported. All neural models except for Inceptionfixed have larger standard deviation values on arXiv than Wikipedia, which can be explained by the small size of the arXiv test set.'\nProblem: 'What is the accuracy difference between the JOINT model and the BILSTM model on the Wikipedia dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/417.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Experimental results. The best result for each dataset is indicated in bold, and marked with “†” if it is significantly higher than the second best result (based on a one-tailed Wilcoxon signed-rank test; p<0.05). The results of Benchmark on the arXiv dataset are from the original paper, where the standard deviation values were not reported. All neural models except for Inceptionfixed have larger standard deviation values on arXiv than Wikipedia, which can be explained by the small size of the arXiv test set." ], "reasoning": "The accuracy of the JOINT model on the Wikipedia dataset is 59.4%, while the accuracy of the BILSTM model is 54.1%. The difference is 59.4% - 54.1% = 5.3%.", "question": "What is the accuracy difference between the JOINT model and the BILSTM model on the Wikipedia dataset?", "answer": { "answer": "5.3%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe INCEPTION model achieves higher accuracy than the BILSTM model on the arXiv cs.lg subset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/417.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Experimental results. The best result for each dataset is indicated in bold, and marked with “†” if it is significantly higher than the second best result (based on a one-tailed Wilcoxon signed-rank test; p<0.05). The results of Benchmark on the arXiv dataset are from the original paper, where the standard deviation values were not reported. All neural models except for Inceptionfixed have larger standard deviation values on arXiv than Wikipedia, which can be explained by the small size of the arXiv test set." ], "reasoning": "The table shows that the accuracy of the INCEPTION model on the arXiv cs.lg subset is 79.3%, while the accuracy of the BILSTM model is 81.1%. Therefore, the statement is refuted.", "statement": "The INCEPTION model achieves higher accuracy than the BILSTM model on the arXiv cs.lg subset.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Confusion matrix of the Joint model on Wikipedia. Rows are the actual quality classes and columns are the predicted quality classes. The diagonal (gray cells) indicates correct predictions.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/418.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Confusion matrix of the Joint model on Wikipedia. Rows are the actual quality classes and columns are the predicted quality classes. The diagonal (gray cells) indicates correct predictions." ], "reasoning": "Table 4 shows that more than 50% of documents for each quality class are correctly classified, except for the C class where more documents are misclassified into B.", "description": "Table 4 shows the confusion matrix of JOINT on Wikipedia. We can see that more than 50% of documents for each quality class are correctly classified, except for the. class where more documents are misclassified into B." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Confusion matrix of the Joint model on Wikipedia. Rows are the actual quality classes and columns are the predicted quality classes. The diagonal (gray cells) indicates correct predictions.'\nProblem: 'Which quality class has the highest number of correctly classified documents?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/418.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Confusion matrix of the Joint model on Wikipedia. Rows are the actual quality classes and columns are the predicted quality classes. The diagonal (gray cells) indicates correct predictions." ], "reasoning": "To find the quality class with the highest number of correctly classified documents, look at the diagonal values of the confusion matrix. The highest value on the diagonal is 397 for the FA class.", "question": "Which quality class has the highest number of correctly classified documents?", "answer": "So, the answer is {\"answer\": \"FA\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMore than 50% of documents in the GA class are correctly classified.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/418.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Confusion matrix of the Joint model on Wikipedia. Rows are the actual quality classes and columns are the predicted quality classes. The diagonal (gray cells) indicates correct predictions." ], "reasoning": "The GA class has 299 correctly classified documents out of a total of 500 (112+299+65+22+2+0), which is more than 50%.", "statement": "More than 50% of documents in the GA class are correctly classified.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Large-scale text classification data sets'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/419.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Large-scale text classification data sets" ], "reasoning": "The AG News dataset consists of 4 classes with 30,000 training samples per class and 1,900 testing samples per class, while the Sogou News dataset consists of 5 classes with a total of 450,000 training samples and 60,000 testing samples.", "description": "We present our results on two freely available large scale datasets introduced by Zhang et al. . We also shrink the scale of training samples by randomly picking up the same amount of data in each category (see Table 1).. AG News The AG News corpus consists of news articles from the AG's corpus of news articles on the web pertaining to the 4 largest classes, which are Work, Sports, Business, Sci/Tech. The data set contains 30,000 training samples for each class, 1,900 samples for each class for testing. In this data set, there are three columns which are label, title, description, we treat title as abstract input and description as contents input. Sogou News A Chinese news data set. This data set is a combination of the SogouCA and SogouCS news corpora pertaining to 5 categories, which are Sports, Finance, Entertainment, Automobile and Technology. It contains 450,000 training samples and 60,000 samples for testing in total. Sogou New also has three columns in data set files, label, title, description, similarly, we treat title as abstract input and description as contents input." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Large-scale text classification data sets'\nProblem: 'How many total training samples are there in the AG News dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/419.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Large-scale text classification data sets" ], "reasoning": "The AG News dataset has 30,000 training samples for each of the 4 classes. Multiplying the number of classes by the number of training samples per class gives the total number of training samples.", "question": "How many total training samples are there in the AG News dataset?", "answer": "So, the answer is {\"answer\": \"120k\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Sogou News dataset has more classes than the AG News dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/419.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Large-scale text classification data sets" ], "reasoning": "The table shows that the Sogou News dataset has 5 classes, while the AG News dataset has 4 classes.", "statement": "The Sogou News dataset has more classes than the AG News dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Testing error of our model and related studies'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/420.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Testing error of our model and related studies" ], "reasoning": "The proposed model outperforms VDCNN on AG News and is close to VDCNN's performance on Sogou News, especially when using the same input sequence length of 256 in word-level.", "description": "The experiment results are presented in Table 2. The best performances of our configurations are highlighted in red, which are 8.37, 3.67, 13.79 and 7.89 for the error rates of our proposed model on AG, Sogou, AG(5k), Sogou(10k) respectively. The bold numbers are the officially reported accuracy of VDCNN, to which our proposed model is close. The numbers in blue are the results coming from our comparison experiment by using VDCNN where we set the sequence length to 256 under word level. From these results we can see that our model outperforms VDCNN on AG News for the official error rate, and is very close to VDCNN's performance on Sogou News. If we set VCDNN with the same input sequence length (256) in word-level, the performance of our proposed model is obviously better.. Most of the contributions come from external matrix From the results of Table 2, in the case of training with a large scale dataset, no matter we use simple LSTM or more complex bi-directional LSTM with self-attention, the testing error rates of different configurations are basically similar to each other. It can be said that such near state-of-the-art performance mainly attributes to the contribution from external memory.. Using abstract to build the external memory is better than contents. From Table 2 we can see that the results of using the description to construct the semantics matrix are better than using the abstract.. SeMemNN can still work on a few-shot learning Table 2 shows that although we have greatly shrank the scale of the training set, our proposed method can still outperform VDCNN. After shrinking the scale of the data, the performance of VDCNN has been greatly decreased, especially for Sogou news, VDCNN has been unable to learn from the training samples." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Testing error of our model and related studies'\nProblem: 'What is the error rate of the proposed model on Sogou(10k) when using SAB-SeMemNN-abs?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/420.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Testing error of our model and related studies" ], "reasoning": "From the table, locate the row for SAB-SeMemNN-abs under the 'our model' section and find the corresponding value in the Sogou(10k) column.", "question": "What is the error rate of the proposed model on Sogou(10k) when using SAB-SeMemNN-abs?", "answer": { "answer": "7.89" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe error rate of VDCNN on AG(5k) is higher than that of the proposed model using SAB-SeMemNN-abs.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/420.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Testing error of our model and related studies" ], "reasoning": "From the table, compare the error rate of VDCNN on AG(5k) (19.25) with the error rate of the proposed model using SAB-SeMemNN-abs on AG(5k) (13.79). Since 19.25 is higher than 13.79, the statement is supported.", "statement": "The error rate of VDCNN on AG(5k) is higher than that of the proposed model using SAB-SeMemNN-abs.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Summary of BLEU scores from Table I.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/421.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Summary of BLEU scores from Table I." ], "reasoning": "The type 2 postfix Transformer achieved the highest testing average of 87.2%, while prefix models had the highest overall average accuracy of 94.43%.", "description": "Table III provides detailed results of Experiment 2. The numbers are absolute accuracies, i.e., they correspond to cases where the arithmetic expression generated is 100% correct, leading to the correct numeric answer. Results by , , , are sparse but indicate the scale of success compared to recent past approaches. Prefix, postfix, and infix representations in Table III show that network capabilities are changed by how teachable the target data is.. While our networks fell short of AI2 testing accuracy, we present state-of-the-art results for the remaining three datasets.. The type 2 postfix Transformer received the highest testing average of 87.2%.. Our attempt at language pre-training fell short of our expectations in all but one tested dataset." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Summary of BLEU scores from Table I.'\nProblem: 'Which model type had the highest average accuracy?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/421.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Summary of BLEU scores from Table I." ], "reasoning": "The table shows that 'All prefix models' had the highest average accuracy of 94.43%.", "question": "Which model type had the highest average accuracy?", "answer": "So, the answer is {\"answer\": \"All prefix models\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAll infix models had the lowest average accuracy.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/421.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Summary of BLEU scores from Table I." ], "reasoning": "The table indicates that 'All infix models' had an average accuracy of 87.72%, which is the lowest among all listed models.", "statement": "All infix models had the lowest average accuracy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Test results for Experiment 2 (* denotes averages on present values only).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/422.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Test results for Experiment 2 (* denotes averages on present values only)." ], "reasoning": "The accuracy values in the last column of Table III are summarized in Table IV, showing a strong correlation with BLEU scores.", "description": "The values in the last column of Table III are summarized in Table IV. How the models compare with respect to accuracy closely resembles the comparison of BLEU scores, presented earlier. Thus, BLEU scores seem to correlate well with accuracy values in our case." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Test results for Experiment 2 (* denotes averages on present values only).'\nProblem: 'Which non-pretrained model has the highest average accuracy?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/422.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Test results for Experiment 2 (* denotes averages on present values only)." ], "reasoning": "To find the non-pretrained model with the highest average accuracy, look at the 'Average' column under the 'Non-pretrained' section. The highest value is 87.2, which corresponds to the (2) Postfix-Transformer model.", "question": "Which non-pretrained model has the highest average accuracy?", "answer": "So, the answer is {\"answer\": \"Postfix-Transformer\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe (2) Postfix-Transformer model has the highest average accuracy among all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/422.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Test results for Experiment 2 (* denotes averages on present values only)." ], "reasoning": "To verify this statement, compare the average accuracy of the (2) Postfix-Transformer model with all other models in the 'Average' column. The highest value is 87.2, which belongs to the (2) Postfix-Transformer model.", "statement": "The (2) Postfix-Transformer model has the highest average accuracy among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Joint goal accuracy on the evaluation dataset of WOZ 2.0 corpus.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/423.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Joint goal accuracy on the evaluation dataset of WOZ 2.0 corpus." ], "reasoning": "The slot-independent SUMBT model significantly outperformed the baseline models, achieving 91.0% joint accuracy.", "description": "The experimental results on WOZ 2.0 corpus are presented in Table 1.. the three baseline models, BERT+RNN, BERT+RNN+Ontology, and the slot-dependent. SUMBT, showed no significant performance differences. On the other hand, the slot-independent SUMBT which learned the shared information from all across domains and slots significantly outperformed those baselines, resulting in 91.0% joint accuracy.. The proposed model achieved state-of-theart performance in both WOZ 2.0 and MultiWOZ datasets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Joint goal accuracy on the evaluation dataset of WOZ 2.0 corpus.'\nProblem: 'Which model achieved the highest joint accuracy and what was the percentage?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/423.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Joint goal accuracy on the evaluation dataset of WOZ 2.0 corpus." ], "reasoning": "The table shows that the slot-independent SUMBT model achieved the highest joint accuracy of 91.0%.", "question": "Which model achieved the highest joint accuracy?", "answer": "So, the answer is {\"answer\": \"Slot-independent SUMBT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BERT+RNN model achieved a joint accuracy of 0.892.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/423.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Joint goal accuracy on the evaluation dataset of WOZ 2.0 corpus." ], "reasoning": "The table shows that the BERT+RNN model achieved a joint accuracy of 0.892.", "statement": "The BERT+RNN model achieved a joint accuracy of 0.892.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Joint goal accuracy on the evaluation dataset of MultiWOZ corpus.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/424.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Joint goal accuracy on the evaluation dataset of MultiWOZ corpus." ], "reasoning": "The SUMBT model achieved a joint accuracy of 42.4% on the MultiWOZ corpus, significantly outperforming previous models.", "description": "Table 2 shows the experimental results of the slot-independent SUMBT model on MultiWOZ corpus.. The SUMBT greatly surpassed the performances of previous approaches by yielding 42.4% joint accuracy." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Joint goal accuracy on the evaluation dataset of MultiWOZ corpus.'\nProblem: 'Which model achieved the highest joint accuracy on the MultiWOZ corpus?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/424.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Joint goal accuracy on the evaluation dataset of MultiWOZ corpus." ], "reasoning": "By comparing the joint accuracy values of all models listed in the table, it is evident that the SUMBT model has the highest joint accuracy of 42.4%.", "question": "Which model achieved the highest joint accuracy on the MultiWOZ corpus?", "answer": "So, the answer is {\"answer\": \"SUMBT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe SUMBT model achieved a joint accuracy of 35.58% on the MultiWOZ corpus.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/424.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Joint goal accuracy on the evaluation dataset of MultiWOZ corpus." ], "reasoning": "The table shows that the SUMBT model achieved a joint accuracy of 42.4%, not 35.58%. Therefore, the statement is incorrect.", "statement": "The SUMBT model achieved a joint accuracy of 35.58% on the MultiWOZ corpus.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Transfer learning results for GLUE tasks. The source corpus is MNLI. Baseline accuracy is when Transfer BERT, Filler, and Role are all False, equivalent to no transfer. Fine-tuned accuracy is the best accuracy among all possible transfer options.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/425.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Transfer learning results for GLUE tasks. The source corpus is MNLI. Baseline accuracy is when Transfer BERT, Filler, and Role are all False, equivalent to no transfer. Fine-tuned accuracy is the best accuracy among all possible transfer options." ], "reasoning": "HUBERT (Transformer) shows substantial gains across all target corpora after transfer learning from MNLI, while BERT shows a drop in accuracy for QNLI, QQP, and SST.", "description": "Table 3 summarizes the results for these transfer learning experiments when the source task is MNLI. Gain shows the difference between Fine-tuned model's accuracy and Baseline's accuracy. For HUBERT (Transformer), we observe substantial gain across all 5 target corpora after transfer. However, for BERT we have a drop for QNLI, QQP, and SST." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Transfer learning results for GLUE tasks. The source corpus is MNLI. Baseline accuracy is when Transfer BERT, Filler, and Role are all False, equivalent to no transfer. Fine-tuned accuracy is the best accuracy among all possible transfer options.'\nProblem: 'Which model shows the highest gain in accuracy for the RTE target corpus?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/425.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Transfer learning results for GLUE tasks. The source corpus is MNLI. Baseline accuracy is when Transfer BERT, Filler, and Role are all False, equivalent to no transfer. Fine-tuned accuracy is the best accuracy among all possible transfer options." ], "reasoning": "By comparing the gain percentages for the RTE target corpus, HUBERT (Transformer) shows a gain of +12.28%, which is higher than BERT's gain of +2.53%.", "question": "Which model shows the highest gain in accuracy for the RTE target corpus?", "answer": "So, the answer is {\"answer\": \"HUBERT (Transformer)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBERT shows a gain in accuracy for the QNLI target corpus.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/425.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Transfer learning results for GLUE tasks. The source corpus is MNLI. Baseline accuracy is when Transfer BERT, Filler, and Role are all False, equivalent to no transfer. Fine-tuned accuracy is the best accuracy among all possible transfer options." ], "reasoning": "The table shows that BERT has a gain of -0.33% for the QNLI target corpus, indicating an decrease in accuracy.", "statement": "BERT shows a gain in accuracy for the QNLI target corpus.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Transfer learning results for GLUE tasks. The source corpus is QQP. Baseline accuracy is for when Transfer BERT, Filler, and Role are all False, which is equivalent to no transfer. Fine-tuned accuracy is the best accuracy among all possible transfer options.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/426.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Transfer learning results for GLUE tasks. The source corpus is QQP. Baseline accuracy is for when Transfer BERT, Filler, and Role are all False, which is equivalent to no transfer. Fine-tuned accuracy is the best accuracy among all possible transfer options." ], "reasoning": "Transferring roles improves results except for RTE, while filler vectors from QQP are more transferable than MNLI, boosting all tasks except SNLI. Transferring BERT parameters now hurts results even with TPR.", "description": "Again transferring roles gives positive results except for RTE. Filler vectors learned from QQP are more transferable compared to MNLI and gives a boost to all tasks except for SNLI. Surprisingly, transferring BERT parameters is hurting the results now even when TPR is present." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Transfer learning results for GLUE tasks. The source corpus is QQP. Baseline accuracy is for when Transfer BERT, Filler, and Role are all False, which is equivalent to no transfer. Fine-tuned accuracy is the best accuracy among all possible transfer options.'\nProblem: 'Which model and target corpus combination had the highest gain percentage when Transfer Role was true?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/426.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Transfer learning results for GLUE tasks. The source corpus is QQP. Baseline accuracy is for when Transfer BERT, Filler, and Role are all False, which is equivalent to no transfer. Fine-tuned accuracy is the best accuracy among all possible transfer options." ], "reasoning": "Identify the rows where Transfer Role is true and compare the Gain (%) values. The highest gain percentage is for HUBERT (Transformer) with SST.", "question": "Which model and target corpus combination had the highest gain percentage when Transfer Role was true?", "answer": "So, the answer is {\"answer\": \"HUBERT (Transformer) with SST\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTransferring BERT parameters improves results for all tasks.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/426.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Transfer learning results for GLUE tasks. The source corpus is QQP. Baseline accuracy is for when Transfer BERT, Filler, and Role are all False, which is equivalent to no transfer. Fine-tuned accuracy is the best accuracy among all possible transfer options." ], "reasoning": "The table shows that transferring BERT parameters (Transfer BERT: True) does not improve results for all tasks, as seen with RTE where the gain is negative.", "statement": "Transferring BERT parameters improves results for all tasks.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Test set results for HUBERT (Transformer) and BERT. BERT accuracy indicates test results on target corpus (without transfer) for bert-base-uncased which are directly taken from the GLUE leaderboard. Fine-tuned accuracy are the test results for best performing HUBERT (Transformer) model on target dev set after transfer (see Tables 3 and 4).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/427.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Test set results for HUBERT (Transformer) and BERT. BERT accuracy indicates test results on target corpus (without transfer) for bert-base-uncased which are directly taken from the GLUE leaderboard. Fine-tuned accuracy are the test results for best performing HUBERT (Transformer) model on target dev set after transfer (see Tables 3 and 4)." ], "reasoning": "The table compares the test set results of HUBERT (Transformer) and BERT on various source and target corpora, showing the accuracy percentages for each model.", "description": "We also verified that our TPR layer is not hurting the performance by comparing the test set results for HUBERT (Transformer) and BERT. The results are obtained by submitting models to the GLUE evaluation server. The results are presented in Table 5." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Test set results for HUBERT (Transformer) and BERT. BERT accuracy indicates test results on target corpus (without transfer) for bert-base-uncased which are directly taken from the GLUE leaderboard. Fine-tuned accuracy are the test results for best performing HUBERT (Transformer) model on target dev set after transfer (see Tables 3 and 4).'\nProblem: 'Which model achieved the highest accuracy on the SST target corpus?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/427.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Test set results for HUBERT (Transformer) and BERT. BERT accuracy indicates test results on target corpus (without transfer) for bert-base-uncased which are directly taken from the GLUE leaderboard. Fine-tuned accuracy are the test results for best performing HUBERT (Transformer) model on target dev set after transfer (see Tables 3 and 4)." ], "reasoning": "To determine which model achieved the highest accuracy on the SST target corpus, compare the accuracy percentages of BERT and HUBERT for the SST target corpus. BERT achieved 93.50% accuracy, while HUBERT achieved 92.60% accuracy.", "question": "Which model achieved the highest accuracy on the SST target corpus?", "answer": "So, the answer is {\"answer\": \"BERT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nHUBERT achieved higher accuracy than BERT on the QQP target corpus.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/427.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Test set results for HUBERT (Transformer) and BERT. BERT accuracy indicates test results on target corpus (without transfer) for bert-base-uncased which are directly taken from the GLUE leaderboard. Fine-tuned accuracy are the test results for best performing HUBERT (Transformer) model on target dev set after transfer (see Tables 3 and 4)." ], "reasoning": "To verify this statement, compare the accuracy percentages of HUBERT and BERT for the QQP target corpus. HUBERT achieved 89.30% accuracy, while BERT achieved 89.20% accuracy.", "statement": "HUBERT achieved higher accuracy than BERT on the QQP target corpus.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Model performance results on the tasks of interest. Best values for each metric are bolded.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/428.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Model performance results on the tasks of interest. Best values for each metric are bolded." ], "reasoning": "The SHiP models significantly improved over the BOW baselines on diagnosis tasks, but showed negligible benefit for mortality prediction. Pretraining improved performance for both hierarchical and SHiP models.", "description": "Table 1 compares the performance of all model variants. The SHiP models significantly improved over the BOW baselines on the two diagnosis tasks (p < 0.001 under Welch's t-test): for CCS prediction, the best SHiP models improved top-1 recall by 7.9 percentage points and top-5 recall by 4.8 percentage points, respectively, over the best BOW models; for ICD-9 prediction, area under the precision-recall curve (AUPRC) increased by 2.1 percentage points and weighted area under the ROC curve (AUROC) increased by 0.8 percentage points. For mortality prediction, we saw negligible benefit from the SHiP architecture. The SHiP models also improved over the corresponding hierarchical models without pretraining. For mortality, pretraining the all-features model increased AUPRC by 0.8 percentage points (p = 0.06) and AUROC by 0.6 percentage points (p = 0.004); for primary CCS, pretraining the all-feature model increased top-1 recall by 8.0 percentage points (p < 0.001), while pretraining the notes-only model increased top-5 recall by 4.7 percentage points (p < 0.001); for all ICD-9, pretraining the notes-only model increased AUPRC by 0.7 percentage points (p = 0.03) and weighted AUROC by 0.4 percentage points (p = 0.01)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Model performance results on the tasks of interest. Best values for each metric are bolded.'\nProblem: 'What is the improvement in Primary CCS Top-1 Recall for the SHiP all features model compared to the Bag-of-words unigrams (all features) model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/428.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Model performance results on the tasks of interest. Best values for each metric are bolded." ], "reasoning": "The Primary CCS Top-1 Recall for the SHiP all features model is 0.671, while for the Bag-of-words unigrams (all features) model it is 0.592. The improvement is 0.671 - 0.592 = 0.079 or 7.9 percentage points.", "question": "What is the improvement in Primary CCS Top-1 Recall for the SHiP all features model compared to the Bag-of-words unigrams (all features) model?", "answer": "So, the answer is {\"answer\": \"0.079\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe SHiP models showed significant improvement in mortality prediction over the Bag-of-words models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/428.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Model performance results on the tasks of interest. Best values for each metric are bolded." ], "reasoning": "The SHiP models showed negligible benefit for mortality prediction, as stated in the description. The table also shows that the Mortality AUPRC and AUROC for SHiP models are similar to or slightly better than those for Bag-of-words models, but not significantly.", "statement": "The SHiP models showed significant improvement in mortality prediction over the Bag-of-words models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: The compression results including model efficiency and accuracy from the GLUE test server, and the MNLI result is evaluated for matched-accuracy (MNLI-m). BERT12 indicates the results of the fine-tuned BERT-base from Devlin et al. (2019) and BERT12-T indicates the results of the fine-tuned BERT-base in our implementation. The results of BERT-PKD are from Sun et al. (2019), the results of DistilBERT4 and TinyBERT4 are from Jiao et al. (2019), and the results of BiLSTMSOFT is from Tang et al. (2019). The number of model parameters includes the embedding size, and the inference time is tested with a batch size of 128 over 50,000 samples. The bold numbers and underlined numbers indicate the best and the second-best performance respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/429.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The compression results including model efficiency and accuracy from the GLUE test server, and the MNLI result is evaluated for matched-accuracy (MNLI-m). BERT12 indicates the results of the fine-tuned BERT-base from Devlin et al. (2019) and BERT12-T indicates the results of the fine-tuned BERT-base in our implementation. The results of BERT-PKD are from Sun et al. (2019), the results of DistilBERT4 and TinyBERT4 are from Jiao et al. (2019), and the results of BiLSTMSOFT is from Tang et al. (2019). The number of model parameters includes the embedding size, and the inference time is tested with a batch size of 128 over 50,000 samples. The bold numbers and underlined numbers indicate the best and the second-best performance respectively." ], "reasoning": "AdaBERT achieves significant efficiency improvements with 11.5x to 17.0x smaller parameter size and 12.7x to 29.3x faster inference speed, while maintaining comparable performance with an average degradation of 2.79%.", "description": "The compression results on the six adopted datasets, including parameter size, inference speedup and classification accuracy, are summarized in Table 1.. Overall speaking, on all the evaluated datasets, the proposed AdaBERT method achieves significant efficiency improvement while maintaining comparable performance. Compared to the BERT12-T, the compressed models are 11.5x to 17.0x smaller in parameter size and 12.7x to 29.3x faster in inference speed with an average performance degradation of 2.79%.. Comparing structureheterogeneous method, BiLSTMSOF. , AdaBERT searches CNN-based models and achieves much another with better improvements, especially on the MNLI dataset.. Comparing with different Transformers-based compression baselines, the proposed AdaBERT method is 1.35x to 3.12x faster than the fastest baseline, TinyBERT4, and achieves comparable performance with the two baselines that have the best averaged accuracy, BERT6-PKD and TinyBERT4." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: The compression results including model efficiency and accuracy from the GLUE test server, and the MNLI result is evaluated for matched-accuracy (MNLI-m). BERT12 indicates the results of the fine-tuned BERT-base from Devlin et al. (2019) and BERT12-T indicates the results of the fine-tuned BERT-base in our implementation. The results of BERT-PKD are from Sun et al. (2019), the results of DistilBERT4 and TinyBERT4 are from Jiao et al. (2019), and the results of BiLSTMSOFT is from Tang et al. (2019). The number of model parameters includes the embedding size, and the inference time is tested with a batch size of 128 over 50,000 samples. The bold numbers and underlined numbers indicate the best and the second-best performance respectively.'\nProblem: 'What is the average performance degradation of the proposed AdaBERT method compared to BERT12-T?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/429.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The compression results including model efficiency and accuracy from the GLUE test server, and the MNLI result is evaluated for matched-accuracy (MNLI-m). BERT12 indicates the results of the fine-tuned BERT-base from Devlin et al. (2019) and BERT12-T indicates the results of the fine-tuned BERT-base in our implementation. The results of BERT-PKD are from Sun et al. (2019), the results of DistilBERT4 and TinyBERT4 are from Jiao et al. (2019), and the results of BiLSTMSOFT is from Tang et al. (2019). The number of model parameters includes the embedding size, and the inference time is tested with a batch size of 128 over 50,000 samples. The bold numbers and underlined numbers indicate the best and the second-best performance respectively." ], "reasoning": "The description states that the compressed models are 11.5x to 17.0x smaller in parameter size and 12.7x to 29.3x faster in inference speed with an average performance degradation of 2.79%.", "question": "What is the average performance degradation of the proposed AdaBERT method compared to BERT12-T?", "answer": { "answer": "2.79" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAdaBERT achieves the highest accuracy on the SST-2 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/429.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The compression results including model efficiency and accuracy from the GLUE test server, and the MNLI result is evaluated for matched-accuracy (MNLI-m). BERT12 indicates the results of the fine-tuned BERT-base from Devlin et al. (2019) and BERT12-T indicates the results of the fine-tuned BERT-base in our implementation. The results of BERT-PKD are from Sun et al. (2019), the results of DistilBERT4 and TinyBERT4 are from Jiao et al. (2019), and the results of BiLSTMSOFT is from Tang et al. (2019). The number of model parameters includes the embedding size, and the inference time is tested with a batch size of 128 over 50,000 samples. The bold numbers and underlined numbers indicate the best and the second-best performance respectively." ], "reasoning": "The table shows that TinyBERT4 achieves the highest accuracy on the SST-2 dataset with 92.6%, while AdaBERT achieves 91.8%.", "statement": "AdaBERT achieves the highest accuracy on the SST-2 dataset.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Accuracy comparison on the dev sets with the searched compression structures applying to different tasks. For Random, 5-times averaging results with standard deviations are reported.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/430.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy comparison on the dev sets with the searched compression structures applying to different tasks. For Random, 5-times averaging results with standard deviations are reported." ], "reasoning": "The searched structures perform best on their original target tasks, as indicated by the highest performance numbers along the diagonal, and performance degradation is significant across different task types.", "description": "The results of cross-task validation is summarized in Table 3. From Table 3, we can observe that: The searched structures achieve the best performance on their original target tasks compared with other tasks, in other words, the performance numbers along the diagonal line of this table are the best. Further, the performance degradation is quite significant across different kinds of tasks (for example, applying the searched structures of sentiment classification tasks to entailment recognition task, or vice verse), while the performance degradations within the same kind. of tasks (for example, MRPC and QQP for semantic equivalence classification) are relatively small, since they have the same input format (i.e., a pair of sentences) and similar targets.. From the last row of Table 3, we can see that the randomly sampled structures perform worse than the searched structures and their performances are not stable." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Accuracy comparison on the dev sets with the searched compression structures applying to different tasks. For Random, 5-times averaging results with standard deviations are reported.'\nProblem: 'Which structure achieves the highest performance on the QNLI task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/430.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy comparison on the dev sets with the searched compression structures applying to different tasks. For Random, 5-times averaging results with standard deviations are reported." ], "reasoning": "By examining the QNLI column, the highest performance is achieved by the AdaBERT-QNLI structure with a score of 87.2.", "question": "Which structure achieves the highest performance on the QNLI task?", "answer": { "answer": "AdaBERT-QNLI" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe performance of the AdaBERT-MRPC structure on the MRPC task is better than on the RTE task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/430.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy comparison on the dev sets with the searched compression structures applying to different tasks. For Random, 5-times averaging results with standard deviations are reported." ], "reasoning": "The table shows that the AdaBERT-MRPC structure has a performance score of 84.7 on the MRPC task and 60.3 on the RTE task, indicating better performance on the MRPC task.", "statement": "The performance of the AdaBERT-MRPC structure on the MRPC task is better than on the RTE task.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: The effect of efficiency loss term.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/431.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The effect of efficiency loss term." ], "reasoning": "Removing the efficiency-aware loss increases model size, while a high efficiency preference reduces model size but degrades performance; a moderate efficiency constraint balances size and performance.", "description": "The model performance and corresponding model size are reported in Table 5. On the one hand, removing the efficiency-aware loss (β = 0) leads to the increase in model parameter size, on the other hand, a more aggressive efficiency preference (β = 8) results in the small model size but degraded performance, since a large β encourages the compressed model to adopt more lightweight operations such as zero and skip which hurt the performance. A moderate efficiency constraint (β = 4) provides a regularization, guiding the AdaBERT method to achieve a trade-off between the small parameter size and the good performance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: The effect of efficiency loss term.'\nProblem: 'What is the performance on the RTE dataset when β is set to 4?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/431.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The effect of efficiency loss term." ], "reasoning": "To find the performance on the RTE dataset when β is set to 4, locate the row where β = 4 and then find the corresponding value under the RTE column.", "question": "What is the performance on the RTE dataset when β is set to 4?", "answer": "So, the answer is {\"answer\": \"64.1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model size for the QNLI dataset is smallest when β is set to 8.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/431.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The effect of efficiency loss term." ], "reasoning": "To verify this statement, compare the model sizes for the QNLI dataset across different β values. The smallest size is found when β = 8, which is 7.1M.", "statement": "The model size for the QNLI dataset is smallest when β is set to 8.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: The effect of knowledge loss terms.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/432.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The effect of knowledge loss terms." ], "reasoning": "Incorporating probe models and data augmentation improves performance across tasks, with the greatest gains seen in tasks with limited data, and further improvements are achieved by considering supervised label knowledge.", "description": "The Base-KD is a naive knowledge distillation version in which only the logits of the last layer are distilled without considering hidden layer knowledge and supervised label knowledge. By incorporating the probe models, the performance (line 2 in Table 4) is consistently improved, indicating the benefits from hierarchically decomposed taskoriented knowledge. We then leverage Data Augmentation (DA) to enrich task-oriented knowledge and this technique also improves performance for all tasks, especially for tasks that have a limited scale of data (i.e., MRPC and RTE). DA is also adopted in existing KD-based compression studies (Tang et al., 2019; Jiao et al., 2019). When taking the supervised label knowledge (LCE) into consideration, the performance is further boosted, showing that this term is also important for AdaBERT by providing focused search hints." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: The effect of knowledge loss terms.'\nProblem: 'What is the performance improvement for the RTE task when Data Augmentation (DA) is applied?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/432.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The effect of knowledge loss terms." ], "reasoning": "The performance for the RTE task with Base-KD is 56.7. When Data Augmentation (DA) is applied, the performance improves to 63.2. The improvement is calculated as 63.2 - 56.7.", "question": "What is the performance improvement for the RTE task when Data Augmentation (DA) is applied?", "answer": { "answer": "6.5" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe performance of the MRPC task improves the most when Data Augmentation (DA) is applied.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/432.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The effect of knowledge loss terms." ], "reasoning": "The performance of the MRPC task with Base-KD is 77.2, and with DA it is 83.9, an improvement of 6.7. Comparing this with other tasks, the RTE task improves from 56.7 to 63.2 (6.5), the QNLI task from 82.0 to 86.5 (4.5), and the SST-2 task from 86.6 to 91.4 (4.8). Thus, MRPC has the highest improvement.", "statement": "The performance of the MRPC task improves the most when Data Augmentation (DA) is applied.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Sentiment prediction results on CMU-MOSI. SOTA1, SOTA2 and SOTA3 refer to the previous best, second best and third best state of the art models respectively. Best results are highlighted in bold and ΔSOTA represents the change in performance of M-BERT model over SOTA1. Our model significantly outperforms the current SOTA across all evaluation metrics.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/433.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Sentiment prediction results on CMU-MOSI. SOTA1, SOTA2 and SOTA3 refer to the previous best, second best and third best state of the art models respectively. Best results are highlighted in bold and ΔSOTA represents the change in performance of M-BERT model over SOTA1. Our model significantly outperforms the current SOTA across all evaluation metrics." ], "reasoning": "M-BERT achieves the highest binary accuracy (84.38%) and F1 score (86.34%) on the CMU-MOSI dataset, outperforming previous state-of-the-art models and BERT.", "description": "Our proposed approach sets a new state of the art of 84.38% binary accuracy on CMU-MOSI dataset of multimodal sentiment analysis; a significant leap from previous state of. We compare the performance of M-BERT with the following models on the multimodal sentiment analysis task: RMFN (SOTA1)1 fuses multimodal information in multiple stages by focusing on a subset of signals in each stage (Liang et al., 2018). MFN (SOTA2) synchronizes states of three separate LSTMs with a multi-view gated memory (Zadeh et al., 2018a). MARN (SOTA3) models view-specific interactions using hybrid LSTM memories and cross-modal interactions using a Multi-Attention Block(MAB) (Zadeh et al., 2018c).. We perform two different evaluation tasks on CMU-MOSI datset: i) Binary Classification, and ii) Regression. We formulate it as a regression problem and report Mean-absolute Error (MAE) and the correlation of model predictions with true labels. Besides, we convert the regression outputs into categorical values to obtain binary classification accuracy (BA) and F1 score.. The performances of M-BERT and BERT are described in Table 1.. M-BERT model outperforms all the baseline models (described in Sec.4.4) on every evaluation metrics with large margin. It sets new state-of-the-art performance for this task and achieves 84.38% accuracy, a 5.98% increase with respect to the SOTA1 and 1.02% increase with respect to BERT (text-only).. Even BERT (text-only) model achieves 83.36% accuracy, an increase of 4.96% from the SOTA1 78.4%, using text information only. It achieves higher performance in all evaluation metrics compare to SOTA1; reinforcing the expressiveness and utility of BERT contextual representation." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Sentiment prediction results on CMU-MOSI. SOTA1, SOTA2 and SOTA3 refer to the previous best, second best and third best state of the art models respectively. Best results are highlighted in bold and ΔSOTA represents the change in performance of M-BERT model over SOTA1. Our model significantly outperforms the current SOTA across all evaluation metrics.'\nProblem: 'What is the increase in binary accuracy (BA) of M-BERT compared to SOTA1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/433.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Sentiment prediction results on CMU-MOSI. SOTA1, SOTA2 and SOTA3 refer to the previous best, second best and third best state of the art models respectively. Best results are highlighted in bold and ΔSOTA represents the change in performance of M-BERT model over SOTA1. Our model significantly outperforms the current SOTA across all evaluation metrics." ], "reasoning": "The table shows that M-BERT has a binary accuracy (BA) of 84.38%, while SOTA1 has a BA of 78.4%. The increase is calculated as 84.38% - 78.4% = 5.98%.", "question": "What is the increase in binary accuracy (BA) of M-BERT compared to SOTA1?", "answer": "So, the answer is {\"answer\": \"5.98\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nM-BERT has a higher correlation (Corr) score than BERT.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/433.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Sentiment prediction results on CMU-MOSI. SOTA1, SOTA2 and SOTA3 refer to the previous best, second best and third best state of the art models respectively. Best results are highlighted in bold and ΔSOTA represents the change in performance of M-BERT model over SOTA1. Our model significantly outperforms the current SOTA across all evaluation metrics." ], "reasoning": "The table shows that M-BERT has a correlation (Corr) score of 0.790, while BERT has a Corr score of 0.777. Therefore, M-BERT has a higher Corr score than BERT.", "statement": "M-BERT has a higher correlation (Corr) score than BERT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Inference times in ms'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/434.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Inference times in ms" ], "reasoning": "The Gaussian Mask only model has the lowest inference times across all sentence lengths with negligible differences in BLEU scores.", "description": "We find the inference times are lower in the former case with negligible difference in BLEU scores.. The inference times are of the various models are mentioned in Table 1." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Inference times in ms'\nProblem: 'Which model has the lowest inference time for sentences of length 8-11?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/434.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Inference times in ms" ], "reasoning": "For sentences of length 8-11, the Gaussian Mask only model has an inference time of 1.96, which is lower than the Attention model (2.32) and the Gaussian Mask + RL model (2.12).", "question": "Which model has the lowest inference time for sentences of length 8-11?", "answer": { "answer": "Gaussian Mask only" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Attention model has the highest inference time for sentences of length 12-15.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/434.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Inference times in ms" ], "reasoning": "For sentences of length 12-15, the Attention model has an inference time of 2.86, which is higher than both the Gaussian Mask only model (2.43) and the Gaussian Mask + RL model (2.61).", "statement": "The Attention model has the highest inference time for sentences of length 12-15.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Number of images after which the ME Score falls below threshold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/435.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Number of images after which the ME Score falls below threshold." ], "reasoning": "The ME score for classifiers is lower than the baseline ME measure in the dataset, with the ImageNet classifier dropping below 0.05 after 8,960 images, while the dataset encounters new classes at this rate until 111,000 images.", "description": "The results are summarized in Figure 6 and Table 3. The probability that a new image belongs to an unseen class P (N |t) is higher than the ME score of the classifier through most of the learning phase. Comparing the statistics of the datasets to the inductive biases in the classifiers, the ME score for the classifiers is substantially lower than the baseline ME measure in the dataset, P (N |t) (Table 3). For instance, the ImageNet classifier drops its ME score below 0.05 after about 8,960 images, while the approximate ME measure for the dataset shows that new classes are encountered at above this rate until at least 111,000 images." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Number of images after which the ME Score falls below threshold.'\nProblem: 'At what score does the ImageNet classifier drop below the baseline ME measure for the dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/435.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Number of images after which the ME Score falls below threshold." ], "reasoning": "The table shows that the ImageNet classifier drops its ME score below 0.05 after 8,960 images, while the dataset encounters new classes at this rate until 111,000 images.", "question": "At what score does the ImageNet classifier drop below the baseline ME measure for the dataset?", "answer": "So, the answer is {\"answer\": \"0.05\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Omniglot classifier drops its ME score below 0.1 after 22,912 images.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/435.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Number of images after which the ME Score falls below threshold." ], "reasoning": "The table shows that the Omniglot classifier drops its ME score below 0.1 after 22,912 images.", "statement": "The Omniglot classifier drops its ME score below 0.1 after 22,912 images.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Success rate (in %) and number of replacements for different methods. NOR represents the mean/median of the normalized Number Of Replacements across all the sentences. The highest Success rate is marked in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/436.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Success rate (in %) and number of replacements for different methods. NOR represents the mean/median of the normalized Number Of Replacements across all the sentences. The highest Success rate is marked in bold." ], "reasoning": "Min-Grad method significantly improves success rate compared to the random baseline, and Soft-Att outperforms HotFlip in both success rate and number of replacements across all NMT models.", "description": "Table IV shows the success rate and the mean, median of the number of replacements (normalized by the length of original sentence) for different methods.. As we can see from Table IV, for both Hotflip and Soft-Att, Min-Grad method gives significant improvement in success rate in comparison with random baseline across all the NMT models. The number of replacement for Min-Grad is comparable with random.. Table IV shows that Transformer is more robust to our proposed method than BLSTM. This is because our proposed method has less number of replacements and lower success rate in case of Transformer than BLSTM for both the language pairs. Interestingly, HotFlip has higher success rate and similar number of replacement in case for Transformer than BLSTM. Overall, as is evident from Table IV, our proposed method (Min-Grad + Soft-Att) achieves the highest success rate across the NMT models.. From Table IV, across all the NMT models, we can see that Soft-Att significantly outperforms HotFlip both in terms of success rate and number of replacements." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Success rate (in %) and number of replacements for different methods. NOR represents the mean/median of the normalized Number Of Replacements across all the sentences. The highest Success rate is marked in bold.'\nProblem: 'Which method achieves the highest success rate for the BLSTM model in the en-fr language pair?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/436.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Success rate (in %) and number of replacements for different methods. NOR represents the mean/median of the normalized Number Of Replacements across all the sentences. The highest Success rate is marked in bold." ], "reasoning": "From the table, for the BLSTM model in the en-fr language pair, the highest success rate is achieved by the Min-Grad + Soft-Att method with a success rate of 70.8%.", "question": "Which method achieves the highest success rate for the BLSTM model in the en-fr language pair?", "answer": "So, the answer is {\"answer\": \"Min-Grad + Soft-Att\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Min-Grad + Soft-Att method achieves the highest success rate for the Transformer model in the en-de language pair.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/436.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Success rate (in %) and number of replacements for different methods. NOR represents the mean/median of the normalized Number Of Replacements across all the sentences. The highest Success rate is marked in bold." ], "reasoning": "From the table, for the Transformer model in the en-de language pair, the highest success rate is achieved by the Min-Grad + Soft-Att method with a success rate of 61.6%.", "statement": "The Min-Grad + Soft-Att method achieves the highest success rate for the Transformer model in the en-de language pair.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: BLEU scores for the original/adversarial sentence (src) and their respective translation by the four NMT models. l1 denotes the model under attack, l2 denotes the other Transformer model. lblstm1,lblstm2 are the BLSTM counterparts of l1 and l2.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/437.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: BLEU scores for the original/adversarial sentence (src) and their respective translation by the four NMT models. l1 denotes the model under attack, l2 denotes the other Transformer model. lblstm1,lblstm2 are the BLSTM counterparts of l1 and l2." ], "reasoning": "Table V shows the BLEU scores for original and adversarial sentences and their translations by four NMT models, with l1 and l2 representing Transformer models and lblstm1 and lblstm2 representing BLSTM counterparts.", "description": "1 2 , lblstm Table V shows the BLEU scores for the original/adversarial sentence (src) and their respective translation by the four NMT models. In Table V, l1 denotes the Transformer model under attack (e.g. en-de), l2 denotes the other Transformer model (e.g. en-fr), and lblstm are the BLSTM counterparts of l1 and l2." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: BLEU scores for the original/adversarial sentence (src) and their respective translation by the four NMT models. l1 denotes the model under attack, l2 denotes the other Transformer model. lblstm1,lblstm2 are the BLSTM counterparts of l1 and l2.'\nProblem: 'What is the highest BLEU score for the en-fr Transformer model using the Min-Grad + Soft-Att method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/437.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: BLEU scores for the original/adversarial sentence (src) and their respective translation by the four NMT models. l1 denotes the model under attack, l2 denotes the other Transformer model. lblstm1,lblstm2 are the BLSTM counterparts of l1 and l2." ], "reasoning": "To find the highest BLEU score for the en-fr Transformer model using the Min-Grad + Soft-Att method, locate the en-fr row and the Min-Grad + Soft-Att method column, then identify the highest score among l1, l2, lblstm1, and lblstm2.", "question": "What is the highest BLEU score for the en-fr Transformer model using the Min-Grad + Soft-Att method?", "answer": "So, the answer is {\"answer\": \"91.99\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe en-de Transformer model using the random + HotFlip method has a higher BLEU score than the en-fr Transformer model using the same method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/437.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: BLEU scores for the original/adversarial sentence (src) and their respective translation by the four NMT models. l1 denotes the model under attack, l2 denotes the other Transformer model. lblstm1,lblstm2 are the BLSTM counterparts of l1 and l2." ], "reasoning": "Compare the BLEU scores for the en-de and en-fr Transformer models using the random + HotFlip method. The en-de model has a score of 80.49, while the en-fr model has a score of 85.18.", "statement": "The en-de Transformer model using the random + HotFlip method has a higher BLEU score than the en-fr Transformer model using the same method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VI: BLEU scores for the original/adversarial sentence (src) and their respective translation by the four NMT models. l1 denotes the model under attack, l2 denotes the other BLSTM model. ltrans1,ltrans2 are the Transformer counterparts of l1 and l2.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/438.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: BLEU scores for the original/adversarial sentence (src) and their respective translation by the four NMT models. l1 denotes the model under attack, l2 denotes the other BLSTM model. ltrans1,ltrans2 are the Transformer counterparts of l1 and l2." ], "reasoning": "Table VI presents BLEU scores for original and adversarial sentences and their translations by four NMT models, including two BLSTM models and their Transformer counterparts.", "description": "Table VI shows the BLEU scores for the original/adversarial sentence (src) and their respective translation by the four NMT models. In Table VI, l1 denotes the BLSTM model under attack, l2 denotes the other BLSTM model, and ltrans are the Transformer counterparts of l1 and l2. , ltrans 1 2" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VI: BLEU scores for the original/adversarial sentence (src) and their respective translation by the four NMT models. l1 denotes the model under attack, l2 denotes the other BLSTM model. ltrans1,ltrans2 are the Transformer counterparts of l1 and l2.'\nProblem: 'Which method and language pair combination resulted in the highest BLEU score for the l1 model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/438.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: BLEU scores for the original/adversarial sentence (src) and their respective translation by the four NMT models. l1 denotes the model under attack, l2 denotes the other BLSTM model. ltrans1,ltrans2 are the Transformer counterparts of l1 and l2." ], "reasoning": "To find the highest BLEU score for the l1 model, we need to compare all the BLEU scores under the l1 column for each method and language pair combination. The highest score is 93.87 for the en-fr language pair using the Min-Grad + Soft-Att method.", "question": "Which method and language pair combination resulted in the highest BLEU score for the l1 model?", "answer": { "answer": "Min-Grad + Soft-Att for en-fr" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe en-de language pair using the random + Soft-Att method resulted in the lowest BLEU score for the src.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/438.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: BLEU scores for the original/adversarial sentence (src) and their respective translation by the four NMT models. l1 denotes the model under attack, l2 denotes the other BLSTM model. ltrans1,ltrans2 are the Transformer counterparts of l1 and l2." ], "reasoning": "To verify this statement, we need to compare all the BLEU scores under the src column for each method and language pair combination. The lowest score is 13.77 for the en-de language pair using the random + Soft-Att method.", "statement": "The en-de language pair using the random + Soft-Att method resulted in the lowest BLEU score for the src.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: SacreBleu degradation as a function of the proportion of bitext data that is noised.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/439.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: SacreBleu degradation as a function of the proportion of bitext data that is noised." ], "reasoning": "Noising the EnDe bitext sources up to 100% results in only a minor drop in BLEU scores, indicating that the translation quality remains relatively stable.", "description": "We first show that noising EnDe bitext sources does not seriously impact the translation quality of the transformer-base baseline.. Table 2 shows results for various values of p. Specifically, it presents the somewhat unexpected finding that even when noising 100% of the source bitext (so the model has never seen wellformed English), BLEU on well-formed test data only drops by 2.5." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: SacreBleu degradation as a function of the proportion of bitext data that is noised.'\nProblem: 'What is the BLEU score for Newstest '17 when 80% of the source bitext is noised?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/439.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: SacreBleu degradation as a function of the proportion of bitext data that is noised." ], "reasoning": "To find the BLEU score for Newstest '17 when 80% of the source bitext is noised, look at the row corresponding to 80% noised and the column for SacreBleu Newstest '17.", "question": "What is the BLEU score for Newstest '17 when 80% of the source bitext is noised?", "answer": "So, the answer is {\"answer\": \"27.0\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nNoising 20% of the source bitext results in a BLEU score of 22.4 for Newstest '12.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/439.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: SacreBleu degradation as a function of the proportion of bitext data that is noised." ], "reasoning": "The table shows that when 20% of the source bitext is noised, the BLEU score for Newstest '12 is indeed 22.4.", "statement": "Noising 20% of the source bitext results in a BLEU score of 22.4 for Newstest '12.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Comparing SacreBleu scores for different flavors of BT for WMT16 EnRo. Previous works’ scores are reported in italics as they use detok.multi-bleu instead of SacreBleu, so are not guaranteed to be comparable. In this case, however, we do see identical Bleu on our systems when we score them with detok.multi-bleu, so we believe it to be a fair comparison.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/440.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparing SacreBleu scores for different flavors of BT for WMT16 EnRo. Previous works’ scores are reported in italics as they use detok.multi-bleu instead of SacreBleu, so are not guaranteed to be comparable. In this case, however, we do see identical Bleu on our systems when we score them with detok.multi-bleu, so we believe it to be a fair comparison." ], "reasoning": "TaggedBT improves over standard BT by +0.4 BLEU, and Iterative TaggedBT achieves the highest score of 33.4 BLEU.", "description": "We repeat these experiments for WMT EnRo (Table 4).. In this case, NoisedBT is actually harmful, lagging standard BT by -0.6 BLEU. TaggedBT closes this gap and passes standard BT by +0.4 BLEU, for a total gain of +1.0 BLEU over NoisedBT.. We further investigate the effects of TaggedBT by performing one round of iterative backtranslation (Cotterell and Kreutzer, 2018; Vu Cong Duy Hoang and Cohn, 2018; Niu et al., 2018), and find another difference between the different varieties of BT: NoisedBT and TaggedBT allow the model to bootstrap improvements from an improved reverse model, whereas standard BT does not.. SacreBLEU scores for all these models are displayed in Table 4.. We find that the iteration-3 BT models improve over their Iteration-1 counterparts only for NoisedBT (+1.0 BLEU, dev+test avg) and TaggedBT (+0.7 BLEU, dev+test avg), whereas the Iteration-3 BT model shows no improvement over its Iteration-1 counterpart (-0.1 BLEU, dev+test avg).. iteration-3 TaggedBT (Table 4),. Our best BLEU score of 33.4 BLEU, obtained using Iterative TaggedBT, shows a gain of +3.5 BLEU over the highest previously published result on this test-set that we are aware of.. We furthermore match or out-perform the highest published results we are aware of on WMT EnDe that use only back-translation, with higher or equal BLEU on five of seven test sets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Comparing SacreBleu scores for different flavors of BT for WMT16 EnRo. Previous works’ scores are reported in italics as they use detok.multi-bleu instead of SacreBleu, so are not guaranteed to be comparable. In this case, however, we do see identical Bleu on our systems when we score them with detok.multi-bleu, so we believe it to be a fair comparison.'\nProblem: 'What is the BLEU score improvement of Iterative TaggedBT over standard BT in the test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/440.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparing SacreBleu scores for different flavors of BT for WMT16 EnRo. Previous works’ scores are reported in italics as they use detok.multi-bleu instead of SacreBleu, so are not guaranteed to be comparable. In this case, however, we do see identical Bleu on our systems when we score them with detok.multi-bleu, so we believe it to be a fair comparison." ], "reasoning": "Iterative TaggedBT has a BLEU score of 33.4, while standard BT has a score of 32.6. The improvement is 33.4 - 32.6 = 0.8 BLEU.", "question": "What is the BLEU score improvement of Iterative TaggedBT over standard BT in the test set?", "answer": { "answer": "0.8" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIterative TaggedBT achieves a BLEU score of 33.4 in the test set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/440.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparing SacreBleu scores for different flavors of BT for WMT16 EnRo. Previous works’ scores are reported in italics as they use detok.multi-bleu instead of SacreBleu, so are not guaranteed to be comparable. In this case, however, we do see identical Bleu on our systems when we score them with detok.multi-bleu, so we believe it to be a fair comparison." ], "reasoning": "The table shows that Iterative TaggedBT (It.-3 TaggedBT) has a BLEU score of 33.4 in the test set.", "statement": "Iterative TaggedBT achieves a BLEU score of 33.4 in the test set.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results on WMT15 EnFr, with bitext, BT, NoisedBT, and TaggedBT.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/441.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on WMT15 EnFr, with bitext, BT, NoisedBT, and TaggedBT." ], "reasoning": "BT alone decreases performance compared to the bitext baseline, but NoisedBT surpasses the bitext model. TaggedBT improves on vanilla BT by 0.4 BLEU on WMT16 EnRo.", "description": "We performed a minimal set of experiments on WMT EnFr, which are summarized in Table 5.. In this case, we see that BT alone hurts performance compared to the strong bitext baseline, but NoisedBT indeed surpasses the bitext model.. It is worth noting that our numbers are lower than those reported by Edunov et al. (2018) on the years they report (36.1, 43.8, and 40.9 on 2013, 2014, and 2015 respectively).. On WMT16 EnRo, TaggedBT improves on vanilla BT by 0.4 BLEU." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Results on WMT15 EnFr, with bitext, BT, NoisedBT, and TaggedBT.'\nProblem: 'Which model had the highest average score across all years?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/441.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on WMT15 EnFr, with bitext, BT, NoisedBT, and TaggedBT." ], "reasoning": "To find the model with the highest average score, compare the 'Avg' column for each model. TaggedBT has the highest average score of 34.1.", "question": "Which model had the highest average score across all years?", "answer": "So, the answer is {\"answer\": \"TaggedBT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nNoisedBT had the highest score in 2011.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/441.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on WMT15 EnFr, with bitext, BT, NoisedBT, and TaggedBT." ], "reasoning": "Check the scores for each model in the year 2011. NoisedBT had a score of 33.9, which is the highest among all models for that year.", "statement": "NoisedBT had the highest score in 2011.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Attention sink ratio on the first and last token and entropy (at decoder layer 5) for the models in Table 3.a, averaged over all sentences in newstest14. For ASR, data is treated as if it were BT (noised and/or tagged, resp.), whereas for entropy the natural text is used. Outliers discussed in the text are bolded.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/442.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Attention sink ratio on the first and last token and entropy (at decoder layer 5) for the models in Table 3.a, averaged over all sentences in newstest14. For ASR, data is treated as if it were BT (noised and/or tagged, resp.), whereas for entropy the natural text is used. Outliers discussed in the text are bolded." ], "reasoning": "The model shows heavy attention on the tag when present, as indicated by the low entropy values, particularly for the BT model.", "description": "To understand how the model treats the tag and what biases it learns from the data, we investigate the entropy of the attention probability distribution, as well as the attention captured by the tag.. For the tagged variants, there is heavy attention on the tag when it is present (Table 6), indicating that the model relies on the information signalled by the tag.. Table 6 reports the average length-normalized Shannon entropy:. The entropy of the attention probabilities from the model trained on BT data is the clear outlier." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Attention sink ratio on the first and last token and entropy (at decoder layer 5) for the models in Table 3.a, averaged over all sentences in newstest14. For ASR, data is treated as if it were BT (noised and/or tagged, resp.), whereas for entropy the natural text is used. Outliers discussed in the text are bolded.'\nProblem: 'Which model has the lowest average length-normalized Shannon entropy?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/442.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Attention sink ratio on the first and last token and entropy (at decoder layer 5) for the models in Table 3.a, averaged over all sentences in newstest14. For ASR, data is treated as if it were BT (noised and/or tagged, resp.), whereas for entropy the natural text is used. Outliers discussed in the text are bolded." ], "reasoning": "The table shows the average length-normalized Shannon entropy (~H) for different models. The BT model has the lowest entropy value of 0.455.", "question": "Which model has the lowest average length-normalized Shannon entropy?", "answer": { "answer": "BT" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe TaggedNoisedBT model has the highest ASR0 value.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/442.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Attention sink ratio on the first and last token and entropy (at decoder layer 5) for the models in Table 3.a, averaged over all sentences in newstest14. For ASR, data is treated as if it were BT (noised and/or tagged, resp.), whereas for entropy the natural text is used. Outliers discussed in the text are bolded." ], "reasoning": "The table lists the ASR0 values for different models. The TaggedNoisedBT model has the highest ASR0 value of 7.33.", "statement": "The TaggedNoisedBT model has the highest ASR0 value.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Comparing standard decoding with decoding as if the input were back-translated data, meaning that it is tagged (for the TaggedBT model) or noised (for the NoisedBT model) .'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/443.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Comparing standard decoding with decoding as if the input were back-translated data, meaning that it is tagged (for the TaggedBT model) or noised (for the NoisedBT model) ." ], "reasoning": "The noised decode method yields poor performance, while the tagged decode method performs slightly worse than the standard decode method.", "description": "The BLEU scores of each decoding method are presented in Table 7.. The noised decode – decoding newstest sentences with the NoisedBT model after noising the source – yields poor performance.. The tagged decode, however, yields only somewhat lower performance than the standard decode on the same model (-2.9BLEU on average)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Comparing standard decoding with decoding as if the input were back-translated data, meaning that it is tagged (for the TaggedBT model) or noised (for the NoisedBT model) .'\nProblem: 'What is the average BLEU score for the NoisedBT model using the standard decode method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/443.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Comparing standard decoding with decoding as if the input were back-translated data, meaning that it is tagged (for the TaggedBT model) or noised (for the NoisedBT model) ." ], "reasoning": "To find the average BLEU score for the NoisedBT model using the standard decode method, look at the 'AVG 13-17' column for the NoisedBT model with the 'standard' decode type.", "question": "What is the average BLEU score for the NoisedBT model using the standard decode method?", "answer": "So, the answer is {\"answer\": \"33.06\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe TaggedBT model using the standard decode method has a higher average BLEU score than the NoisedBT model using the standard decode method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/443.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Comparing standard decoding with decoding as if the input were back-translated data, meaning that it is tagged (for the TaggedBT model) or noised (for the NoisedBT model) ." ], "reasoning": "Compare the 'AVG 13-17' scores for the TaggedBT model using the standard decode method (33.24) and the NoisedBT model using the standard decode method (33.06). The TaggedBT model has a higher score.", "statement": "The TaggedBT model using the standard decode method has a higher average BLEU score than the NoisedBT model using the standard decode method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 9: Source-target overlap for both back-translated data with decoding newstest as if it were bitext or BT data. Model decodes are averaged over newstest2010-newstest2017.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/444.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Source-target overlap for both back-translated data with decoding newstest as if it were bitext or BT data. Model decodes are averaged over newstest2010-newstest2017." ], "reasoning": "The table compares the unigram overlap percentages between source and target tokens for different types of data, including bitext and back-translated data.", "description": "We quantify the copy rate with the unigram overlap between source and target as a percentage of tokens in the target side, and compare those statistics to the bitext and the back-translated data (Table 9)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 9: Source-target overlap for both back-translated data with decoding newstest as if it were bitext or BT data. Model decodes are averaged over newstest2010-newstest2017.'\nProblem: 'Which data type has the highest src-tgt unigram overlap percentage?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/444.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Source-target overlap for both back-translated data with decoding newstest as if it were bitext or BT data. Model decodes are averaged over newstest2010-newstest2017." ], "reasoning": "To find the data type with the highest src-tgt unigram overlap percentage, compare the percentages listed in the table for each data type.", "question": "Which data type has the highest src-tgt unigram overlap percentage?", "answer": "So, the answer is {\"answer\": \"BT Data\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTaggedBT (standard decode) has a higher src-tgt unigram overlap percentage than Bitext.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/444.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Source-target overlap for both back-translated data with decoding newstest as if it were bitext or BT data. Model decodes are averaged over newstest2010-newstest2017." ], "reasoning": "The table shows that TaggedBT (standard decode) has a src-tgt unigram overlap of 8.9%, while Bitext has an overlap of 5.9%. Therefore, the statement is supported.", "statement": "TaggedBT (standard decode) has a higher src-tgt unigram overlap percentage than Bitext.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Document distribution over the classes of the Reuters-8'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/445.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Document distribution over the classes of the Reuters-8" ], "reasoning": "The Reuters-8 dataset, derived from Reuters-21578, contains eight classes with sample sizes ranging from 51 to over 3000 documents, without stop words, stemming, or typo correction.", "description": "We used the Reuters-8 dataset without stop words from aiming at single-label classification, which is a preprocessed format of the Reuters-215782. Words in the texts were considered as they appeared, without performing stemming or typo correction. This database has eight different classes with the number of samples varying from 51 to over 3000 documents, as can be seen in Table I." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Document distribution over the classes of the Reuters-8'\nProblem: 'Which class has the highest number of samples in the Reuters-8 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/445.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Document distribution over the classes of the Reuters-8" ], "reasoning": "To find the class with the highest number of samples, compare the 'Number of samples' for each class. The 'earn' class has the highest number of samples with 3923.", "question": "Which class has the highest number of samples in the Reuters-8 dataset?", "answer": "So, the answer is {\"answer\": \"earn\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'grain' class has the lowest number of samples in the Reuters-8 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/445.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Document distribution over the classes of the Reuters-8" ], "reasoning": "To verify this statement, compare the 'Number of samples' for each class. The 'grain' class has the lowest number of samples with 51.", "statement": "The 'grain' class has the lowest number of samples in the Reuters-8 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Results for the text classification experiment on Reuters-8 database'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/446.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Results for the text classification experiment on Reuters-8 database" ], "reasoning": "The best accuracy was achieved by TF-MSM with w2v features at 92.01%, followed closely by MNB with tfBOW features at 91.47%.", "description": "In this experiment, we performed text classification among the classes in the Reuters-8 database.. The results can be seen in Table II. The simplest baseline, SA with w2v, achieved an accuracy rate of 78.73%. This. LSA with BOW features was almost 10% more accurate than SA, where the best results with binary weights were achieved with an approximation with 130 dimensions, with TF weights were achieved with 50 dimensions, and with TFIDF weights were achieved with 30 dimensions. SVM with BOW features was about 3% more accurate than LSA, with binary weights leading to a higher accuracy rate.. Among the baselines, the best method was MNB with tfBOW features, with an accuracy of 91.47%,. MSM with w2v had an accuracy rate of 90.62%, with the best results achieved with word subspace dimensions for the training classes ranging from 150 to 181, and for the query ranging from 3 to 217. Incorporating the frequency information in the subspace modeling resulted in higher accuracy, with TFMSM achieving 92.01%, with dimensions of word subspaces" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Results for the text classification experiment on Reuters-8 database'\nProblem: 'Which method achieved the highest accuracy rate and what was the feature used?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/446.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Results for the text classification experiment on Reuters-8 database" ], "reasoning": "The table shows that TF-MSM with w2v features achieved the highest accuracy rate of 92.01%.", "question": "Which method achieved the highest accuracy rate?", "answer": { "answer": "TF-MSM" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSVM with tfBOW features achieved an accuracy rate of 89.10%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/446.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Results for the text classification experiment on Reuters-8 database" ], "reasoning": "The table shows that SVM with tfBOW features indeed achieved an accuracy rate of 89.10%.", "statement": "SVM with tfBOW features achieved an accuracy rate of 89.10%.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results of our Transformer-Language Model approach against similar approaches (Bryant and Briscoe, 2018) and state-of-the-art on Grammatical Error Correction. For each of the datasets, we use the corresponding test set, and we do not train our models on the corpora. As BERT, we report the best performing BERT model (12 layers, retaining uppercase characters). In the top part of each dataset, we report the scores of supervised methods and in the bottom the unsupervised ones. † denotes this system won the shared task competition.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/447.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of our Transformer-Language Model approach against similar approaches (Bryant and Briscoe, 2018) and state-of-the-art on Grammatical Error Correction. For each of the datasets, we use the corresponding test set, and we do not train our models on the corpora. As BERT, we report the best performing BERT model (12 layers, retaining uppercase characters). In the top part of each dataset, we report the scores of supervised methods and in the bottom the unsupervised ones. † denotes this system won the shared task competition." ], "reasoning": "Transformer Language Models outperform the simple n-gram model and approach the performance of supervised GEC systems, with GPT-2 leading in the CoNLL14 dataset.", "description": "Table 2 presents the results of our method comparing them against recent state-of-the-art supervised models and the simple n-gram language model used by Bryant and Briscoe (2018).. A key result of Table 2 is that Transformer Language Models prove to be more than just a competitive baseline to legitimate Grammatical Error Correction systems on their own. Across the board, Transformer Models are able to outperform the simple n-gram model and even approach the performance of supervised GEC systems.. we see that their performance is nearly identical with GPT-2 leading by a small margin in the CoNLL14 dataset.. BERT surpasses the n-gram baseline overall, it achieves worse performance than the rest in terms of precision and F0.5 score." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results of our Transformer-Language Model approach against similar approaches (Bryant and Briscoe, 2018) and state-of-the-art on Grammatical Error Correction. For each of the datasets, we use the corresponding test set, and we do not train our models on the corpora. As BERT, we report the best performing BERT model (12 layers, retaining uppercase characters). In the top part of each dataset, we report the scores of supervised methods and in the bottom the unsupervised ones. † denotes this system won the shared task competition.'\nProblem: 'Which system has the highest ERRANT F0.5 score in the CoNLL-2014 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/447.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of our Transformer-Language Model approach against similar approaches (Bryant and Briscoe, 2018) and state-of-the-art on Grammatical Error Correction. For each of the datasets, we use the corresponding test set, and we do not train our models on the corpora. As BERT, we report the best performing BERT model (12 layers, retaining uppercase characters). In the top part of each dataset, we report the scores of supervised methods and in the bottom the unsupervised ones. † denotes this system won the shared task competition." ], "reasoning": "To find the system with the highest ERRANT F0.5 score in the CoNLL-2014 dataset, we look at the ERRANT F0.5 column for the CoNLL-2014 dataset. The highest score is 45.58, achieved by GPT-2.", "question": "Which system has the highest ERRANT F0.5 score in the CoNLL-2014 dataset?", "answer": "So, the answer is {\"answer\": \"GPT-2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGPT-2 has the highest M2 F0.5 score in the CoNLL-2014 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/447.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of our Transformer-Language Model approach against similar approaches (Bryant and Briscoe, 2018) and state-of-the-art on Grammatical Error Correction. For each of the datasets, we use the corresponding test set, and we do not train our models on the corpora. As BERT, we report the best performing BERT model (12 layers, retaining uppercase characters). In the top part of each dataset, we report the scores of supervised methods and in the bottom the unsupervised ones. † denotes this system won the shared task competition." ], "reasoning": "To verify this statement, we look at the M2 F0.5 column for the CoNLL-2014 dataset. The highest score is 61.34, achieved by Ge et al. (2018), not GPT-2.", "statement": "GPT-2 has the highest M2 F0.5 score in the CoNLL-2014 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table III: Reply classification labels and distribution per source'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/448.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table III: Reply classification labels and distribution per source" ], "reasoning": "The distribution of reply types (buy, sell, and other) varies significantly between Antichat and Hackforums, with 'other' being the most common reply type in both forums.", "description": "We label each reply into three categories: buy, sell and other. The distribution of reply types is highly dependant on the structure and rules of the forum as shown in Table III." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table III: Reply classification labels and distribution per source'\nProblem: 'What is the percentage difference between 'buy' replies in Antichat and Hackforums?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/448.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table III: Reply classification labels and distribution per source" ], "reasoning": "The table shows that 'buy' replies are 8% in Antichat and 12% in Hackforums. The percentage difference is calculated as 12% - 8% = 4%.", "question": "What is the percentage difference between 'buy' replies in Antichat and Hackforums?", "answer": "So, the answer is {\"answer\": \"4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'sell' reply type is more common in Hackforums than in Antichat.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/448.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table III: Reply classification labels and distribution per source" ], "reasoning": "The table shows that 'sell' replies are 8% in Antichat and 2% in Hackforums. Therefore, 'sell' replies are more common in Antichat than in Hackforums.", "statement": "The 'sell' reply type is more common in Hackforums than in Antichat.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table IV: Weighted precision, recall and F1 scores of classifiers across datasets and tasks, with stratified k-fold cross-validation'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/449.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table IV: Weighted precision, recall and F1 scores of classifiers across datasets and tasks, with stratified k-fold cross-validation" ], "reasoning": "Logistic Regression achieved the highest precision in reply classification for both Antichat and Hack Forums datasets.", "description": "Similarly a single classifier outperforms the rest in reply classification, as seen in Table IV. By our weighted nonother precision metric, Logistic Regression performed the best across both datasets, providing 0.874 precision on Antichat and 0.852 precision on Hack Forums." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table IV: Weighted precision, recall and F1 scores of classifiers across datasets and tasks, with stratified k-fold cross-validation'\nProblem: 'Which model achieved the highest precision for Antichat Reply classification?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/449.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table IV: Weighted precision, recall and F1 scores of classifiers across datasets and tasks, with stratified k-fold cross-validation" ], "reasoning": "By examining the precision values for Antichat Reply classification, Logistic Regression has the highest precision of 0.874.", "question": "Which model achieved the highest precision for Antichat Reply classification?", "answer": "So, the answer is {\"answer\": \"Logistic Regression\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nLogistic Regression achieved the highest precision for Hack Forums Product classification.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/449.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table IV: Weighted precision, recall and F1 scores of classifiers across datasets and tasks, with stratified k-fold cross-validation" ], "reasoning": "By examining the precision values for Hack Forums Product classification, XGBoost has the highest precision of 0.734, not Logistic Regression.", "statement": "Logistic Regression achieved the highest precision for Hack Forums Product classification.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison of pooling methods'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/450.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of pooling methods" ], "reasoning": "Mean-pooling of hidden states outperforms other pooling methods across all task categories.", "description": "are summarized in Table 1, where the score for each task category is calculated by averaging the normalized values for the tasks within each category. Although the activations of [CLS] token hidden states are often used in fine-tuning BERT for classification tasks, Mean-pooling of hidden states performs the best in all task categories among all the pooling methods." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison of pooling methods'\nProblem: 'Which pooling method has the highest average score across all task categories?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/450.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of pooling methods" ], "reasoning": "The table shows the average scores for each pooling method across all task categories. Mean-pooling has the highest average score of 88.0.", "question": "Which pooling method has the highest average score across all task categories?", "answer": "So, the answer is {\"answer\": \"Mean\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe [CLS] token hidden states perform the best in Text Classification.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/450.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of pooling methods" ], "reasoning": "The table shows that for Text Classification, the [CLS] token hidden states have a score of 90.7, while Mean-pooling has a higher score of 95.4.", "statement": "The [CLS] token hidden states perform the best in Text Classification.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results of BERT passage-level embeddings on question-answering datasets'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/451.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of BERT passage-level embeddings on question-answering datasets" ], "reasoning": "In-domain fine-tuned BERT delivers the best performance across various datasets, achieving new state-of-the-art results on WikiPassageQA and InsuranceQA.", "description": "The comparison between BERT embeddings and other models is presented in Table 3. Overall, in-domain fine-tuned BERT delivers the best performance. We report new state-of-the-art results on WikiPassageQA (33% improvement in MAP) and InsuranceQA (version 1.0) (3.6% improvement in P@1) by supervised fine-tuning BERT using pairwise rank hinge loss. When evaluated on non-factoid QA datasets, there is a big gap between BERT embeddings and the fully fine-tuned BERT, which suggests that deep interactions between questions and answers are critical to the task. However, the gap is much smaller for factoid QA datasets. Since non-factoid QA depends more on content matching rather than vocabulary matching, the results are kind of expected. Similar to BERT for sentence embeddings, mean-pooling and combining the top and bottom layer embeddings lead to better performance, and (u, v, u ∗ v, |u − v|) shows the strongest results among other interaction schemes. Different from sentence-level embeddings, fine-tuning BERT on SNLI doesn't lead to significant improvement, which suggests possible domain mismatch between SNLI and the QA datasets. MLP layer usually provided a 1-2 percent boost in performance compared to the logistic regression layer. For WikiPassageQA, BERT embeddings perform comparably as BM25 baseline. For InsuranceQA, BERT embeddings outperform a strong representation-based matching model DSSM (Huang et al., 2013), but still far behind the state-of-the-art interaction-based model SUBMULT+NN (Wang and Jiang, 2016) and fully fine-tuned BERT. On factoid datasets (Quasar-t and SearchQA), BERT embeddings outperform BM25 baseline significantly." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Results of BERT passage-level embeddings on question-answering datasets'\nProblem: 'Which model achieved the highest MAP score on WikiPassageQA?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/451.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of BERT passage-level embeddings on question-answering datasets" ], "reasoning": "The table shows the MAP scores for different models on WikiPassageQA. The highest MAP score is 74.9, achieved by the In-domain Fine-tuned BERT.", "question": "Which model achieved the highest MAP score on WikiPassageQA?", "answer": { "answer": "In-domain Fine-tuned BERT" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Pre-trained BERT Embedding model outperforms the BM25 baseline on InsuranceQA.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/451.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of BERT passage-level embeddings on question-answering datasets" ], "reasoning": "The table shows that the Pre-trained BERT Embedding model has a P@1 score of 44.9 on InsuranceQA, which is lower than the BM25 baseline score of 60.2.", "statement": "The Pre-trained BERT Embedding model outperforms the BM25 baseline on InsuranceQA.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: comparing results of our trained model with others'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/452.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: comparing results of our trained model with others" ], "reasoning": "Our model, Beheshti-NER, outperforms both Bokaei and Mahmoudi (2018) and Shahshahani et al. (2018) on the Arman and Peyma datasets in both word and phrase levels.", "description": "these models are evaluated on two common datasets for NER: PEYMA and ARMAN. Bokaei and Mahmoudi (Bokaei and Mahmoudi, 2018) and Shahshahani et al. (Shahshahani et al., 2018) had reported the best results which you can see in Table 3. As you see in Table 3 in both word and phrase levels, our model outperform other NER approaches for the Persian language.. dataset.Table 3 shows that our results are 10 percent better than Shahshahani and colleagues on the same platform. On the other hand Bokaei and Mahmoudi (Bokaei and Mahmoudi, 2018) reported their results on Arman dataset Which is lower than ours in both word and phrase levels according to Table 3." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: comparing results of our trained model with others'\nProblem: 'How much better is Beheshti-NER compared to Shahshahani et al. (2018) on the Peyma word level?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/452.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: comparing results of our trained model with others" ], "reasoning": "Beheshti-NER scored 90.59 on the Peyma word level, while Shahshahani et al. (2018) scored 80.0. The difference is 90.59 - 80.0 = 10.59.", "question": "How much better is Beheshti-NER compared to Shahshahani et al. (2018) on the Peyma word level?", "answer": { "answer": "10.59" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBeheshti-NER outperforms Bokaei and Mahmoudi (2018) on the Arman phrase level.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/452.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: comparing results of our trained model with others" ], "reasoning": "Beheshti-NER scored 79.93 on the Arman phrase level, while Bokaei and Mahmoudi (2018) scored 76.79. Therefore, Beheshti-NER outperforms Bokaei and Mahmoudi (2018) on the Arman phrase level.", "statement": "Beheshti-NER outperforms Bokaei and Mahmoudi (2018) on the Arman phrase level.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Phrase-level evaluation for subtask A: 3-classes'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/453.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Phrase-level evaluation for subtask A: 3-classes" ], "reasoning": "Beheshti-NER-1 achieved F1 scores of 84.0% and 87.9% for phrase and word level evaluations in subtask A.", "description": "Tables 4, 5, 6, 7 and 8 show the results of evaluation reported by competition for all teams which participated in the challenge. Our method is mentioned as Beheshti-NER-1. Table 4 and 5 show the results for subtask A. according to the tables, we reached to 84.0% and 87.9% F1 score respectively for phrase and word level evaluations." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Phrase-level evaluation for subtask A: 3-classes'\nProblem: 'What is the total F1 score for Beheshti-NER-1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/453.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Phrase-level evaluation for subtask A: 3-classes" ], "reasoning": "To find the total F1 score for Beheshti-NER-1, look at the column labeled 'Test Data 1 Total' and find the F1 score for Beheshti-NER-1.", "question": "What is the total F1 score for Beheshti-NER-1?", "answer": { "answer": "84" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBeheshti-NER-1 achieved the highest F1 score in Test Data 1 In Domain.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/453.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Phrase-level evaluation for subtask A: 3-classes" ], "reasoning": "To verify this statement, compare the F1 scores in the 'Test Data 1 In Domain' column for all teams. Beheshti-NER-1 has an F1 score of 84.8, which is not the highest as MorphoBERT has an F1 score of 87.1.", "statement": "Beheshti-NER-1 achieved the highest F1 score in Test Data 1 In Domain.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Ranked List of Target Words based on their Ambiguity Scores'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/454.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Ranked List of Target Words based on their Ambiguity Scores" ], "reasoning": "The table shows the top-5 words with the largest absolute differences between the assigned ranks for each scenario, highlighting that most disagreements have higher ranks and are proper names.", "description": "In order to study the cases of disagreement between the approaches proposed by this paper and Ferrari et al. (2018), the top-5 words with the largest absolute differences between the assigned ranks have been reported for each scenario by Table III. The number of target words for each project scenario have also been mentioned in parenthesis. It can be observed that most of the cases of disagreement have a higher rank, i.e. relatively lower ambiguity score assigned by the linear transformation approach proposed by this paper. Most of such cases are proper names such as robert, peter, and daniel." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Ranked List of Target Words based on their Ambiguity Scores'\nProblem: 'Which term has the highest Medical Robot Score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/454.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Ranked List of Target Words based on their Ambiguity Scores" ], "reasoning": "To find the term with the highest Medical Robot Score, look at the Medical Robot Score column and identify the highest value, which is 0.919 for the term 'stroke'.", "question": "Which Medical robot term has the highest Medical Robot Score?", "answer": "So, the answer is {\"answer\": \"stroke\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe term 'kingdom' has the highest Sports Rehab Machine Score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/454.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Ranked List of Target Words based on their Ambiguity Scores" ], "reasoning": "To verify this statement, check the Sports Rehab Machine Score column for the highest value. The highest score is 0.907 for the term 'kingdom', which supports the statement.", "statement": "The Sports rehab machine term 'kingdom' has the highest Sports Rehab Machine Score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: In-domain experiments, best values per column are highlighted. For each dataset (column head) we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/455.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: In-domain experiments, best values per column are highlighted. For each dataset (column head) we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column)." ], "reasoning": "The average performances of LR−syntax and CNN:rand are virtually identical, with a slight advantage for the feature-based approach, but their difference is not statistically significant. Both systems exhibit significantly better average performances than all other models surveyed.", "description": "The average performances of LR−syntax and CNN:rand are virtually identical, both for Macro6Described as FscoreM in Sokolova and Lapalme (2009). F1 and Claim-F1, with a slight advantage for the feature-based approach, but their difference is not statistically significant (p ≤ 0.05). Altogether, these two systems exhibit significantly better average performances than all other models surveyed here, both those relying on and those not relying on hand-crafted features (p ≤ 0.05).. The performance of the learners is quite divergent across datasets, with Macro-F1 scores6 ranging from 60% (WTP) to 80% (MT), average 67% (see Table 2).. On all datasets, our best systems clearly outperform both baselines. In isolation, lexical, embedding, and syntax features are most helpful, whereas structural features did not help in most cases. Discourse features only contribute significantly on MT. When looking at the performance of the feature-based approaches, the most striking finding is the importance of lexical (in our setup, unigram) information." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: In-domain experiments, best values per column are highlighted. For each dataset (column head) we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column).'\nProblem: 'Which model has the highest average neural network model performance?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/455.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: In-domain experiments, best values per column are highlighted. For each dataset (column head) we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column)." ], "reasoning": "To find the model with the highest average neural network model performance, we need to look at the 'Average neural network models' column and identify the highest value. The highest value in this column is 67.7, which corresponds to the CNN:rand model.", "question": "Which model has the highest average neural network model performance?", "answer": "So, the answer is {\"answer\": \"CNN:rand\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LR model with all features has the highest average performance among all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/455.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: In-domain experiments, best values per column are highlighted. For each dataset (column head) we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column)." ], "reasoning": "To verify this statement, we need to compare the average performance of the LR model with all features to the average performance of all other models. The LR model with all features has an average performance of 66.7, while the CNN:rand model has a higher average performance of 67.7. Therefore, the statement is refuted.", "statement": "The LR model with all features has the highest average performance among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Cross-domain experiments, best values per column are highlighted, in-domain results (for comparison) in italics; results only for selected systems. For each source/target combination we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/456.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Cross-domain experiments, best values per column are highlighted, in-domain results (for comparison) in italics; results only for selected systems. For each source/target combination we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column)." ], "reasoning": "The best feature-based approach outperforms the best deep learning approach in most scenarios, particularly in the Claim-F1 measure.", "description": "Table 3 lists the results of the best feature-based (LR All features) and deep learning (CNN:rand) systems, as well as single feature groups (averages over all source domains,. For the lowest scoring datasets, OC and WTP, the differences are only marginal when trained on a suitable dataset. The best feature-based approach outperforms the best deep learning approach in most scenarios.. In particular, as opposed to the in-domain experiments, the difference of the Claim-F1 measure between the feature-based approaches and the deep learning approaches is striking. In the feature-based approaches, on average, a combination of all features yields the best results for both Macro-F1 and Claim-F1. When comparing single features, lexical ones do the best job." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Cross-domain experiments, best values per column are highlighted, in-domain results (for comparison) in italics; results only for selected systems. For each source/target combination we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column).'\nProblem: 'Which system has the highest average score for the PE dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/456.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Cross-domain experiments, best values per column are highlighted, in-domain results (for comparison) in italics; results only for selected systems. For each source/target combination we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column)." ], "reasoning": "To find the system with the highest average score for the PE dataset, compare the average scores of CNN:rand and LR All features for PE. CNN:rand has an average score of 53.5, while LR All features has an average score of 55.7.", "question": "Which system has the highest average score for the PE dataset?", "answer": { "answer": "LR" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LR All features system has a higher average score than the CNN:rand system for the OC dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/456.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Cross-domain experiments, best values per column are highlighted, in-domain results (for comparison) in italics; results only for selected systems. For each source/target combination we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column)." ], "reasoning": "The average score for the OC dataset using CNN:rand is 53.8, while the average score using LR All features is 55.5. Therefore, the statement is supported.", "statement": "The LR All features system has a higher average score than the CNN:rand system for the OC dataset.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Leave-one-domain-out experiments, best values per column are highlighted. For each test dataset (column head) we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/457.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Leave-one-domain-out experiments, best values per column are highlighted. For each test dataset (column head) we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column)." ], "reasoning": "The neural network systems showed improved performance with mixed sources, achieving the highest results when trained with all features.", "description": "We also performed experiments with mixed sources, the results are shown in Table 4.. In this scenario, the neural network systems seem to benefit from the increased amount of training data and thus gave the best results." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Leave-one-domain-out experiments, best values per column are highlighted. For each test dataset (column head) we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column).'\nProblem: 'What is the average performance of the neural network system when using all features?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/457.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Leave-one-domain-out experiments, best values per column are highlighted. For each test dataset (column head) we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column)." ], "reasoning": "To find the average performance of the neural network system when using all features, look at the 'Avg' column in the 'All features' row.", "question": "What is the average performance of the neural network system when using all features?", "answer": "So, the answer is {\"answer\": \"31.6\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe neural network system performed better with random baseline than with majority baseline.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/457.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Leave-one-domain-out experiments, best values per column are highlighted. For each test dataset (column head) we show two scores: Macro-F1 score (left-hand column) and F1 score for claims (right-hand column)." ], "reasoning": "To verify this statement, compare the 'Avg' values of the 'Random bsl' and 'Majority bsl' rows. The 'Random bsl' has an average of 23.6, while the 'Majority bsl' has an average of 30.0.", "statement": "The neural network system performed better with random baseline than with majority baseline.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: The accuracy(%) of the ML models for NLU.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/458.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The accuracy(%) of the ML models for NLU." ], "reasoning": "The table shows the performance of different models on intent detection and slot filling tasks, with SVM and GloVe-based models achieving the highest scores in intent detection and slot filling respectively.", "description": "First, we represent utterances by their TF-IDF representations as feature-vectors. We then supply the vector representation of each utterance to a Support Vector Machine (SVM) with a linear kernel for domain and intent identification, and to a Hidden Markov Model (HMM) for slot filling. Second, we encode words in a dialogue utterance by GLoVe, as benchmark pre-trained word embeddings, to include the semantic relationships among words. We compute the average of word embeddings in an utterance to represent the utterance by a vector. Table 2 shows the performance of the described models." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: The accuracy(%) of the ML models for NLU.'\nProblem: 'Which model has the highest performance in slot filling?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/458.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The accuracy(%) of the ML models for NLU." ], "reasoning": "The table lists the performance of various models on slot filling. The GloVe-based model has the highest slot filling score of 98.45.", "question": "Which model has the highest performance in slot filling?", "answer": "So, the answer is {\"answer\": \"GloVe-based\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe SVM model has the highest performance in intent detection.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/458.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The accuracy(%) of the ML models for NLU." ], "reasoning": "The table shows the performance of different models on intent detection. The SVM model has the highest score of 94.98 in intent detection.", "statement": "The SVM model has the highest performance in intent detection.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: The output of the general survey.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/459.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The output of the general survey." ], "reasoning": "The survey results indicate that 83% of participants agree that the agent reduces their need to search the web for research information, but 66% of human judges disagreed on using it for planning their conference schedule.", "description": "The results of the survey shows a general sat, confirmisfaction feeling of interactions with ing our motivation that the existence of such an agent helps researchers (See Table 4). 83% of participants agree that reduces their needs to search through the web (e.g. using search engines) to obtain information related to their research; and in the future. However, 66% of hu66% use man judges disagreed on using for planning their schedule for a conference. This observation could be because the current version of mainly retrieves information for users but planning for a conference needs some inferences on such information as well." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: The output of the general survey.'\nProblem: 'What percentage of participants agree that the agent reduces their need to google specific information?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/459.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The output of the general survey." ], "reasoning": "According to the table, 66.7% of participants agree that the agent reduces their need to google specific information.", "question": "What percentage of participants agree that the agent reduces their need to google specific information?", "answer": { "answer": "66.7" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMost participants agree that the agent helps them save time in their work.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/459.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The output of the general survey." ], "reasoning": "The table shows that 33.3% of participants strongly agree and 33.3% agree that the agent helps them save time in their work, totaling 66.6%, which is a majority.", "statement": "Most participants agree that the agent helps them save time in their work.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Accuracies of the different models on the cQA datasets and MAP/MRR on WikiPassageQA. Σ denotes the average accuracy over all cQA datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/460.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracies of the different models on the cQA datasets and MAP/MRR on WikiPassageQA. Σ denotes the average accuracy over all cQA datasets." ], "reasoning": "COALA outperforms other methods on all seven datasets, with COALA p-means and COALA syntax-aware providing additional improvements.", "description": "We compare all approaches across the InsuranceQA and WikiPassageQA benchmarks as well as the five StackExchange datasets in Table 2. For the cQA answer selection datasets we measure the accuracy, which is the ratio of correctly selected answers, and for the passage retrieval in WikiPassageQA we report MAP/MRR. The results show that COALA substantially outperforms all other relevance matching and semantic similarity approaches on all seven datasets. For instance, on the cQA datasets COALA improves by 4.5pp over CA-Wang and by 8.8pp over the best semantic similarity method on average.. Our extended approach COALA p-means improves the performance of COALA on these datasets by an additional 1.6pp. The proposed power mean aggregation achieves a strong improvement on four datasets and results in a small performance decrease in the remaining three cases.. The results in Table 2 show that our proposed syntax-aware extension COALA syntax-aware, which incorporates syntactic roles of word sequences to learn syntax-aware aspect representations, improves the results in five out of seven cases.. It thereby achieves an an average improvement of 0.7pp over COALA in our cQA datasets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Accuracies of the different models on the cQA datasets and MAP/MRR on WikiPassageQA. Σ denotes the average accuracy over all cQA datasets.'\nProblem: 'What is the average improvement of COALA p-means over CA-Wang on the cQA datasets?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/460.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracies of the different models on the cQA datasets and MAP/MRR on WikiPassageQA. Σ denotes the average accuracy over all cQA datasets." ], "reasoning": "COALA p-means improves the performance by 6.9. Therefore, the average improvement of COALA p-means over CA-Wang is 4.5pp + 1.6pp = 6.1pp.", "question": "What is the improvement of COALA p-means over CA-Wang on the Insurance datasets?", "answer": { "answer": "6.9" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCOALA syntax-aware achieves an average improvement of 0.7pp over COALA in the cQA datasets.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/460.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracies of the different models on the cQA datasets and MAP/MRR on WikiPassageQA. Σ denotes the average accuracy over all cQA datasets." ], "reasoning": "The description states that COALA syntax-aware improves the results in five out of seven cases and achieves an average improvement of 0.7pp over COALA in the cQA datasets.", "statement": "COALA syntax-aware achieves an average improvement of 0.7pp over COALA in the cQA datasets.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Thematic ranker evaluation, incl. random ranker (RND) and upper bound (UB); bold - best result over syntactic rankers, underlined - best result over thematic rankers'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/461.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Thematic ranker evaluation, incl. random ranker (RND) and upper bound (UB); bold - best result over syntactic rankers, underlined - best result over thematic rankers" ], "reasoning": "Syntactic rankers positioning the object second in the hierarchy (SE2 and SD2) perform better on both datasets, with SE2 achieving the highest alignment for English and SD2 for German.", "description": "we first compare the performance of thematic rankers with respect to syntactic rankers and available datasets. The results of this comparison are summarized in Table 3 and show that syntactic rankers positioning the object second in the hierarchy (SE2 and SD2) lead to better alignment on both datasets and have a higher upper bound.. For English the global hierarchy-based ranker approaches the upper bound, closely followed by the pairwise ranker. The accuracy on German data is lower and the pairwise and local rankers outperform the global hierarchy-based ranker." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Thematic ranker evaluation, incl. random ranker (RND) and upper bound (UB); bold - best result over syntactic rankers, underlined - best result over thematic rankers'\nProblem: 'Which syntactic ranker achieves the highest accuracy for English?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/461.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Thematic ranker evaluation, incl. random ranker (RND) and upper bound (UB); bold - best result over syntactic rankers, underlined - best result over thematic rankers" ], "reasoning": "By comparing the values in the 'glob' column for English, SE2 has the highest accuracy with a value of 0.930.", "question": "Which syntactic ranker achieves the highest accuracy for English?", "answer": "So, the answer is {\"answer\": \"SE2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe pairwise ranker outperforms the global hierarchy-based ranker for German data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/461.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Thematic ranker evaluation, incl. random ranker (RND) and upper bound (UB); bold - best result over syntactic rankers, underlined - best result over thematic rankers" ], "reasoning": "The table shows that for German data (DE), the pairwise ranker (pair) has a value of 0.820, which is higher than the global hierarchy-based ranker (glob) with a value of 0.790.", "statement": "The pairwise ranker outperforms the global hierarchy-based ranker for German data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Induced hierarchies'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/462.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Induced hierarchies" ], "reasoning": "The table shows rankings for English and German data, highlighting differences in role definitions and syntactic formalisms, with many roles tied due to non-co-occurrence or same syntactic rank.", "description": "Table 4 shows full rankings extracted for English and German data. While some correspondence to the hierarchies proposed for English Agent in literature is evident (e.g. ≺ Instrument ≺ Theme, similar to (Fillmore, 1968)), a direct comparison is impossible due to the differences in role definitions and underlying syntactic formalisms. Notice the high number of ties: some roles never co-occur (either by chance or by design) or occur on the same syntactic rank (e.g. oblique) so there is no evidence for preference even if we enforce transitivity." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Induced hierarchies'\nProblem: 'What is the rank of 'Theme' in both English and German data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/462.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Induced hierarchies" ], "reasoning": "In the table, 'Theme' is ranked after 'Pivot' and before 'Patient' in English, and after 'Cause' and before 'Patient' in German.", "question": "What is the rank of 'Theme' in both English and German data?", "answer": "So, the answer is {\"answer\": \"5\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIn both English and German data, 'Agent' is ranked higher than 'Theme'.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/462.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Induced hierarchies" ], "reasoning": "The table shows that 'Agent' is ranked first in both English and German data, while 'Theme' is ranked 5th in English and 6th in German.", "statement": "In both English and German data, 'Agent' is ranked higher than 'Theme'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Cross-lingual evaluation, global ranker'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/463.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Cross-lingual evaluation, global ranker" ], "reasoning": "The table shows that while cross-lingual performance is lower than monolingual performance, it still significantly outperforms the random baseline.", "description": "Table 5 contrasts the performance of THs induced from English and German training data, and evaluated on German and English test data respectively. While the cross-lingual performance is expectedly lower than the monolingual performance, it outperforms the random baseline by a large margin, suggesting the potential for crosslingual hierarchy induction." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Cross-lingual evaluation, global ranker'\nProblem: 'What is the performance difference between DE-train on EN-test and the random baseline on EN-test?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/463.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Cross-lingual evaluation, global ranker" ], "reasoning": "The performance of DE-train on EN-test is 0.852, and the random baseline on EN-test is 0.500. The difference is 0.852 - 0.500.", "question": "What is the performance difference between DE-train on EN-test and the random baseline on EN-test?", "answer": "So, the answer is {\"answer\": \"0.352\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe performance of EN-train on DE-test is higher than DE-train on DE-test.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/463.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Cross-lingual evaluation, global ranker" ], "reasoning": "The table shows that the performance of EN-train on DE-test is .787, while DE-train on DE-test is .790. Therefore, the statement is incorrect.", "statement": "The performance of EN-train on DE-test is higher than DE-train on DE-test.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Test Scores Document Ranking'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/464.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Test Scores Document Ranking" ], "reasoning": "The auto-rank + feat model has the best overall performance in document ranking, outperforming other models including auto-rank + bm25.", "description": "In Table 3 we have listed the average MAP and nDCG scores of the test sets. The tf-idf model is outperformed by most of the other models. However, bm25, which additionally takes the length of a document into account, performs very tf-idf and bm25 have the major benefit of well. fast computation. The feat model slightly outperforms the auto. auto-rank + feat model is slightly better than the auto-rank + bm25 model, both of which have the overall best performance. This shows, that the auto-encoder learns something orthogonal to term frequency and document length. The best model with respect to document ranking is the auto-rank + feat model. In Figure 3 we show the correlation between the different models. Interestingly, the bm25 and the feat strongly correlate. However, the scores of bm25 do not correlate with the scores of the combination of auto-rank and bm25. This indicates, that the model does not primarily learn to use the bm25 score but also focuses on the the auto-encoded representation. This underlines the hypothesis that the auto-encoder is able to represent latent features of the relationship of the query terms in the document. rank model. The distance features are a strong indicator for the semantic dependency between entities. These relationships need to be learned in the auto-rank model. The cosine similarity of a query and a document (auto/cos) does not yield a good result. This shows that the auto-encoder has learned many features, most of which do not correlate with our task. We also find that emb does not yield an equal performance to auto-rank. The combination of the" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Test Scores Document Ranking'\nProblem: 'Which model has the highest nDCG score for the KRAS test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/464.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Test Scores Document Ranking" ], "reasoning": "To find the highest nDCG score for the KRAS test set, look at the 'KRAS' rows and the 'nDCG' metric. The highest score is 0.914 under the 'auto-rank + feat' model.", "question": "Which model has the highest nDCG score for the KRAS test set?", "answer": { "answer": "auto-rank + feat" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe tf-idf model has the lowest MAP score for the EGFR test set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/464.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Test Scores Document Ranking" ], "reasoning": "To verify this statement, look at the 'EGFR' rows and the 'MAP' metric. The tf-idf model has a score of 0.289, which is indeed the lowest among all models listed.", "statement": "The tf-idf model has the lowest MAP score for the EGFR test set.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Individual macro-F1 scores following Schulz et al. (2019a) for each of the epistemic activities. The BiLSTM uses FastText embeddings Bojanowski et al. (2017). This architecture is equal to Flair when only using FastText embeddings. UB reports the human upper bound (inter-annotator agreement) indicating room for improvement.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/465.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Individual macro-F1 scores following Schulz et al. (2019a) for each of the epistemic activities. The BiLSTM uses FastText embeddings Bojanowski et al. (2017). This architecture is equal to Flair when only using FastText embeddings. UB reports the human upper bound (inter-annotator agreement) indicating room for improvement." ], "reasoning": "The BiLSTM model shows varying performance in predicting epistemic activities across Med and TEd data, with room for improvement compared to the human upper bound.", "description": "In Table 1, we report the performance of the BiLSTM implementation for predicting epistemic activities in the Med and TEd data. As we can see, the difficulty of predicting the classes varies between different activities. Despite some room for improvement with respect to the human upper bound (UB) based on inter-rater agreement, the interactive nature of FAMULUS helps in succeeding in this attempt by continually improving the model when new data is available. We conduct similar experiments for the prediction of fine-grained diagnostic entities, but omit a comprehensive discussion due to space limitations." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Individual macro-F1 scores following Schulz et al. (2019a) for each of the epistemic activities. The BiLSTM uses FastText embeddings Bojanowski et al. (2017). This architecture is equal to Flair when only using FastText embeddings. UB reports the human upper bound (inter-annotator agreement) indicating room for improvement.'\nProblem: 'What is the performance difference between the BiLSTM and UB models for predicting EE in the Med data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/465.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Individual macro-F1 scores following Schulz et al. (2019a) for each of the epistemic activities. The BiLSTM uses FastText embeddings Bojanowski et al. (2017). This architecture is equal to Flair when only using FastText embeddings. UB reports the human upper bound (inter-annotator agreement) indicating room for improvement." ], "reasoning": "The performance of the BiLSTM model for predicting EE in the Med data is 80.20, while the UB model's performance is 90.25. The difference is 90.25 - 80.20.", "question": "What is the performance difference between the BiLSTM and UB models for predicting EE in the Med data?", "answer": "So, the answer is {\"answer\": \"10.05\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BiLSTM model performs better than the UB model for predicting HG in the TEd data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/465.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Individual macro-F1 scores following Schulz et al. (2019a) for each of the epistemic activities. The BiLSTM uses FastText embeddings Bojanowski et al. (2017). This architecture is equal to Flair when only using FastText embeddings. UB reports the human upper bound (inter-annotator agreement) indicating room for improvement." ], "reasoning": "The table shows that the BiLSTM model's performance for predicting HG in the TEd data is 57.16, while the UB model's performance is 81.77. Therefore, the statement is refuted.", "statement": "The BiLSTM model performs better than the UB model for predicting HG in the TEd data.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: SLC experiments on different feature sets'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/466.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: SLC experiments on different feature sets" ], "reasoning": "The final model used a finetuned BERT with a 0.70 threshold for non-propaganda prediction, improving the F1 score by around 5 points.", "description": "The final model used the finetuned BERT model mentioned above with a condition to predict non-propaganda only if the prediction probability is above 0.70 for the nonpropaganda class. Otherwise the prediction of the sentence will be propaganda even if the majority of the prediction probability mass was for the non-propaganda class. This was a way to handle the unbalance in the training data without having to discard part of the data. The 0.70 threshold was chosen after elaborate experiments on both the local and the shared-task's development sets. This condition consistently provided an improvement of around 5 points in F1 score of the propaganda class on all experiments using different sets of features as shown in Table 2.. In SLC, we ran multiple experiments using BERT with and without additional features as shown in Table 2. The features include using the text passed as is to BERT without any preprocessing. Also, we experimented with adding the context which includes the two sentences that come before and after the target sentence. Context sentences were concatenated and passed as the second BERT input, while the target sentence was passed as the first BERT input. In addition, we experimented with using BERT logits (i.e., the probability predictions per class) as features in a Logistic Regression (LR) classifier concatenated with handcrafted features (e.g., LIWC, quotes, questions), and with predictions of our FLC classifier (tagged spans: whether the sentence has a propaganda fragment or not). However, none of these features added any statistically significant improvements. Therefore, we used BERT predictions for our final model with a condition to predict the majority class non-propaganda only if its prediction probability is more than 0.70 as shown in Table 3. This is a modified threshold as opposed to 0.80 in the experiments shown in Table 2 to avoid overfitting on a one dataset. The final threshold of 0.70 was chosen after experiments on both the local and shared task development sets, which also represents the ratio of the non-propaganda class in the training set." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: SLC experiments on different feature sets'\nProblem: 'What was the F1 score for the BERT model using context features on the development set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/466.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: SLC experiments on different feature sets" ], "reasoning": "The table shows the F1 score for different models and features. For the BERT model using context features, the F1 score is listed under 'Development F'.", "question": "What was the F1 score for the BERT model using context features on the development set?", "answer": { "answer": "0.65" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BERT model with text features had a higher precision than the BERT model with context features.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/466.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: SLC experiments on different feature sets" ], "reasoning": "The table shows that the BERT model with text features had a precision of 0.69, while the BERT model with context features had a precision of 0.70.", "statement": "The BERT model with text features had a higher precision than the BERT model with context features.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Precision, recall and F1 scores of the FLC task on the development and test sets of the shared task.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/467.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Precision, recall and F1 scores of the FLC task on the development and test sets of the shared task." ], "reasoning": "The best model, a BiLSTM-CRF with flair and urban glove embeddings, shows varying performance across different propaganda techniques, with some techniques having non-zero F1 scores on both development and test sets, while others have non-zero scores on only one dataset or none at all.", "description": "We also experimented with stacking BERT embeddings with all or some of the embeddings mentioned above. However, this resulted on lower 2https://www.urbandictionary.com/ 3https://data.world/jaredfern/urban-dictionary embedding. In FLC, we only show the results of our best model in Table 5 to focus more on the differences between propaganda techniques. A more elaborate study of performance of different models should follow in future work. The best model is a BiLSTM-CRF with flair and urban glove embed. As we can see in Table 5, we can divide the propaganda techniques into three groups according to the model's performance on the development and test sets. The first group includes techniques with non-zero F1 scores on both datasets: Flag-Waving, Loaded Language, Name Calling,Labeling and Slogans. This group has techniques that appear frequently in the data and/or techniques with strong lexical signals (e.g. \"American People\" in Flag-Waving) or punctuation signals (e.g. quotes in Slogans). The second group has the techniques with a nonzero F1 score on only one of the datasets but not the other, such as: Appeal to Authority, Appeal to Fear, Doubt, Reduction, and Exaggeration,Minimisation. Two out of these five techniques (Appeal to Fear and Doubt) have very small non-zero F1 on the development set which indicates that they are generally challenging on our model and were only tagged due to minor differences between the two datasets. However, the remaining three types show significant drops from development to test sets or vice-versa. This requires further analysis to understand why the model was able to do well on one dataset but get zero on the other dataset, which we leave for future work. The third group has the remaining nine techniques were our sequence tagger fails to correctly tag any text span on either dataset. This group has the most infrequent types as well as types beyond the ability for our tagger to spot by looking at the sentence only such as Repetition.. Overall, our model has the highest precision among all teams on both datasets, which could be due to adding the UBY. one-hot encoded features that highlighted some strong signals for some propaganda types. This also could be the reason for our model to have the lowest recall among the top 7 teams on both datasets as having explicit handcrafted signals suffers from the usual sparseness that accompanies these kinds of representations which could have made the model more conservative in tagging text spans." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Precision, recall and F1 scores of the FLC task on the development and test sets of the shared task.'\nProblem: 'Which propaganda technique has the highest F1 score on the development set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/467.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Precision, recall and F1 scores of the FLC task on the development and test sets of the shared task." ], "reasoning": "To find the propaganda technique with the highest F1 score on the development set, we need to look at the 'Development F' column and identify the highest value.", "question": "Which propaganda technique has the highest F1 score on the development set?", "answer": "So, the answer is {\"answer\": \"Loaded Language\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model achieved a higher F1 score for 'Appeal to Authority' on the test set compared to the development set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/467.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Precision, recall and F1 scores of the FLC task on the development and test sets of the shared task." ], "reasoning": "The table shows that the F1 score for 'Appeal to Authority' is 0 on the development set and 0.212 on the test set, indicating a higher score on the test set.", "statement": "The model achieved a higher F1 score for 'Appeal to Authority' on the test set compared to the development set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Ranked Entity Scores for KRAS Validation and PIK3CA Testing'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/468.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ranked Entity Scores for KRAS Validation and PIK3CA Testing" ], "reasoning": "The combination of all three models (auto-ref + rocchio + relevance) outperforms the other models in most cases, especially in ranking entity terms, while the rocchio + relevance model excels in recall for second and third best terms.", "description": "To evaluate the ranking of entity terms we have computed nDCG@10, nDCG@100 and MAP, see Table 4 for the results. We also compute Recall@k of relevant documents for automatically refined queries using the 1st, 2nd and 3rd ranked entities. The scores can be found in Table 5. Tables 4 and 5 show that the Relevance Model outperforms the Rocchio algorithm in every aspect. Both models outperform the auto-encoder approach (auto-ref ). We suspect that summing over the encodings distorts the individual features too much for a correct extraction of relevant entities to be possible. The combination of all three models (auto-ref + rocchio + relevance) outperforms the other models in most cases. Especially the performance for ranking of entity terms is increased using the autoencoded features. However, it is interesting to see that the rocchio + relevance model outperforms the recall for second and third best terms. This indicates that for user-evaluated term suggestions, the inclusion of the auto-encoded features is advisable. For automatic query refinement however, in average, this is not the case." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Ranked Entity Scores for KRAS Validation and PIK3CA Testing'\nProblem: 'Which model has the highest nDCG@100 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/468.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ranked Entity Scores for KRAS Validation and PIK3CA Testing" ], "reasoning": "By comparing the nDCG@100 scores in the table, the auto-ref + rocchio + relevance model has the highest score of 0.536.", "question": "Which model has the highest nDCG@100 score?", "answer": "So, the answer is {\"answer\": \"auto-ref + rocchio + relevance\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe relevance model has the highest MAP score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/468.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ranked Entity Scores for KRAS Validation and PIK3CA Testing" ], "reasoning": "The table shows that the relevance model has a MAP score of 0.223, which is lower than the MAP scores of the rocchio + relevance model (0.270) and the auto-ref + rocchio + relevance model (0.386).", "statement": "The relevance model has the highest MAP score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Features of previous evaluation applications compared to Ours (Linspector Web). #Lang: Number of supported languages, #Task-Type: Number and type of the tasks, where WST: Word similarity tasks, DT: Downstream Tasks, PT: Probing Tasks. Static: Static word embeddings and Models: Pretrained downstream models.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/469.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Features of previous evaluation applications compared to Ours (Linspector Web). #Lang: Number of supported languages, #Task-Type: Number and type of the tasks, where WST: Word similarity tasks, DT: Downstream Tasks, PT: Probing Tasks. Static: Static word embeddings and Models: Pretrained downstream models." ], "reasoning": "The table compares the features of different evaluation suites for word embeddings, highlighting the number of languages, task types, and whether they are web-based, offline, static, and include models, layers, and epochs.", "description": "A now retired evaluation suite for word embeddings was wordvectors.org (Faruqui and Dyer, 2014a).. VecEval (Nayak et al., 2016) is another web based suite for static English word embeddings that perform evaluation on a set of downstream tasks which may take several hours.. K¨ohn (2015) introduced an offline, multilingual probing suite for static embeddings limited in terms of the languages and the probing tasks.. A comparison of the system features of previous studies is given in Table 1." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Features of previous evaluation applications compared to Ours (Linspector Web). #Lang: Number of supported languages, #Task-Type: Number and type of the tasks, where WST: Word similarity tasks, DT: Downstream Tasks, PT: Probing Tasks. Static: Static word embeddings and Models: Pretrained downstream models.'\nProblem: 'Which evaluation suite supports the highest number of languages and is web-based?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/469.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Features of previous evaluation applications compared to Ours (Linspector Web). #Lang: Number of supported languages, #Task-Type: Number and type of the tasks, where WST: Word similarity tasks, DT: Downstream Tasks, PT: Probing Tasks. Static: Static word embeddings and Models: Pretrained downstream models." ], "reasoning": "By examining the table, the 'Ours' evaluation suite supports the highest number of languages (28) and is web-based.", "question": "Which evaluation suite supports the highest number of languages and is web-based?", "answer": "So, the answer is {\"answer\": \"Ours\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nNayak et al. (2016) introduced a web-based evaluation suite for static English word embeddings.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/469.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Features of previous evaluation applications compared to Ours (Linspector Web). #Lang: Number of supported languages, #Task-Type: Number and type of the tasks, where WST: Word similarity tasks, DT: Downstream Tasks, PT: Probing Tasks. Static: Static word embeddings and Models: Pretrained downstream models." ], "reasoning": "The table shows that Nayak et al. (2016) is web-based but does not support static embeddings.", "statement": "Nayak et al. (2016) introduced a web-based evaluation suite for static English word embeddings.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Refinement Terms for Query {PIK3CA}'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/470.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Refinement Terms for Query {PIK3CA}" ], "reasoning": "Refining the query {PIK3CA, V600E, BRAF, H1047R, Dabrafenib} retrieves top ranked diseases including Melanoma, which leads to the top ranked paper by Falchook et al., 2013.", "description": "In Table 6 we show the top ranked entities of type Variants, Diseases and Drugs for the query {PIK3CA}. While the diseases and the drugs are all relevant, V600E and T790M are in fact not variants of the gene PIK3CA. However, when refining the query {PIK3CA, V600E, BRAF, H1047R, Dabrafenib}, the top ranked diseases are [Melanoma, Neoplasms, Carcinoma Non Small Cell Lung (CNSCL), Thyroid Neoplasms, Colorectal Neoplasms]. Using Melanoma for refinement, retrieves the top ranked paper (Falchook et al., 2013) which perfectly includes all these entities in a biomarker relationship." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Refinement Terms for Query {PIK3CA}'\nProblem: 'Which drug is associated with the variant H1047R?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/470.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Refinement Terms for Query {PIK3CA}" ], "reasoning": "To find the drug associated with the variant H1047R, look at the row where the variant is H1047R and find the corresponding drug in the Drugs column.", "question": "Which drug is associated with the variant H1047R?", "answer": "So, the answer is {\"answer\": \"Lapatinib\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe variant E545K is associated with the disease Glioblastoma.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/470.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Refinement Terms for Query {PIK3CA}" ], "reasoning": "To verify the statement, check the row where the variant is E545K and see if the corresponding disease in the Diseases column is Glioblastoma.", "statement": "The variant E545K is associated with the disease Glioblastoma.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Results of the difficulty prediction approaches. SVM (original) has been taken from Beinborn (2016)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/471.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of the difficulty prediction approaches. SVM (original) has been taken from Beinborn (2016)" ], "reasoning": "The SVM model, both original and reproduced, shows strong performance in difficulty prediction tasks, especially in generalizing to new data.", "description": "The right-hand side of table 1 shows the performance of our SVM and the two neural methods. The results indicate that the SVM setup is well suited for the difficulty prediction task and that it successfully generalizes to new data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Results of the difficulty prediction approaches. SVM (original) has been taken from Beinborn (2016)'\nProblem: 'Which model has the highest Original data qwk value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/471.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of the difficulty prediction approaches. SVM (original) has been taken from Beinborn (2016)" ], "reasoning": "By comparing the Original data qwk values for all models, the SVM (reproduced) has the highest value of 0.47.", "question": "Which model has the highest Original data qwk value?", "answer": "So, the answer is {\"answer\": \"SVM (reproduced)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe MLP model has the lowest New data rmse value.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/471.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of the difficulty prediction approaches. SVM (original) has been taken from Beinborn (2016)" ], "reasoning": "By comparing the New data rmse values for all models, the MLP model has a value of 0.22, which is not the lowest. The SVM (reproduced) has the lowest value of 0.21.", "statement": "The MLP model has the lowest New data rmse value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: rmse for both strategies on each corpora with randomly sampled target difficulties τ'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/472.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: rmse for both strategies on each corpora with randomly sampled target difficulties τ" ], "reasoning": "SEL consistently outperforms SIZE across all three corpora, as shown in Table 2.", "description": "Table 2 shows the results for our three corpora. Throughout all three corpora, both manipulation strategies perform well. SEL consistently outperforms SIZE, which matches our observations from the previous experiment." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: rmse for both strategies on each corpora with randomly sampled target difficulties τ'\nProblem: 'Which strategy performed better in the Reuters corpus?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/472.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: rmse for both strategies on each corpora with randomly sampled target difficulties τ" ], "reasoning": "By comparing the values in the Reuters column, SEL has a value of .12 while SIZE has a value of .15. Therefore, SIZE performed better in the Reuters corpus.", "question": "Which strategy performed better in the Reuters corpus?", "answer": "{\"answer\": \"SIZE\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSEL performed better than SIZE in the Brown corpus.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/472.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: rmse for both strategies on each corpora with randomly sampled target difficulties τ" ], "reasoning": "In the Brown corpus, SEL has a value of .11 while SIZE has a value of .13. Therefore, SIZE performed better than SEL in the Brown corpus.", "statement": "SEL performed better than SIZE in the Brown corpus.", "answer": "{\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Mean error rates e(T) per text and strategy. Results marked with ∗ deviate significantly from DEF'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/473.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Mean error rates e(T) per text and strategy. Results marked with ∗ deviate significantly from DEF" ], "reasoning": "Both SEL and SIZE strategies significantly affect the test's difficulty compared to DEF, with most individual text and strategy pairs showing statistically significant results.", "description": "Table 3 shows the error rates per C-test and strategy. Both SEL and SIZE are overall able to significantly (p < 0.025) increase and decrease the test's difficulty over DEF, and with the exception of. SEL,dec , the effect is also statistically significant 4 for all individual text and strategy pairs." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Mean error rates e(T) per text and strategy. Results marked with ∗ deviate significantly from DEF'\nProblem: 'What is the average error rate for the hard (inc) SEL strategy?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/473.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Mean error rates e(T) per text and strategy. Results marked with ∗ deviate significantly from DEF" ], "reasoning": "To find the average error rate for the hard (inc) SEL strategy, look at the 'Average' row under the 'hard (inc) SEL' column.", "question": "What is the average error rate for the hard (inc) SEL strategy?", "answer": "So, the answer is {\"answer\": \"0.53\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe error rate for T2 using the easy (dec) SIZE strategy is lower than the error rate for T2 using the default DEF strategy.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/473.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Mean error rates e(T) per text and strategy. Results marked with ∗ deviate significantly from DEF" ], "reasoning": "The error rate for T2 using the easy (dec) SIZE strategy is 0.11, while the error rate for T2 using the default DEF strategy is 0.34. Since 0.11 is lower than 0.34, the statement is supported.", "statement": "The error rate for T2 using the easy (dec) SIZE strategy is lower than the error rate for T2 using the default DEF strategy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: FrameId results (in %) on English (upper) and German (lower) with and without using the lexicon. Reported are accuracy and F1-macro, both also for ambiguous predicates (mean scores over ten runs). Models: (a) Data, Lexicon, and Data-Lexicon Baselines. (b) Previous models for English. (c) Ours: unimodal our-uni, multimodal on top of our-uni – our-mm – with Imagined embeddings (and synset visual embeddings for English). Best results highlighted in bold. The best run’s results for English were: our_uni: acc: 89.35 ; acc_amb: 76.45 ; F1-m: 76.95 ; F1-m_amb: 54.02 (with lexicon) our_mm (im, synsV): acc: 89.09 ; acc_amb: 75.86 ; F1-m: 78.17 ; F1-m_amb: 57.48 (with lexicon)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/474.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: FrameId results (in %) on English (upper) and German (lower) with and without using the lexicon. Reported are accuracy and F1-macro, both also for ambiguous predicates (mean scores over ten runs). Models: (a) Data, Lexicon, and Data-Lexicon Baselines. (b) Previous models for English. (c) Ours: unimodal our-uni, multimodal on top of our-uni – our-mm – with Imagined embeddings (and synset visual embeddings for English). Best results highlighted in bold. The best run’s results for English were: our_uni: acc: 89.35 ; acc_amb: 76.45 ; F1-m: 76.95 ; F1-m_amb: 54.02 (with lexicon) our_mm (im, synsV): acc: 89.09 ; acc_amb: 75.86 ; F1-m: 78.17 ; F1-m_amb: 57.48 (with lexicon)" ], "reasoning": "Our unimodal system trained on English data achieved an accuracy of 89.35%, surpassing the previous state of the art of 88.41%. For German data, our system set a new state of the art with 80.76% accuracy.", "description": "First, we report our results on English data (see Table 3, top). we compare against German data (see Table 3, bottom).. Our new strong Data-Lexicon Baseline reaches a considerable accuracy of 86.32 %, which is hard to beat by trained models. Even the most recent state of the art only beats it by about two points: 88.41 % (Hermann et al., 2014). However, the accuracy of the baseline drops for ambiguous predicates (69.73 %) and the F1-macro score reveals its weakness toward minority classes (drop from 64.54 % to 37.42 %).. Our unimodal system trained and evaluated on English data slightly exceeds the accuracy of the previous state of the art (88.66 % on average versus 88.41 % for Hermann et al., 2014); our best run's accuracy is 89.35 %.. Table 3: FrameId results (in %) on English (upper) and German (lower) with and without using the lexicon. Reported are accuracy and F1-macro, both also for ambiguous predicates (mean scores over ten runs). Models: (a) Data, Lexicon, and Data-Lexicon Baselines. (b) Previous models for English. (c) Ours: unimodal our-uni, multimodal on top of our-uni – our-mm – with IMAGINED embeddings (and synset visual embeddings for English). Best results highlighted in bold. The best run's results for English were: our uni: acc: 89.35 ; acc amb: 76.45 ; F1-m: 76.95 ; F1-m amb: 54.02 (with lexicon) our mm (im, synsV): acc: 89.09 ; acc amb: 75.86 ; F1-m: 78.17 ; F1-m amb: 57.48 (with lexicon). Our system evaluated on German data sets a new state of the art on this corpus with 80.76 % accuracy, outperforming the baselines (77.16 %; no other system evaluated on this dataset).. We report results achieved without the lexicon to evaluate independently of its quality (Hartmann et al., 2017). On English data, our systems outperforms Hartmann et al. (2017) by more than two points in accuracy and we achieve a large improvement over the Data Baseline. Comparing the F1-macro with and without lexicon, it can be seen that the additional information stored in the lexicon strongly increases the score by about 20 points for English data. For German data, the increase of F1-macro with lexicon versus without is small (one point)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: FrameId results (in %) on English (upper) and German (lower) with and without using the lexicon. Reported are accuracy and F1-macro, both also for ambiguous predicates (mean scores over ten runs). Models: (a) Data, Lexicon, and Data-Lexicon Baselines. (b) Previous models for English. (c) Ours: unimodal our-uni, multimodal on top of our-uni – our-mm – with Imagined embeddings (and synset visual embeddings for English). Best results highlighted in bold. The best run’s results for English were: our_uni: acc: 89.35 ; acc_amb: 76.45 ; F1-m: 76.95 ; F1-m_amb: 54.02 (with lexicon) our_mm (im, synsV): acc: 89.09 ; acc_amb: 75.86 ; F1-m: 78.17 ; F1-m_amb: 57.48 (with lexicon)'\nProblem: 'What is the accuracy of the best run of the unimodal system on English data with the lexicon?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/474.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: FrameId results (in %) on English (upper) and German (lower) with and without using the lexicon. Reported are accuracy and F1-macro, both also for ambiguous predicates (mean scores over ten runs). Models: (a) Data, Lexicon, and Data-Lexicon Baselines. (b) Previous models for English. (c) Ours: unimodal our-uni, multimodal on top of our-uni – our-mm – with Imagined embeddings (and synset visual embeddings for English). Best results highlighted in bold. The best run’s results for English were: our_uni: acc: 89.35 ; acc_amb: 76.45 ; F1-m: 76.95 ; F1-m_amb: 54.02 (with lexicon) our_mm (im, synsV): acc: 89.09 ; acc_amb: 75.86 ; F1-m: 78.17 ; F1-m_amb: 57.48 (with lexicon)" ], "reasoning": "The table shows that the best run of the unimodal system (our_uni) on English data with the lexicon achieved an accuracy of 89.35%.", "question": "What is the accuracy of the best run of the unimodal system on English data with the lexicon?", "answer": "So, the answer is {\"answer\": \"89.35\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe accuracy of the Data-Lexicon Baseline on German data without the lexicon is 77.16%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/474.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: FrameId results (in %) on English (upper) and German (lower) with and without using the lexicon. Reported are accuracy and F1-macro, both also for ambiguous predicates (mean scores over ten runs). Models: (a) Data, Lexicon, and Data-Lexicon Baselines. (b) Previous models for English. (c) Ours: unimodal our-uni, multimodal on top of our-uni – our-mm – with Imagined embeddings (and synset visual embeddings for English). Best results highlighted in bold. The best run’s results for English were: our_uni: acc: 89.35 ; acc_amb: 76.45 ; F1-m: 76.95 ; F1-m_amb: 54.02 (with lexicon) our_mm (im, synsV): acc: 89.09 ; acc_amb: 75.86 ; F1-m: 78.17 ; F1-m_amb: 57.48 (with lexicon)" ], "reasoning": "The table shows that the accuracy of the Data-Lexicon Baseline on German data without the lexicon is indeed 77.16%.", "statement": "The accuracy of the Data-Lexicon Baseline on German data without the lexicon is 77.16%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1. The performances of various models on MSCOCO Karpathy split'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/475.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. The performances of various models on MSCOCO Karpathy split" ], "reasoning": "Our model achieves the highest scores across all automatic evaluation metrics, particularly excelling in the CIDEr score with 123.5.", "description": "The automatic evaluation results on the test set of MSCOCO are shown in Table 1. Our model outperforms all the compared approaches on all automatic evaluation metrics. In particular, by benefiting from the retrieved captions, our model gets the highest CIDEr score of 123.5, suggesting that the captions generated by our model are informative." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1. The performances of various models on MSCOCO Karpathy split'\nProblem: 'Which model achieved the highest CIDEr score and what was the score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/475.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. The performances of various models on MSCOCO Karpathy split" ], "reasoning": "By examining the CIDEr column, we see that the highest score is 123.5, achieved by the UGRIC model.", "question": "Which model achieved the highest CIDEr score?", "answer": "So, the answer is {\"answer\": \"UGRIC\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe UGRIC model achieved the highest BLEU-4 score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/475.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. The performances of various models on MSCOCO Karpathy split" ], "reasoning": "By looking at the BLEU-4 column, we see that the highest score is 38.4, achieved by the UGRIC model.", "statement": "The UGRIC model achieved the highest BLEU-4 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Fraction of incorrect summaries produced by recent summarization systems on the CNN-DM test set, evaluated on a subset of 100 summaries. ROUGE scores (on full test set) and average summary length for reference.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/476.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Fraction of incorrect summaries produced by recent summarization systems on the CNN-DM test set, evaluated on a subset of 100 summaries. ROUGE scores (on full test set) and average summary length for reference." ], "reasoning": "FAS and BUS have a higher fraction of incorrect summaries compared to PGC, and the higher abstractiveness of FAS and BUS summaries increases the chance of errors.", "description": "Table 1 shows the evaluation results. In line with the findings for sentence summarization (Cao et al., 2018; Li et al., 2018), we observe that factual errors are also a frequent problem for document summarization. Interestingly, the fraction of incorrect summaries is substantially higher for FAS and BUS compared to PGC. The length of the generated summaries appears to be unrelated to the number of errors. Instead, the higher abstractiveness of summaries produced by FAS and BUS, as analyzed in their respective papers, seems to also increase the chance of introducing errors. In addition, we also observe that among the three systems correctness and ROUGE scores do not correlate, emphasizing one more time that a ROUGE based evaluation alone is far too limited to account for the full scope of the summarization task." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Fraction of incorrect summaries produced by recent summarization systems on the CNN-DM test set, evaluated on a subset of 100 summaries. ROUGE scores (on full test set) and average summary length for reference.'\nProblem: 'Which model has the highest ROUGE-1 score and what is its fraction of incorrect summaries?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/476.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Fraction of incorrect summaries produced by recent summarization systems on the CNN-DM test set, evaluated on a subset of 100 summaries. ROUGE scores (on full test set) and average summary length for reference." ], "reasoning": "From the table, BUS has the highest ROUGE-1 score of 41.52%.", "question": "Which model has the highest ROUGE-1 score?", "answer": "So, the answer is {\"answer\": \"BUS gehmann18\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe length of the generated summaries is related to the number of errors.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/476.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Fraction of incorrect summaries produced by recent summarization systems on the CNN-DM test set, evaluated on a subset of 100 summaries. ROUGE scores (on full test set) and average summary length for reference." ], "reasoning": "The description states that the length of the generated summaries appears to be unrelated to the number of errors.", "statement": "The length of the generated summaries is related to the number of errors.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Fraction of incorrect summaries at first position after reranking with different NLI models. ↑ and ↓ show the absolute number of improved (incorrect replaced by correct) and worsened (vice versa) instances.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/477.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Fraction of incorrect summaries at first position after reranking with different NLI models. ↑ and ↓ show the absolute number of improved (incorrect replaced by correct) and worsened (vice versa) instances." ], "reasoning": "Reranking with ESIM and InferSent models slightly improves validation results but increases incorrect summaries in the test set.", "description": "For 107 out of the 200 documents, an incorrect and correct summary is among the 5 alternatives. Table 2 shows that in this sample from the validation data, the fraction of incorrect summaries at first position, when the 5 alternatives are ranked as during beam search, is at 42.1%.. Using entailment probabilities of ESIM and InferSent, we can slightly improve upon that and reduce incorrect summaries. However, with DA and SSE, more incorrect summaries end up in the first position. Note that these results are not in line with the model's NLI accuracies, underlining that performance on NLI does not directly transfer to our task. Only for BERT, which outperforms the other models on NLI by a large margin, we also see substantially better reranking performance. But even for this powerful model, more than half of the errors still remain in the summaries.5 Interestingly, we also find that for ESIM and InferSent, reranking hurts in many cases, leaving just a few cases of net improvement.. Given the validation results, we then applied reranking to the CNN-DM test data followed by a post-hoc correctness evaluation as in Section 4. We used the ESIM model and reranked all 10. beam hypotheses generated by FAS.6 In contrast to the validation sample, the fraction of incorrect summaries increases from 26% to 29% (Table 2), demonstrating that the slight improvement on the validation data does not transfer to the test set." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Fraction of incorrect summaries at first position after reranking with different NLI models. ↑ and ↓ show the absolute number of improved (incorrect replaced by correct) and worsened (vice versa) instances.'\nProblem: 'Which NLI model showed the largest decrease in incorrect summaries on the validation data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/477.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Fraction of incorrect summaries at first position after reranking with different NLI models. ↑ and ↓ show the absolute number of improved (incorrect replaced by correct) and worsened (vice versa) instances." ], "reasoning": "By comparing the 'Δ' column for the validation data, BERT shows the largest decrease with -14.1.", "question": "Which NLI model showed the largest decrease in incorrect summaries on the validation data?", "answer": "So, the answer is {\"answer\": \"BERT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ESIM model improved the fraction of incorrect summaries on the test data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/477.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Fraction of incorrect summaries at first position after reranking with different NLI models. ↑ and ↓ show the absolute number of improved (incorrect replaced by correct) and worsened (vice versa) instances." ], "reasoning": "The table shows that the fraction of incorrect summaries increased from 26% to 29% when using the ESIM model on the test data.", "statement": "The ESIM model improved the fraction of incorrect summaries on the test data.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: French contraction rules.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/478.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: French contraction rules." ], "reasoning": "The table compares different French prepositional contractions and their corresponding forms.", "description": "The two best-performing approaches in the task of generating sentences from dependency trees have been feature-based incremental text generation (Bohnet et al., 2010; Liu et al., 2015; Puduppully et al., 2016; King and White, 2018) and 2http://universaldependencies.org/ techniques performing more global input-output mapping (Castro Ferreira et al., 2018; Elder and Hokamp, 2018).. Each of these approaches has their advantages and limitations (Table 2)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: French contraction rules.'\nProblem: 'What is the contraction form of 'à les'?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/478.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: French contraction rules." ], "reasoning": "To find the contraction form of 'à les', look at the first column of the table where 'à les' is listed and find its corresponding contraction form.", "question": "What is the contraction form of 'à les'?", "answer": "So, the answer is {\"answer\": \"aux\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe contraction form of 'de les' is 'des'.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/478.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: French contraction rules." ], "reasoning": "To verify the statement, look at the last row of the table where 'de les' is listed and find its corresponding contraction form.", "statement": "The contraction form of 'de les' is 'des'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Accuracy of postle models on *BLESS datasets, for two different sets of English distributional vectors: Skip-Gram (SG) and GloVe (GL). lear reports highest scores on *BLESS datasets in the literature.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/479.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy of postle models on *BLESS datasets, for two different sets of English distributional vectors: Skip-Gram (SG) and GloVe (GL). lear reports highest scores on *BLESS datasets in the literature." ], "reasoning": "POSTLE models perform identically to LEAR in the FULL setting due to complete lexical constraint coverage, but outperform LEAR in the DISJOINT setting by generalizing LEAR-specializations to unseen test words.", "description": "The accuracy scores on *BLESS test sets are provided in Table 1.8 Our POSTLE models display exactly the same performance as LEAR in the FULL setting: this is simply because all words found in *BLESS datasets are covered by the lexical constraints, and POSTLE does not generalize the initial LEAR transformation to unseen test words. In the DISJOINT setting, however, LEAR is left \"blind\" as it has not seen a single test word in the constraints: it leaves distributional vectors of *BLESS test words identical. In this setting, LEAR performance is equivalent to the original distributional space. In contrast, learning to generalize the LE specialization function from LEAR-specializations of other words, POSTLE models are able to successfully LE-specialize vectors of test *BLESS words. As in the graded LE, the adversarial POSTLE architecture outperforms the simpler DFFN model." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Accuracy of postle models on *BLESS datasets, for two different sets of English distributional vectors: Skip-Gram (SG) and GloVe (GL). lear reports highest scores on *BLESS datasets in the literature.'\nProblem: 'What is the accuracy score of the POSTLE adv model in the DISJOINT setting for the DBLESS test set using the SG method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/479.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy of postle models on *BLESS datasets, for two different sets of English distributional vectors: Skip-Gram (SG) and GloVe (GL). lear reports highest scores on *BLESS datasets in the literature." ], "reasoning": "To find the accuracy score of the POSTLE adv model in the DISJOINT setting for the DBLESS test set using the SG method, locate the row for 'postle adv' and the column for 'Setup: Disjoint DBLESS SG'. The value is 0.942.", "question": "What is the accuracy score of the POSTLE adv model in the DISJOINT setting for the DBLESS test set using the SG method?", "answer": "So, the answer is {\"answer\": \"0.942\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LEAR model performs better than the POSTLE adv model in the DISJOINT setting for the WBLESS test set using the GL method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/479.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy of postle models on *BLESS datasets, for two different sets of English distributional vectors: Skip-Gram (SG) and GloVe (GL). lear reports highest scores on *BLESS datasets in the literature." ], "reasoning": "To verify this statement, compare the accuracy scores of the LEAR and POSTLE adv models in the DISJOINT setting for the WBLESS test set using the GL method. LEAR has a score of 0.746, while POSTLE adv has a score of 0.757. Therefore, the statement is refuted.", "statement": "The LEAR model performs better than the POSTLE adv model in the DISJOINT setting for the WBLESS test set using the GL method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Average precision (AP) of postle models in cross-lingual transfer. Results are shown for both postle models (dffn and adv), two target languages (Spanish and French) and three methods for inducing bilingual vector spaces: Ar [Artetxe:2018acl], Co [Conneau:2018iclr], and Sm [Smith:2017iclr].'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/480.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average precision (AP) of postle models in cross-lingual transfer. Results are shown for both postle models (dffn and adv), two target languages (Spanish and French) and three methods for inducing bilingual vector spaces: Ar [Artetxe:2018acl], Co [Conneau:2018iclr], and Sm [Smith:2017iclr]." ], "reasoning": "The transferred POSTLE methods significantly outperform both the distributional vectors and the random baseline in AP scores for cross-lingual word embedding induction.", "description": "The average precision (AP) ranking scores achieved via cross-lingual transfer of POSTLE are shown in Table 2. We report AP scores using three methods for cross-lingual word embedding induction, and compare their performance to two baselines: 1) random word pair scoring, and 2) the original (FASTTEXT) vectors. The results uncover the inability of distributional vectors to capture LE – they yield lower performance than the random baseline, which strongly emphasizes the need for the LE-specialization. The transferred POSTLE yields an immense improve" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Average precision (AP) of postle models in cross-lingual transfer. Results are shown for both postle models (dffn and adv), two target languages (Spanish and French) and three methods for inducing bilingual vector spaces: Ar [Artetxe:2018acl], Co [Conneau:2018iclr], and Sm [Smith:2017iclr].'\nProblem: 'Which method achieved the highest AP score for the target language French?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/480.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average precision (AP) of postle models in cross-lingual transfer. Results are shown for both postle models (dffn and adv), two target languages (Spanish and French) and three methods for inducing bilingual vector spaces: Ar [Artetxe:2018acl], Co [Conneau:2018iclr], and Sm [Smith:2017iclr]." ], "reasoning": "By comparing the AP scores for the target language French across all methods, the highest score is found under 'postle adv' with a score of .786.", "question": "Which method achieved the highest AP score for the target language French?", "answer": "So, the answer is {\"answer\": \"0.786\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe distributional vectors performed better than the random baseline for the target language Spanish.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/480.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average precision (AP) of postle models in cross-lingual transfer. Results are shown for both postle models (dffn and adv), two target languages (Spanish and French) and three methods for inducing bilingual vector spaces: Ar [Artetxe:2018acl], Co [Conneau:2018iclr], and Sm [Smith:2017iclr]." ], "reasoning": "The table shows that the AP scores for distributional vectors (.362) are lower than the random baseline (.498) for the target language Spanish.", "statement": "The distributional vectors performed better than the random baseline for the target language Spanish.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Evaluations based on maximum span, MINA, and head spans on the CoNLL-2012 test set and WikiCoref. The ranking of corresponding scores is specified in parentheses. Rankings which are different based on maximum vs. MINA spans are highlighted.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/481.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluations based on maximum span, MINA, and head spans on the CoNLL-2012 test set and WikiCoref. The ranking of corresponding scores is specified in parentheses. Rankings which are different based on maximum vs. MINA spans are highlighted." ], "reasoning": "The reinforcement learning model of deep-coref has the most significant difference in scores between maximum and minimum spans, while the ensemble model of e2e-coref has the least difference, indicating better recognition of maximum span boundaries in out-of-domain data.", "description": "The reinforcement learning model of deep-coref, i.e., deep-coref RL, has the most significant difference when it is evaluated based on maximum vs. minimum spans (about 4 points). The ensemble model of e2e-coref, on the other hand, has the least difference between maximum and minimum span scores (1.4 points), which indicates it better recognizes maximum span boundaries in out-of-domain data. The ranking of systems is very different by using maximum vs. min. Table 4 shows the maximum vs. minimum span evaluations of several recent coreference resolvers on the CoNLL-2012 test set and the WikiCoref dataset. The examined coreference resolvers are as follows: the Stanford rule-based system (Lee et al., 2013), the coreference resolver of Peng et al. (2015), the ranking model of cort (Martschat and Strube, 2015), the ranking and reinforcement learning models of deep-coref (Clark and Manning, 2016a,b), the single and ensemble models of Lee et al. (2017), and the current stateof-the-art system by Lee et al. (2018).. The coreference resolver of Peng et al. (2015) has the smallest difference between its maximum and minimum span evaluation scores.. Based on maximum spans, Peng et al. (2015) performs on-par with cort while cort outperforms it by about one percent when they are evaluated based on minimum spans." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Evaluations based on maximum span, MINA, and head spans on the CoNLL-2012 test set and WikiCoref. The ranking of corresponding scores is specified in parentheses. Rankings which are different based on maximum vs. MINA spans are highlighted.'\nProblem: 'Which coreference resolver has the smallest difference between its maximum and minimum span evaluation scores on the CoNLL-2012 test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/481.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluations based on maximum span, MINA, and head spans on the CoNLL-2012 test set and WikiCoref. The ranking of corresponding scores is specified in parentheses. Rankings which are different based on maximum vs. MINA spans are highlighted." ], "reasoning": "By comparing the differences between the maximum and minimum span scores in the CoNLL-2012 test set, the coreference resolver of Peng et al. (2015) has the smallest difference (63.05 - 63.50 = -0.45).", "question": "Which coreference resolver has the smallest difference between its maximum and minimum span evaluation scores on the CoNLL-2012 test set?", "answer": "So, the answer is {\"answer\": \"Peng et al. (2015)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe coreference resolver of Peng et al. (2015) performs better than cort based on maximum spans.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/481.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluations based on maximum span, MINA, and head spans on the CoNLL-2012 test set and WikiCoref. The ranking of corresponding scores is specified in parentheses. Rankings which are different based on maximum vs. MINA spans are highlighted." ], "reasoning": "The table shows that Peng et al. (2015) has a maximum span score of 63.05, while cort has a maximum span score of 63.03 on the CoNLL-2012 test set, indicating that Peng et al. (2015) performs slightly better.", "statement": "The coreference resolver of Peng et al. (2015) performs better than cort based on maximum spans.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Quality of the regression model’s predictions on the test set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/482.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Quality of the regression model’s predictions on the test set." ], "reasoning": "Experiment 2 shows an improvement with a lower MSE and a higher R2 score compared to Experiment 1.", "description": "Table 1 shows and compares MSE and R2 reported from these experiments. The results show that in experiment 2, the mean squared error is reduced and the r2 score is increased." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Quality of the regression model’s predictions on the test set.'\nProblem: 'Which experiment has the highest R2 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/482.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Quality of the regression model’s predictions on the test set." ], "reasoning": "By comparing the R2 scores of all experiments, Experiment 3 has the highest R2 score of 3.39857.", "question": "Which experiment has the highest R2 score?", "answer": "So, the answer is {\"answer\": \"Experiment 3\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nExperiment 1 has a lower MSE than Experiment 3.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/482.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Quality of the regression model’s predictions on the test set." ], "reasoning": "The table shows that Experiment 1 has an MSE of 0.03448, which is lower than Experiment 3's MSE of 0.17112.", "statement": "Experiment 1 has a lower MSE than Experiment 3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: The Accuracy of Different Syntactic Representations'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/483.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: The Accuracy of Different Syntactic Representations" ], "reasoning": "POS-CNN outperforms ST-CNN by a large margin across all benchmark datasets, and POS-HAN consistently outperforms POS-CNN.", "description": "Table III reports the accuracy of different syntactic representations for all the benchmark datasets. In ST encoding, the. the corresponding paper. The experimental results demon strate that our proposed syntactic representation (POS-CNN outperforms the previously proposed method (ST-CNN) by the benchmark datasets (38.6% in a large margin in all CCAT10, 30.80% in CCAT50, 19.62% in BLOGS10, 11.94% in BLOGS50). This improvement in performance can be due. syntactic representations are kept identical. According to Table III, POS-HAN outperforms POS-CNN model consistently across all the benchmark datasets (1.74% in CCAT10, 0.32% in CCAT50, 1.06% in BLOGS10, 2.91% in BLOGS50). This" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: The Accuracy of Different Syntactic Representations'\nProblem: 'Which model has the highest accuracy in the BLOGS50 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/483.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: The Accuracy of Different Syntactic Representations" ], "reasoning": "By comparing the accuracy values in the BLOGS50 column, POS-HAN has the highest accuracy with 57.76%.", "question": "Which model has the highest accuracy in the BLOGS50 dataset?", "answer": "So, the answer is {\"answer\": \"POS-HAN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPOS-CNN has a higher accuracy than ST-CNN in the CCAT50 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/483.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: The Accuracy of Different Syntactic Representations" ], "reasoning": "According to the table, POS-CNN has an accuracy of 40.98% in CCAT50, which is higher than ST-CNN's accuracy of 10.08%.", "statement": "POS-CNN has a higher accuracy than ST-CNN in the CCAT50 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: The Accuracy of Syntactic (Syntactic-HAN), Lexical (Lexical-HAN),and combined (Style-HAN) Models'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/484.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: The Accuracy of Syntactic (Syntactic-HAN), Lexical (Lexical-HAN),and combined (Style-HAN) Models" ], "reasoning": "Combining Syntactic-HAN with Lexical-HAN to form Style-HAN consistently improves performance across all benchmark datasets.", "description": "the table, lexical model consistently outperforms the syntactic model across all the benchmark datasets. Moreover, combining the two representations further improves the performance results. Based on the observation, we realize that even if Syntactic-HAN achieves a comparable performance results combining it with Lexical-HAN, slightly improves the overall performance (Style-HAN). As shown in Table IV, the performance improvement in terms of accuracy is consistent across all the benchmark datasets (4.54% in CCAT10, 2.85% in CCAT50, 2.02% in BLOGS10, 1.42% in BLOGS50)" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: The Accuracy of Syntactic (Syntactic-HAN), Lexical (Lexical-HAN),and combined (Style-HAN) Models'\nProblem: 'What is the accuracy improvement of Style-HAN over Lexical-HAN in the CCAT50 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/484.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: The Accuracy of Syntactic (Syntactic-HAN), Lexical (Lexical-HAN),and combined (Style-HAN) Models" ], "reasoning": "To find the accuracy improvement, subtract the accuracy of Lexical-HAN from the accuracy of Style-HAN in the CCAT50 dataset: 82.35 - 79.50.", "question": "What is the accuracy improvement of Style-HAN over Lexical-HAN in the CCAT50 dataset?", "answer": "So, the answer is {\"answer\": \"2.85\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nStyle-HAN achieves the highest accuracy in the CCAT10 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/484.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: The Accuracy of Syntactic (Syntactic-HAN), Lexical (Lexical-HAN),and combined (Style-HAN) Models" ], "reasoning": "The table shows that Style-HAN has an accuracy of 90.58 in the CCAT10 dataset, which is higher than both Syntactic-HAN and Lexical-HAN.", "statement": "Style-HAN achieves the highest accuracy in the CCAT10 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: The accuracy of Different Fusion Approaches'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/485.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: The accuracy of Different Fusion Approaches" ], "reasoning": "Training two parallel networks for lexical and syntax encoding achieves higher accuracy than using combined embeddings for the CCAT10 and BLOG10 datasets.", "description": "Table V reports the accuracy of the combined and the parallel fusion approaches. According to these results, training two parallel networks for lexical and syntax encoding achieves higher accuracy when compared to training the same network with combined embeddings." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: The accuracy of Different Fusion Approaches'\nProblem: 'Which dataset shows the highest accuracy for the parallel fusion approach?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/485.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: The accuracy of Different Fusion Approaches" ], "reasoning": "By comparing the accuracy values in the 'Parallel' column, the highest accuracy is 90.58 for the CCAT10 dataset.", "question": "Which dataset shows the highest accuracy for the parallel fusion approach?", "answer": "So, the answer is {\"answer\": \"CCAT10\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe combined approach achieves higher accuracy than the parallel approach for the CCAT50 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/485.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: The accuracy of Different Fusion Approaches" ], "reasoning": "The table shows that for the CCAT50 dataset, the combined approach has an accuracy of 81.21, while the parallel approach has an accuracy of 82.35. Therefore, the statement is refuted.", "statement": "The combined approach achieves higher accuracy than the parallel approach for the CCAT50 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VI: Test Accuracy of models for each dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/486.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Test Accuracy of models for each dataset" ], "reasoning": "Style-HAN outperforms other models on all four benchmark datasets, with improvements of 2.38%, 1.35%, 8.73%, and 4.46% over CCAT10, CCAT50, BLOGs10, and BLOGS50 respectively.", "description": "Table VI reports the accuracy of the models on the four benchmark datasets. All the results are bold. It shows that Style-HAN outperforms the baselines by 2.38%, 1.35%, 8.73%, and 4.46% over the CCAT10, CCAT50, BLOGs10, and BLOGS50 datasets, respectively." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VI: Test Accuracy of models for each dataset'\nProblem: 'Which model has the highest accuracy on the CCAT50 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/486.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Test Accuracy of models for each dataset" ], "reasoning": "By comparing the accuracy values for the CCAT50 dataset across all models, Style-HAN has the highest accuracy with 82.35.", "question": "Which model has the highest accuracy on the CCAT50 dataset?", "answer": "So, the answer is {\"answer\": \"Style-HAN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nStyle-HAN has the lowest accuracy on the BLOGS50 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/486.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Test Accuracy of models for each dataset" ], "reasoning": "By comparing the accuracy values for the BLOGS50 dataset across all models, Style-HAN has the highest accuracy with 61.19, not the lowest.", "statement": "Style-HAN has the lowest accuracy on the BLOGS50 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Experimental results for the discussion forum dataset (bold are best scores)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/487.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experimental results for the discussion forum dataset (bold are best scores)" ], "reasoning": "The LSTMconditional model achieved the highest F1 scores for both S and NS classes, with 73.32% and 70.56% respectively, showing significant improvements over other models.", "description": "Table 2 shows the classification results on the discussion forum dataset. Although a vast majority of the context posts contain 3-4 sentences, around 100 context posts have more than ten sentences and thus we set a cutoff to a maximum of ten sentences for context modeling. For the reply r we considered the entire reply.. The SV Mbl models that are based on discrete features did not perform very well, and adding context actually hurt the performance. Regarding the performance of the neural network models, we observe that modeling context improves the performance using all types of LSTM architectures that read both context (c) and reply (r). The highest performance when considering both the S and. S classes is achieved by the LSTMconditional model (73.32% F1 for S class and 70.56% F1 for. S, showing a 6% and 3% improvement over LSTMr for S and. S classes, respectively). The LSTM model with sentence-level attentions on both context and reply (LSTMcas +LSTMras ) gives the best F1 score of 73.7% for the S class. For the. S class, while we notice an improvement in precision we notice a drop in recall when compared to the LSTM model with sentence level attention only on reply (LSTMras ).. We observe the performance (69.88% F1 for S category) deteriorates, probably due to the lack of enough training data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Experimental results for the discussion forum dataset (bold are best scores)'\nProblem: 'Which model achieved the highest F1 score for the S class and what was the score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/487.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experimental results for the discussion forum dataset (bold are best scores)" ], "reasoning": "The table shows that the LSTMcas+LSTMras model achieved the highest F1 score for the S class with a score of 73.70%.", "question": "Which model achieved the highest F1 score for the S class?", "answer": { "answer": "LSTM cas+LSTM ras" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LSTMconditional model achieved the highest F1 score for the NS class.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/487.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experimental results for the discussion forum dataset (bold are best scores)" ], "reasoning": "The table shows that the LSTMconditional model achieved an F1 score of 70.56% for the NS class, which is the highest among all models listed.", "statement": "The LSTMconditional model achieved the highest F1 score for the NS class.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Experimental results for Twitter dataset (bold are best scores)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/488.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Experimental results for Twitter dataset (bold are best scores)" ], "reasoning": "The LSTMconditional model shows significant improvements in F1 scores for both S and NS classes compared to LSTMr, while attention-based models show smaller improvements.", "description": "Table 3 shows the results on the Twitter dataset.. As for discussion forums, adding context using the SVM models does not show a statistically significant improvement. For the neural networks model, similar to the results on discussion forums, the LSTM models that read both context and reply outperform the LSTM model that reads only the reply (LSTMr). The best performing architectures are again the LSTMconditional and LSTM with sentence-level attentions (LSTMcas +LSTMras ). LSTMconditional model shows an improvement of 11% F1 on the S class and 4-5%F1 on the. NS class, compared to LSTMr. For the attentionbased models, the improvement using context is smaller (∼2% F1)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Experimental results for Twitter dataset (bold are best scores)'\nProblem: 'Which model shows the highest F1 score for the NS class?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/488.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Experimental results for Twitter dataset (bold are best scores)" ], "reasoning": "By examining the table, the LSTM cas+LSTM ras model has the highest NS F1 score of 73.57.", "question": "Which model shows the highest F1 score for the NS class?", "answer": "So, the answer is {\"answer\": \"LSTM cas+LSTM ras\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LSTMconditional model shows the highest F1 score for the S class.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/488.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Experimental results for Twitter dataset (bold are best scores)" ], "reasoning": "The table shows that the LSTM cas+LSTM ras model has the highest F1 score for the S class at 76.36, not the LSTMconditional model.", "statement": "The LSTMconditional model shows the highest F1 score for the S class.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Speaker-dependent setup. All results are averaged across five folds where each fold present weighted F-score across both sarcastic and non-sarcastic classes.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/489.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Speaker-dependent setup. All results are averaged across five folds where each fold present weighted F-score across both sarcastic and non-sarcastic classes." ], "reasoning": "The combination of visual and textual features achieves the best performance, significantly improving over unimodal variants with a relative error rate reduction of up to 12.9%.", "description": "Table 2 presents the classification results for sarcasm prediction in the speaker-dependent setup.. The lowest performance is obtained with the Majority baseline which achieves 33.3% weighted Fscore (66.7% F-score for non-sarcastic class and 0% for sarcastic). The pre-trained features for the visual modality provide the best performance among the unimodal variants. The addition of textual features through concatenation improves the unimodal baseline and achieves the best performance. The tri-modal variant is unable to achieve the best score due to a slightly sub-optimal performance from the audio modality. Overall, the combination of visual and textual signals significantly improves over the unimodal variants, with a relative error rate reduction of up to 12.9%.. In multiple evaluations, the multimodal variants were shown to significantly outperform their unimodal counterparts, with relative error rate reductions of up to 12.9%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Speaker-dependent setup. All results are averaged across five folds where each fold present weighted F-score across both sarcastic and non-sarcastic classes.'\nProblem: 'Which modality combination achieves the highest F-score in sarcasm prediction?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/489.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Speaker-dependent setup. All results are averaged across five folds where each fold present weighted F-score across both sarcastic and non-sarcastic classes." ], "reasoning": "The table shows that the SVM algorithm with the T+V modality combination achieves the highest F-score of 71.6%.", "question": "Which modality combination achieves the highest F-score in sarcasm prediction?", "answer": "So, the answer is {\"answer\": \"T+V\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe tri-modal variant achieves the best performance in sarcasm prediction.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/489.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Speaker-dependent setup. All results are averaged across five folds where each fold present weighted F-score across both sarcastic and non-sarcastic classes." ], "reasoning": "The table shows that the tri-modal variant (T+A+V) has an F-score of 71.5%, which is slightly lower than the T+V variant with an F-score of 71.6%.", "statement": "The tri-modal variant achieves the best performance in sarcasm prediction.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Multimodal sarcasm classification. Evaluated using an speaker-independent setup. Note: T=text, A=audio, V=video.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/490.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Multimodal sarcasm classification. Evaluated using an speaker-independent setup. Note: T=text, A=audio, V=video." ], "reasoning": "In the speaker-independent setup, multimodal variants do not greatly outperform unimodal counterparts, with the audio channel playing a more important role and slightly improved by adding text.", "description": "Table 3 presents the performance of our baselines in the speaker-independent setup. In this case, the multimodal variants do not greatly outperform the unimodal counterparts. Unlike Table 2, the audio channel plays a more important role, and it is slightly improved by adding text.. In multiple evaluations, the multimodal variants were shown to significantly outperform their unimodal counterparts, with relative error rate reductions of up to 12.9%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Multimodal sarcasm classification. Evaluated using an speaker-independent setup. Note: T=text, A=audio, V=video.'\nProblem: 'Which modality combination achieved the highest F-Score in the speaker-independent setup?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/490.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Multimodal sarcasm classification. Evaluated using an speaker-independent setup. Note: T=text, A=audio, V=video." ], "reasoning": "By examining the F-Score column, the highest value is 63.1, which corresponds to the SVM algorithm with the T+A modality combination.", "question": "Which modality combination achieved the highest F-Score in the speaker-independent setup?", "answer": "So, the answer is {\"answer\": \"T+A\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe SVM algorithm with the A modality achieved the highest precision.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/490.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Multimodal sarcasm classification. Evaluated using an speaker-independent setup. Note: T=text, A=audio, V=video." ], "reasoning": "By examining the Precision column, the highest value is 65.1, which corresponds to the SVM algorithm with the A modality.", "statement": "The SVM algorithm with the A modality achieved the highest precision.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Role of context and utterance’s speaker. Note: T=text, A=audio, V=video.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/491.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Role of context and utterance’s speaker. Note: T=text, A=audio, V=video." ], "reasoning": "The best multimodal variant in the speaker dependent setup shows a slight improvement with context features, while other models do not show improvement.", "description": "Table 4 shows the results for both evaluation settings for the textual baseline and the best multimodal variant. For the context features, we see a slight improvement in the best variant of the speaker independent setup (text plus audio); however, in other models, there is no improvement." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Role of context and utterance’s speaker. Note: T=text, A=audio, V=video.'\nProblem: 'What is the F-Score of the best multimodal variant in the speaker independent setup?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/491.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Role of context and utterance’s speaker. Note: T=text, A=audio, V=video." ], "reasoning": "The best multimodal variant in the speaker independent setup is 'Best (T + A)' with an F-Score of 63.1.", "question": "What is the F-Score of the best multimodal variant in the speaker independent setup?", "answer": "So, the answer is {\"answer\": \"63.1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe speaker dependent setup with context features has the highest F-Score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/491.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Role of context and utterance’s speaker. Note: T=text, A=audio, V=video." ], "reasoning": "The highest F-Score in the speaker dependent setup is 71.8, achieved by both 'Best (T + V)' and '+ speaker' features, not '+ context' which has an F-Score of 65.0.", "statement": "The speaker dependent setup with context features has the highest F-Score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Semantic parsing accuracies (id = in domain test set; ood = out of domain test set).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/0.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Semantic parsing accuracies (id = in domain test set; ood = out of domain test set)." ], "reasoning": "The use of BERT embeddings significantly improved performance across all graphbanks, setting new state-of-the-art scores in most cases, especially in out-of-domain evaluations.", "description": "Table 1 (upper part) shows the results of our basic semantic parser (with GloVe embeddings) on all six graphbanks. Our results are competitive across the board, and set a new state of the art for EDS Smatch scores (Cai and Knight, 2013) among EDS parsers which are not trained on gold syntax information.. Our EDM score (Dridan and Oepen, 2011) on EDS is lower,. The use of BERT embeddings is highly effective across the board. We set a new state of the art (without gold syntax) on all graphbanks except AMR 2017;. The improvement is particularly pronounced in the out-of-domain evaluations, illustrating BERT's ability to transfer across domains.. The results on the test dataset are shown in Table 1 (bottom). With GloVe, multi-task learning led to substantial improvements; with BERT the improvements are smaller but still noticeable." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Semantic parsing accuracies (id = in domain test set; ood = out of domain test set).'\nProblem: 'Which method achieved the highest EDS Smatch F score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/0.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Semantic parsing accuracies (id = in domain test set; ood = out of domain test set)." ], "reasoning": "By examining the EDS Smatch F column, the highest score is 90.9, achieved by Chen et al. (2018).", "question": "Which method achieved the highest EDS Smatch F score?", "answer": { "answer": "Chen et al. (2018)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe use of BERT embeddings set a new state of the art for AMR 2017 Smatch F score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/0.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Semantic parsing accuracies (id = in domain test set; ood = out of domain test set)." ], "reasoning": "The table shows that the highest AMR 2017 Smatch F score is 76.3 by Zhang et al. (2019), which is higher than the scores achieved with BERT embeddings.", "statement": "The use of BERT embeddings set a new state of the art for AMR 2017 Smatch F score.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: All experiment results of transfer learning and abbreviation expansion (top-2 scores marked as bold). MedNLI (expanded) denotes MedNLI with abbreviation expansion.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/1.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: All experiment results of transfer learning and abbreviation expansion (top-2 scores marked as bold). MedNLI (expanded) denotes MedNLI with abbreviation expansion." ], "reasoning": "BioBERT shows better performance on MedNLI, especially with transfer learning, while BERT performs better on general domain tasks like MNLI and SNLI.", "description": "We conduct transfer learning on four different combinations of MedNLI, SNLI, and MNLI as it shown in the table 4 (line 4 to 7) and also add the results of general domain tasks (MNLI, SNLI) for comparison.. BERT performs better on tasks in the general domain while BioBERT performs better on MedNLI which is in the clinical domain.. positive transfer occurs on MedNLI.. even though BioBERT is finetuned on general domain tasks before MedNLI, transfer learning shows better results than that fine-tuned on MedNLI directly.. the domain specific language representations from BioBERT are maintained while fine-tuning on general domain tasks by showing that the transfer learning results of MedNLI on BioBERT have better performance than the results on BERT (line 4 to 7).. the accuracy of MNLI and SNLI on BioBERT is lower than the accuracy on BERT.. The best combination is SNLI → MNLI → MedNLI on BioBERT.. MedNLI (expanded) shows better performance than MedNLI on BioBERT while MedNLI works better on BERT (see table 4).. the performance of MedNLI (expanded) with transfer learning is higher on BERT and lower on BioBERT than the performance of MedNLI with transfer learning." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: All experiment results of transfer learning and abbreviation expansion (top-2 scores marked as bold). MedNLI (expanded) denotes MedNLI with abbreviation expansion.'\nProblem: 'Which model shows better performance on MedNLI with transfer learning from SNLI and MNLI?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/1.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: All experiment results of transfer learning and abbreviation expansion (top-2 scores marked as bold). MedNLI (expanded) denotes MedNLI with abbreviation expansion." ], "reasoning": "The table shows that BioBERT has higher accuracy on MedNLI when transfer learning from SNLI and MNLI (83.51 on dev and 82.63 on test) compared to BERT (81.72 on dev and 77.98 on test).", "question": "Which model shows better performance on MedNLI with transfer learning from SNLI and MNLI?", "answer": { "answer": "BioBERT" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBERT performs better than BioBERT on MedNLI when transfer learning from SNLI and MNLI.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/1.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: All experiment results of transfer learning and abbreviation expansion (top-2 scores marked as bold). MedNLI (expanded) denotes MedNLI with abbreviation expansion." ], "reasoning": "The table shows that BioBERT has higher accuracy on MedNLI with transfer learning from SNLI and MNLI (83.51 on dev and 82.63 on test) compared to BERT (81.72 on dev and 77.98 on test), refuting the statement.", "statement": "BERT performs better than BioBERT on MedNLI when transfer learning from SNLI and MNLI.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Average performance (across 100 seeds) of ELMo on the SST2 task. We show performance on A-but-B sentences (“but”), negations (“neg”).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/2.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average performance (across 100 seeds) of ELMo on the SST2 task. We show performance on A-but-B sentences (“but”), negations (“neg”)." ], "reasoning": "Switching to ELMo word embeddings improves performance by 2.9 percentage points on average, with significant improvements in A-but-B and negation style sentences.", "description": "Switching to ELMo word embeddings improves performance by 2.9 percentage points on an average, corresponding to about 53 test sentences. Of these, about 32 sentences (60% of the improvement) correspond to A-but-B and negation style sentences,. As further evidence that ELMo helps on these specific constructions, the non-ELMo baseline model (no-project, no-distill) gets 255 sentences wrong in the test corpus on average, only 89 (34.8%) of which are A-but-B style or negations." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Average performance (across 100 seeds) of ELMo on the SST2 task. We show performance on A-but-B sentences (“but”), negations (“neg”).'\nProblem: 'How many sentences does the non-ELMo baseline model get wrong on average in the test corpus?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/2.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average performance (across 100 seeds) of ELMo on the SST2 task. We show performance on A-but-B sentences (“but”), negations (“neg”)." ], "reasoning": "The tables shows that the ELMo model gets 88.96 in the test corpus.", "question": "What score does the ELMo achieved on the test corpus?", "answer": { "answer": "88.96" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ELMo model with no-project configuration improves performance by 2.9 percentage points on average.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/2.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average performance (across 100 seeds) of ELMo on the SST2 task. We show performance on A-but-B sentences (“but”), negations (“neg”)." ], "reasoning": "The description states that switching to ELMo word embeddings improves performance by 2.9 percentage points on average, and the table shows that the ELMo model with no-project configuration has a test score of 88.89, which is an improvement over the baseline.", "statement": "The ELMo model with no-project configuration improves performance by 2.9 percentage points on average.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Number of sentences in the crowdsourced study (447 sentences) which got marked as neutral and which got the opposite of their labels in the SST2 dataset, using various thresholds. Inter-annotator agreement is computed using Fleiss’ Kappa. Average accuracies of the baseline and ELMo (over 100 seeds) on non-neutral sentences are also shown.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/3.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Number of sentences in the crowdsourced study (447 sentences) which got marked as neutral and which got the opposite of their labels in the SST2 dataset, using various thresholds. Inter-annotator agreement is computed using Fleiss’ Kappa. Average accuracies of the baseline and ELMo (over 100 seeds) on non-neutral sentences are also shown." ], "reasoning": "Higher thresholds correspond to less ambiguous sentences, leading to higher inter-annotator agreement and improved model performance.", "description": "We present statistics of our dataset10 in Table 3.. As expected, inter-annotator agreement is higher for higher thresholds (less ambiguous sentences). According to Landis and Koch (1977), κ ∈ (0.2, 0.4] corresponds to \"fair agreement\", whereas κ ∈ (0.4, 0.6] corresponds to \"moderate agreement\".. We next compute the accuracy of our model for each threshold by removing the corresponding neutral sentences. Higher thresholds correspond to sets of less ambiguous sentences. Table 3 shows that ELMo's performance gains in Table 2 extends across all thresholds." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Number of sentences in the crowdsourced study (447 sentences) which got marked as neutral and which got the opposite of their labels in the SST2 dataset, using various thresholds. Inter-annotator agreement is computed using Fleiss’ Kappa. Average accuracies of the baseline and ELMo (over 100 seeds) on non-neutral sentences are also shown.'\nProblem: 'What is the Fleiss' Kappa value for the threshold with the highest number of neutral sentiments?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/3.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Number of sentences in the crowdsourced study (447 sentences) which got marked as neutral and which got the opposite of their labels in the SST2 dataset, using various thresholds. Inter-annotator agreement is computed using Fleiss’ Kappa. Average accuracies of the baseline and ELMo (over 100 seeds) on non-neutral sentences are also shown." ], "reasoning": "The threshold with the highest number of neutral sentiments is 0.90, which has 234 neutral sentiments. The Fleiss' Kappa value for this threshold is 0.58.", "question": "What is the Fleiss' Kappa value for the threshold with the highest number of neutral sentiments?", "answer": "So, the answer is {\"answer\": \"0.58\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Fleiss' Kappa value for the threshold 0.66 corresponds to 'moderate agreement'.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/3.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Number of sentences in the crowdsourced study (447 sentences) which got marked as neutral and which got the opposite of their labels in the SST2 dataset, using various thresholds. Inter-annotator agreement is computed using Fleiss’ Kappa. Average accuracies of the baseline and ELMo (over 100 seeds) on non-neutral sentences are also shown." ], "reasoning": "According to Landis and Koch (1977), a Fleiss' Kappa value between 0.4 and 0.6 corresponds to 'moderate agreement'. The Fleiss' Kappa value for the threshold 0.66 is 0.42, which falls within this range.", "statement": "The Fleiss' Kappa value for the threshold 0.66 corresponds to 'moderate agreement'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Tuning Data Results AVG_COS_SIM. Top F per Concept Input Type in Bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/4.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Tuning Data Results AVG_COS_SIM. Top F per Concept Input Type in Bold." ], "reasoning": "Google embeddings with TF*IDF weighting yield the highest F scores for all concept input types, with the best overall F score of .945 in the Both setting.", "description": "The top tuning data scores for AVG COS SIM (Table 1) show that the Google embeddings with TF*IDF weighting yield the top F score for all three concept input types (.881 - .945). Somewhat expectedly, the best overall F score (.945) is produced in the setting Both, which provides the most information. Actually, this is true for all four weighting schemes for both GloVe and Google, while fastText consistently yields its top F scores (.840 - .911) in the Label setting, which provides the least information." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Tuning Data Results AVG_COS_SIM. Top F per Concept Input Type in Bold.'\nProblem: 'Which embedding and weighting scheme combination yields the highest F score in the Both setting?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/4.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Tuning Data Results AVG_COS_SIM. Top F per Concept Input Type in Bold." ], "reasoning": "The table shows that the highest F score in the Both setting is .945, which is achieved by Google embeddings with TF*IDF weighting.", "question": "Which embedding and weighting scheme combination yields the highest F score in the Both setting?", "answer": "So, the answer is {\"answer\": \"Google embeddings with TF*IDF weighting\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe highest F score for fastText embeddings is achieved in the Both setting.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/4.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Tuning Data Results AVG_COS_SIM. Top F per Concept Input Type in Bold." ], "reasoning": "The table shows that the highest F score for fastText embeddings is .911, which is achieved in the Label setting, not the Both setting.", "statement": "The highest F score for fastText embeddings is achieved in the Both setting.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Tuning Data Results TOP_n_COS_SIM_AVG. Top F per Concept Input Type in Bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/5.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Tuning Data Results TOP_n_COS_SIM_AVG. Top F per Concept Input Type in Bold." ], "reasoning": "Google embeddings yield the best F score for the Label setting, GloVe leads in the Description setting, and fastText produces the best F score in the Both setting.", "description": "For TOP n COS SIM AVG, the tuning data results (Table 2) are somewhat more varied: First, there is no single best performing set of embeddings: Google yields the best F score for the Label setting (.953), while GloVe (though only barely) leads in the Description setting (.912). This time, it is fastText which produces the best F score in the Both setting, which is also the best overall tuning data F score for TOP n COS SIM AVG (.954)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Tuning Data Results TOP_n_COS_SIM_AVG. Top F per Concept Input Type in Bold.'\nProblem: 'Which embedding method produces the best overall tuning data F score for TOP n COS SIM AVG?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/5.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Tuning Data Results TOP_n_COS_SIM_AVG. Top F per Concept Input Type in Bold." ], "reasoning": "The description states that fastText produces the best F score in the Both setting, which is also the best overall tuning data F score for TOP n COS SIM AVG.", "question": "Which embedding method produces the best overall tuning data F score for TOP n COS SIM AVG?", "answer": { "answer": "fastText" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGoogle embeddings yield the best F score for the Description setting.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/5.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Tuning Data Results TOP_n_COS_SIM_AVG. Top F per Concept Input Type in Bold." ], "reasoning": "The table shows that GloVe leads in the Description setting with an F score of .912, not Google.", "statement": "Google embeddings yield the best F score for the Description setting.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Test Data Results'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/6.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Test Data Results" ], "reasoning": "The AVG COS SIM measure outperforms both the general-purpose embeddings (topic wiki) and the science domain-adapted embeddings (topic science) in all three settings, with the Both setting achieving the highest overall result of .926.", "description": "The results can be found in Table 3. For comparison, the two top rows provide the best results of Gong et al. (2018). The first interesting finding is that the AVG COS SIM measure again performs very well: In all three settings, it beats both the system based on general-purpose embeddings (topic wiki) and the one that is adapted to the science domain (topic science), with again the Both setting yielding the best overall result (.926). Note that our Both setting is probably the one most similar to the concept input used by Gong et al. (2018)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Test Data Results'\nProblem: 'Which method and setting combination achieved the highest F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/6.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Test Data Results" ], "reasoning": "The table shows that the AVG COS SIM method with the Both setting achieved the highest F1 score of 0.926.", "question": "Which method and setting combination achieved the highest F1 score?", "answer": { "answer": "0.926" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe AVG COS SIM method with the Both setting achieved the highest F1 score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/6.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Test Data Results" ], "reasoning": "The table indicates that the AVG COS SIM method with the Both setting has the highest F1 score of .926, which is higher than any other method and setting combination.", "statement": "The AVG COS SIM method with the Both setting achieved the highest F1 score.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Best results after re-ranking using different re-ranker, and different values for k-best hypotheses extracted from the baseline output (%). In addition, to evaluate our re-ranker with MRR we fixed k CNNk=8 LSTMk=4'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/7.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Best results after re-ranking using different re-ranker, and different values for k-best hypotheses extracted from the baseline output (%). In addition, to evaluate our re-ranker with MRR we fixed k CNNk=8 LSTMk=4" ], "reasoning": "The table compares various models' accuracy metrics on a dataset, showing that our FDCLSTM models outperform other baselines, especially in the LSTM full and MRR metrics.", "description": "We use two pre-trained deep models: a CNN (Jaderberg et al., 2016) and an LSTM (Ghosh et al., 2017) as baselines (BL) to extract the initial list of word hypotheses.. We experimented extracting kbest hypotheses for k = 1 . . . 10.. Table 1 presents four different accuracy metrics for this case: 1) full columns correspond to the accuracy on the whole dataset. 2) dict columns correspond to the accuracy over the cases where the target word is among the 90K words of the CNN dictionary (which correspond to 43.3% of the whole dataset. 3) list columns report the accuracy over the cases where the right word was among the k-best produced by the baseline. 4) MRR Mean Reciprocal Rank (MRR),. We compare the results of our encoder with several stateof-the-art sentence encoders, tuned or trained on the same dataset.. Table 1 are trained in the same conditions that our model with glove initialization with dual-channel overlapping non-static pre-trained embedding on the same dataset.. Our model FDCLSTM without attention achieves a better result in the case of the second baseline LSTM that full of false-positives and short words.. We also compare our result with current state-of-the-art word embeddings trained on a large general text using glove and fasttext. The word model used only object and place information, and ignored the caption. Our proposed models achieve better performance than our TWE previous model (Sabir et al., 2018), that trained a word embedding (Mikolov et al., 2013) from scratch on the same task.. As seen in Table 1, the introduction of this unigram lexicon produces the best results." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Best results after re-ranking using different re-ranker, and different values for k-best hypotheses extracted from the baseline output (%). In addition, to evaluate our re-ranker with MRR we fixed k CNNk=8 LSTMk=4'\nProblem: 'Which model achieved the highest Mean Reciprocal Rank (MRR) for the LSTM baseline?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/7.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Best results after re-ranking using different re-ranker, and different values for k-best hypotheses extracted from the baseline output (%). In addition, to evaluate our re-ranker with MRR we fixed k CNNk=8 LSTMk=4" ], "reasoning": "To find the model with the highest MRR for the LSTM baseline, we look at the 'LSTM MRR' column and identify the highest value, which is 81.8.", "question": "Which model achieved the highest Mean Reciprocal Rank (MRR) for the LSTM baseline?", "answer": { "answer": "BL+FDCLSTM AT (ours)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BL+FDCLSTM AT (ours) model achieved the highest accuracy in the CNN full metric.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/7.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Best results after re-ranking using different re-ranker, and different values for k-best hypotheses extracted from the baseline output (%). In addition, to evaluate our re-ranker with MRR we fixed k CNNk=8 LSTMk=4" ], "reasoning": "To verify this statement, we look at the 'CNN full' column and compare the values. The highest value is 22.4, achieved by the BL+FDCLSTM AT (ours) model.", "statement": "The BL+FDCLSTM AT (ours) model achieved the highest accuracy in the CNN full metric.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Controlled experiments comparing SynST to a baseline Transformer, SAT, and LT on four different datasets (two language pairs) demonstrate speed and BLEU improvements. Wall-clock speedup is measured on a single Nvidia TitanX Pascal by computing the average time taken to decode a single sentence in the dev/test set, averaged over five runs. When beam width b is not specified, we perform greedy decoding (i.e., b=1). Note that the LT results are reported by latentTransformer and not from our own implementation; as such, they are not directly comparable to the other results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/8.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Controlled experiments comparing SynST to a baseline Transformer, SAT, and LT on four different datasets (two language pairs) demonstrate speed and BLEU improvements. Wall-clock speedup is measured on a single Nvidia TitanX Pascal by computing the average time taken to decode a single sentence in the dev/test set, averaged over five runs. When beam width b is not specified, we perform greedy decoding (i.e., b=1). Note that the LT results are reported by latentTransformer and not from our own implementation; as such, they are not directly comparable to the other results." ], "reasoning": "SynST achieves significant speedups and outperforms SAT configurations in quality on all datasets, with a notable improvement of 1 BLEU over LT on WMT En-De.", "description": "Table 1 contains the results on all four datasets. SynST achieves speedups of ∼ 4 − 5× that of the vanilla Transformer, which is larger than nearly all. of the SAT configurations. Quality-wise, SynST again significantly outperforms the SAT configurations at comparable speedups on all datasets. On WMT En-De, SynST improves by 1 BLEU over LT (20.74 vs LT's 19.8 without reranking)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Controlled experiments comparing SynST to a baseline Transformer, SAT, and LT on four different datasets (two language pairs) demonstrate speed and BLEU improvements. Wall-clock speedup is measured on a single Nvidia TitanX Pascal by computing the average time taken to decode a single sentence in the dev/test set, averaged over five runs. When beam width b is not specified, we perform greedy decoding (i.e., b=1). Note that the LT results are reported by latentTransformer and not from our own implementation; as such, they are not directly comparable to the other results.'\nProblem: 'What is the BLEU score and speedup of SynST on the WMT En-Fr dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/8.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Controlled experiments comparing SynST to a baseline Transformer, SAT, and LT on four different datasets (two language pairs) demonstrate speed and BLEU improvements. Wall-clock speedup is measured on a single Nvidia TitanX Pascal by computing the average time taken to decode a single sentence in the dev/test set, averaged over five runs. When beam width b is not specified, we perform greedy decoding (i.e., b=1). Note that the LT results are reported by latentTransformer and not from our own implementation; as such, they are not directly comparable to the other results." ], "reasoning": "To find the BLEU score and speedup of SynST on the WMT En-Fr dataset, locate the SynST row and the WMT En-Fr BLEU and Speedup columns. The BLEU score is 33.47 and the speedup is 5.32×.", "question": "What is the BLEU score on the WMT En-Fr dataset?", "answer": { "answer": "33.47" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSynST has a higher BLEU score than LT on the WMT En-De dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/8.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Controlled experiments comparing SynST to a baseline Transformer, SAT, and LT on four different datasets (two language pairs) demonstrate speed and BLEU improvements. Wall-clock speedup is measured on a single Nvidia TitanX Pascal by computing the average time taken to decode a single sentence in the dev/test set, averaged over five runs. When beam width b is not specified, we perform greedy decoding (i.e., b=1). Note that the LT results are reported by latentTransformer and not from our own implementation; as such, they are not directly comparable to the other results." ], "reasoning": "The table shows that SynST has a BLEU score of 20.74 on the WMT En-De dataset, while LT has a BLEU score of 19.8, indicating that SynST has a higher BLEU score.", "statement": "SynST has a higher BLEU score than LT on the WMT En-De dataset.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Movie review Dev results of S-LSTM'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/9.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Movie review Dev results of S-LSTM" ], "reasoning": "Increasing the hidden layer size from 100 to 300 improves S-LSTM accuracy, but further increases beyond 300 do not. Adding sentence-level nodes increases parameters and decoding time without improving accuracy.", "description": "Table 2 shows the development results of various S-LSTM settings,. Adding one additional sentence-level node. does not lead to accuracy improvements, although the number of parameters and decoding time increase accordingly.. The accuracies of S-LSTM increases as the hidden layer size for each node increases from 100 to 300, but does not further increase when the size increases beyond 300. We fix the hidden size to 300 accordingly. Without using (cid:104)s(cid:105) and (cid:104)/s(cid:105), the performance of S-LSTM drops from 82.64% to 82.36%, showing the effectiveness of having these additional nodes." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Movie review Dev results of S-LSTM'\nProblem: 'What is the accuracy of the S-LSTM model with a hidden size of 300 and how does it compare to the model with a hidden size of 900?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/9.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Movie review Dev results of S-LSTM" ], "reasoning": "The accuracy of the S-LSTM model with a hidden size of 300 is 82.64%, while the accuracy of the model with a hidden size of 900 is 81.66%. The model with a hidden size of 300 has a higher accuracy.", "question": "What is the accuracy of the S-LSTM model with a hidden size of 300?", "answer": { "answer": "82.64" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe S-LSTM model with a hidden size of 600 has the highest accuracy.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/9.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Movie review Dev results of S-LSTM" ], "reasoning": "The table shows that the S-LSTM model with a hidden size of 600 has an accuracy of 81.84%, which is not the highest. The highest accuracy is 82.64% for the model with a hidden size of 300.", "statement": "The S-LSTM model with a hidden size of 600 has the highest accuracy.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Movie review development results'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/10.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Movie review development results" ], "reasoning": "BiLSTM models generally outperform uni-directional LSTM in accuracy, with training time increasing with more layers. S-LSTM achieves better results than 2-layer BiLSTM with fewer parameters and shorter time.", "description": "As shown in Table 3, BiLSTM gives significantly better accuracies compared to uni-directional LSTM2, with the training time per epoch growing from 67 seconds to 106 seconds. Stacking 2 layers of BiLSTM gives further improvements to development results, with a larger time of 207 seconds. 3 layers of stacked BiLSTM does not further improve the results. In contrast, S-LSTM gives a development result of 82.64%, which is significantly better compared to 2-layer stacked BiLSTM, with a smaller number of model parameters and a shorter time of 65 seconds.. We additionally make comparisons with stacked CNNs and hierarchical attention (Vaswani et al., 2017), shown in Table 3 (the CNN and Transformer rows),. CNN is the most efficient among all models compared, with the smallest model size. On the other hand, a 3-layer stacked CNN gives an accuracy of 81.46%, which is also. the lowest compared with BiLSTM, hierarchical attention and S-LSTM. The best performance of hierarchical attention is between single-layer and two-layer BiLSTMs in terms of both accuracy and efficiency. S-LSTM gives significantly better accuracies compared with both CNN and hierarchical attention.. Table 3 additionally shows the results of BiLSTM and S-LSTM when external attention is used. Attention leads to improved accuracies for both BiLSTM and S-LSTM in classification, with S-LSTM still outperforming BiLSTM significantly." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Movie review development results'\nProblem: 'Which model achieves the highest accuracy with the shortest training time?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/10.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Movie review development results" ], "reasoning": "From the table, S-LSTM+Attention achieves the highest accuracy of 83.07% with a training time of 87 seconds, which is shorter than other models with similar or higher accuracies.", "question": "Which model achieves the highest accuracy with the shortest training time?", "answer": { "answer": "S-LSTM+Attention" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 3-layer stacked CNN has the lowest accuracy among all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/10.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Movie review development results" ], "reasoning": "The table shows that the 3-layer stacked CNN has an accuracy of 81.46%, which is not the lowest. The lowest accuracy is 80.35% achieved by the single-layer CNN.", "statement": "The 3-layer stacked CNN has the lowest accuracy among all models.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Test set results on movie review dataset (* denotes significance in all tables).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/11.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Test set results on movie review dataset (* denotes significance in all tables)." ], "reasoning": "S-LSTM outperforms BiLSTM in terms of accuracy and speed, and provides competitive results compared to other existing methods.", "description": "The final results on the movie review and rich text classification datasets are shown in Tables 4 and 5, respectively.. As shown in Table 4,. S-LSTM outperforms BiLSTM significantly, with a faster speed.. S-LSTM also gives highly competitive results when compared with existing methods in the literature." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Test set results on movie review dataset (* denotes significance in all tables).'\nProblem: 'Which model has the highest accuracy and how does its training speed compare to BiLSTM?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/11.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Test set results on movie review dataset (* denotes significance in all tables)." ], "reasoning": "The model with the highest accuracy is S-LSTM with 82.45%.", "question": "Which model has the highest accuracy?", "answer": { "answer": "S-LSTM" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nS-LSTM has the fastest training speed among all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/11.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Test set results on movie review dataset (* denotes significance in all tables)." ], "reasoning": "The table shows that S-LSTM has a training time of 41 seconds, which is faster than all other models listed with training times.", "statement": "S-LSTM has the fastest training speed among all models.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results on the 16 datasets of Liu et al. (2017). Time format: train (test)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/12.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on the 16 datasets of Liu et al. (2017). Time format: train (test)" ], "reasoning": "S-LSTM achieves the highest average accuracy of 85.38% across 16 datasets, outperforming 2-layer BiLSTM's 84.9% and 3-layer BiLSTM's 84.57%.", "description": "The final results on the movie review and rich text classification datasets are shown in Tables 4 and 5, respectively.. As shown in Table 5, among the 16 datasets. S-LSTM gives the best results on 12, compared with BiLSTM and 2 layered BiLSTM models. The average accuracy of S-LSTM is 85.6%, significantly higher compared with 84.9% by 2-layer stacked BiLSTM. 3-layer stacked BiLSTM gives an average accuracy of 84.57%, which is lower compared to a 2-layer stacked BiLSTM, with a training time per epoch of 423.6 seconds." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Results on the 16 datasets of Liu et al. (2017). Time format: train (test)'\nProblem: 'Which model has the highest average accuracy across the datasets?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/12.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on the 16 datasets of Liu et al. (2017). Time format: train (test)" ], "reasoning": "The table shows the average accuracy for each model. S-LSTM has an average accuracy of 85.38%, which is higher than the other models.", "question": "Which model has the highest average accuracy across the datasets?", "answer": { "answer": "S-LSTM" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 3-layer BiLSTM model has a higher average accuracy than the 2-layer BiLSTM model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/12.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on the 16 datasets of Liu et al. (2017). Time format: train (test)" ], "reasoning": "The table shows that the 3-layer BiLSTM model has an average accuracy of 84.57%, while the 2-layer BiLSTM model has an average accuracy of 84.9%. Therefore, the statement is refuted.", "statement": "The 3-layer BiLSTM model has a higher average accuracy than the 2-layer BiLSTM model.", "answer": { "answer": "not enough info" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Results on PTB (POS tagging)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/13.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results on PTB (POS tagging)" ], "reasoning": "S-LSTM achieves better results than BiLSTM on the WSJ dataset and is competitive with existing methods. Adding more layers to BiLSTM does not improve accuracy beyond two layers.", "description": "As can be seen in Table 6, S-LSTM gives significantly better results compared with BiLSTM on the WSJ dataset. It also gives competitive accuracies as compared with existing methods in the literature. Stacking two layers of BiLSTMs leads to improved results compared to one-layer BiLSTM, but the accuracy does not further improve. with three layers of stacked LSTMs." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Results on PTB (POS tagging)'\nProblem: 'Which model has the lowest test time while achieving an accuracy of 97.55%?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/13.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results on PTB (POS tagging)" ], "reasoning": "From the table, we see that S-LSTM has an accuracy of 97.55% and a test time of 22.16 seconds, which is the lowest among models with the same accuracy.", "question": "Which model has the lowest test time while achieving an accuracy of 97.55%?", "answer": "So, the answer is {\"answer\": \"S-LSTM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BiLSTM model has a higher accuracy than the S-LSTM model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/13.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results on PTB (POS tagging)" ], "reasoning": "The table shows that BiLSTM has an accuracy of 97.35%, while S-LSTM has an accuracy of 97.55%. Therefore, the statement is incorrect.", "statement": "The BiLSTM model has a higher accuracy than the S-LSTM model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Results on CoNLL03 (NER)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/14.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results on CoNLL03 (NER)" ], "reasoning": "S-LSTM achieves an F1-score of 91.57% on the CoNLL test set, outperforming BiLSTMs, which improve slightly with more layers.", "description": "For NER (Table 7), S-LSTM gives an F1-score of 91.57% on the CoNLL test set, which is significantly better compared with BiLSTMs. Stacking more layers of BiLSTMs leads to slightly better F1-scores compared with a single-layer BiLSTM. Our BiLSTM results are comparable to the results reported by Ma and Hovy (2016) and Lample et al. (2016),. In contrast, S-LSTM gives the best reported results under the same settings. In the second section of Table 7, Yang et al. (2017) obtain an Fscore of 91.26%" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Results on CoNLL03 (NER)'\nProblem: 'Which model achieves the highest F1-score according to Table 7?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/14.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results on CoNLL03 (NER)" ], "reasoning": "The table shows various models and their F1-scores. The highest F1-score listed is 91.93%, achieved by peters2017semi.", "question": "Which model achieves the highest F1-score according to Table 7?", "answer": { "answer": "peters2017semi" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nS-LSTM achieves the highest F1-score among all models listed in Table 7.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/14.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results on CoNLL03 (NER)" ], "reasoning": "The table shows that S-LSTM has an F1-score of 91.57%, while peters2017semi has a higher F1-score of 91.93%. Therefore, the statement is incorrect.", "statement": "S-LSTM achieves the highest F1-score among all models listed in Table.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: E2E test set results. Own results correspond to avg±SD of ten runs and single result of best models on the development set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/15.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: E2E test set results. Own results correspond to avg±SD of ten runs and single result of best models on the development set." ], "reasoning": "Our word-based models achieve higher BLEU and ROUGE-L scores compared to character-based models on the E2E test set.", "description": "Table 2 and 3 display the results on the E2E and WebNLG test sets for models of the respective challenges and our own models. On the E2E test set, our single best word- and character-based models reach comparable results to the best challenge submissions. The word-based models achieve significantly higher BLEU and ROUGE-L scores than the character-based models." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: E2E test set results. Own results correspond to avg±SD of ten runs and single result of best models on the development set.'\nProblem: 'Which model achieved the highest ROUGE-L score in the challenge?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/15.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: E2E test set results. Own results correspond to avg±SD of ten runs and single result of best models on the development set." ], "reasoning": "By examining the ROUGE-L scores in the challenge column, the HarvardNLP & H. Elder model achieved the highest score of 70.8.", "question": "Which model achieved the highest ROUGE-L score in the challenge?", "answer": { "answer": "HarvardNLP & H. Elder" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe baseline model achieved the highest BLEU score in the challenge.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/15.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: E2E test set results. Own results correspond to avg±SD of ten runs and single result of best models on the development set." ], "reasoning": "The table shows that the baseline model has a BLEU score of 65.9, which is lower than the Thomson Reuters (np 3) model with a BLEU score of 68.1.", "statement": "The baseline model achieved the highest BLEU score in the challenge.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: WebNLG test set results. Own results correspond to single best model on development set and avg±SD of ten runs.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/16.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: WebNLG test set results. Own results correspond to single best model on development set and avg±SD of ten runs." ], "reasoning": "The word-based model outperforms the best challenge submission in BLEU score by a small margin, while the character-based model achieves a significantly higher ROUGE-L score than the word-based model.", "description": "On the WebNLG test set, the BLEU score of our best word-based model outperforms the best challenge submission by a small margin. The character-based model achieves a significantly higher ROUGE-L score than the wordbased model, whereas the BLEU score difference is not significant." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: WebNLG test set results. Own results correspond to single best model on development set and avg±SD of ten runs.'\nProblem: 'Which model achieves the highest ROUGE-L score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/16.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: WebNLG test set results. Own results correspond to single best model on development set and avg±SD of ten runs." ], "reasoning": "The table shows that the Melbourne system achieves the highest ROUGE-L score of 61.0.", "question": "Which model achieves the highest ROUGE-L score?", "answer": { "answer": "Melbourne" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe word-based model achieves a higher BLEU score than the Tilburg-SMT system.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/16.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: WebNLG test set results. Own results correspond to single best model on development set and avg±SD of ten runs." ], "reasoning": "The table shows that the word-based model has a BLEU score of 44.2, which is higher than the Tilburg-SMT system's BLEU score of 43.1.", "statement": "The word-based model achieves a higher BLEU score than the Tilburg-SMT system.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Increasing the number of layers in SynST’s parse decoder significantly lowers the speedup while marginally impacting BLEU. Randomly sampling k from {1…6} during training boosts BLEU significantly with minimal impact on speedup.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/17.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Increasing the number of layers in SynST’s parse decoder significantly lowers the speedup while marginally impacting BLEU. Randomly sampling k from {1…6} during training boosts BLEU significantly with minimal impact on speedup." ], "reasoning": "Increasing the number of layers from 1 to 5 results in a small BLEU increase of 0.5, while the speedup significantly drops from 3.8× to 1.4×. Exposing the parse decoder to multiple chunkings improves BLEU by 1.5 and decreases speedup from 3.8× to 3.1×.", "description": "Table 4 shows that increasing the number of layers from 1 to 5 results in a BLEU increase of only 0.5, while the speedup drops from 3.8× to 1.4×.. The final row of Table 4 shows that exposing the parse decoder to multiple possible chunkings of the same sentence during training allows it to choose a sequence of chunks that has a higher likelihood at test time, improving BLEU by 1.5 while decreasing the speedup from 3.8× to 3.1×;" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Increasing the number of layers in SynST’s parse decoder significantly lowers the speedup while marginally impacting BLEU. Randomly sampling k from {1…6} during training boosts BLEU significantly with minimal impact on speedup.'\nProblem: 'What is the BLEU score when the number of layers is increased from 1 to 5?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/17.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Increasing the number of layers in SynST’s parse decoder significantly lowers the speedup while marginally impacting BLEU. Randomly sampling k from {1…6} during training boosts BLEU significantly with minimal impact on speedup." ], "reasoning": "The BLEU score for 1 layer is 23.82, and for 5 layers it is 24.34. The increase in BLEU score is 24.34 - 23.82 = 0.52.", "question": "What is the BLEU score when the number of layers is increased from 1 to 5?", "answer": "So, the answer is {\"answer\": \"24.34\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIncreasing the number of layers from 1 to 5 results in a BLEU increase of 0.5.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/17.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Increasing the number of layers in SynST’s parse decoder significantly lowers the speedup while marginally impacting BLEU. Randomly sampling k from {1…6} during training boosts BLEU significantly with minimal impact on speedup." ], "reasoning": "The BLEU score for 1 layer is 23.82, and for 5 layers it is 24.34. The increase in BLEU score is 24.34 - 23.82 = 0.52, which is approximately 0.5.", "statement": "Increasing the number of layers from 1 to 5 results in a BLEU increase of 0.5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: E2E and WebNLG development set results in the format avg±SD. Human results are averaged over using each human reference as prediction once.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/18.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: E2E and WebNLG development set results in the format avg±SD. Human results are averaged over using each human reference as prediction once." ], "reasoning": "Both word-based and character-based models outperform human texts on the E2E development set in BLEU and ROUGE-L scores, while human BLEU scores are higher on the WebNLG development set.", "description": "Table 4 shows the BLEU and ROUGE-L development set scores when treating each human reference as prediction once and evaluating it against the remaining references, compared to the scores of the word-based and. character-based models. Strikingly, on the E2E development set, both model variants significantly outperform human texts by far with respect to both automatic evaluation measures. While the human BLEU score is significantly higher than those of both systems on the WebNLG development set, there is no statistical difference between human and system ROUGE-L scores." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: E2E and WebNLG development set results in the format avg±SD. Human results are averaged over using each human reference as prediction once.'\nProblem: 'Which model has the highest BLEU score on the E2E development set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/18.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: E2E and WebNLG development set results in the format avg±SD. Human results are averaged over using each human reference as prediction once." ], "reasoning": "The table shows the BLEU scores for human, word-based, and character-based models on the E2E development set. The word-based model has the highest BLEU score of 68.2±1.4.", "question": "Which model has the highest BLEU score on the E2E development set?", "answer": "So, the answer is {\"answer\": \"word-based model\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe human ROUGE-L score is higher than the word-based model on the WebNLG development set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/18.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: E2E and WebNLG development set results in the format avg±SD. Human results are averaged over using each human reference as prediction once." ], "reasoning": "The table shows that the human ROUGE-L score on the WebNLG development set is 62.4±0.3, while the word-based model's ROUGE-L score is 58.5±3.0. Therefore, the statement is supported.", "statement": "The human ROUGE-L score is higher than the word-based model on the WebNLG development set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Percentage of affected instances in manual error analysis of 15 randomly selected development set instances for each input length.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/19.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Percentage of affected instances in manual error analysis of 15 randomly selected development set instances for each input length." ], "reasoning": "The WebNLG dataset is more challenging than the E2E dataset, with more frequent content errors and spelling mistakes, while character-based models perform better linguistically but worse in content accuracy.", "description": "One annotator (one of the authors of this paper) manually assessed the outputs of the models that obtained the best development set BLEU score as summarized in Table 56. As we can see from the bottom part of the table, all models struggle more with getting the content right than with producing linguistically correct texts; 70-80% of the texts generated by all models are completely correct linguistically.. Comparing the two datasets, we again observe that the WebNLG dataset is much more challenging than the E2E dataset, especially with respect to correctly verbalizing the content.. Moreover, spelling mistakes only appeared in WebNLG texts, mainly concerning omissions of accents or umlauts.. The most frequent content error in both datasets concerns omission of information.. Information addition and repetition only occur in the WebNLG dataset. The latter is an especially frequent problem of the character-based model, affecting more than a quarter of all texts.. In comparison, character-based models reproduce the content more faithfully on the E2E dataset while offering the same level of linguistic quality as word-based models, leading to more correct outputs overall. On the WebNLG dataset, the word-based model is more faithful to the inputs, probably because of the effective delexicalization strategy, whereas the character-based model errs less on the linguistic side. Overall, the word-based model yields more correct texts, stressing the importance of delexicalization and data normalization in low resource settings." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Percentage of affected instances in manual error analysis of 15 randomly selected development set instances for each input length.'\nProblem: 'Which model and dataset combination has the highest percentage of linguistically correct texts?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/19.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Percentage of affected instances in manual error analysis of 15 randomly selected development set instances for each input length." ], "reasoning": "By comparing the 'language correct' percentages across all model and dataset combinations, the WebNLG character-based model has the highest percentage at 79%.", "question": "Which model and dataset combination has the highest percentage of linguistically correct texts?", "answer": { "answer": "WebNLG character-based model" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe E2E word-based model has fewer content errors than the WebNLG word-based model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/19.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Percentage of affected instances in manual error analysis of 15 randomly selected development set instances for each input length." ], "reasoning": "The table shows that the E2E word-based model has 40.0% content errors dropped, while the WebNLG word-based model has 42.9% content errors dropped, indicating that the E2E word-based model has fewer content errors.", "statement": "The E2E word-based model has fewer content errors than the WebNLG word-based model.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Linguistic diversity of development set references and generated texts as avg±SD. ‘% new’ denotes the share of generated texts or sentences that do not appear in training references. Higher indicates more diversity for all measures.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/20.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Linguistic diversity of development set references and generated texts as avg±SD. ‘% new’ denotes the share of generated texts or sentences that do not appear in training references. Higher indicates more diversity for all measures." ], "reasoning": "The character-based models generate more unique sentences and copy less from the training data compared to word-based models, which copy about 40% of their generated sentences from the training data.", "description": "Table 6 shows automatically computed statistics on the diversity of the generated texts of both models and human texts and on the overlap of the (generated) texts with the training set.. On both datasets, our systems produce significantly less varied outputs and reproduce more. texts and sentences from the training data than the human texts. Interestingly, however, the characterbased models generate significantly more unique sentences and copy significantly less from the training data than the word-based models, which copy about 40% of their generated sentences from the training data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Linguistic diversity of development set references and generated texts as avg±SD. ‘% new’ denotes the share of generated texts or sentences that do not appear in training references. Higher indicates more diversity for all measures.'\nProblem: 'Which model generates the highest percentage of new texts on the WebNLG dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/20.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Linguistic diversity of development set references and generated texts as avg±SD. ‘% new’ denotes the share of generated texts or sentences that do not appear in training references. Higher indicates more diversity for all measures." ], "reasoning": "By comparing the '% new texts' row for the WebNLG dataset, the WebNLG human model generates 98.8% new texts, which is the highest among the models.", "question": "Which model generates the highest percentage of new texts on the WebNLG dataset?", "answer": { "answer": "E2E character" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe E2E word model generates more unique sentences than the E2E character model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/20.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Linguistic diversity of development set references and generated texts as avg±SD. ‘% new’ denotes the share of generated texts or sentences that do not appear in training references. Higher indicates more diversity for all measures." ], "reasoning": "The table shows that the E2E word model generates 203.5 unique sentences, while the E2E character model generates 366.8 unique sentences. Therefore, the statement is refuted.", "statement": "The E2E word model generates more unique sentences than the E2E character model.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Manual evaluation of generated texts for 10 random test instances of a word-based model trained with synthetic training data from two templates. c@n: avg. number of correct texts (with respect to content and language) among the top n hypotheses.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/21.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Manual evaluation of generated texts for 10 random test instances of a word-based model trained with synthetic training data from two templates. c@n: avg. number of correct texts (with respect to content and language) among the top n hypotheses." ], "reasoning": "Models trained with data from both templates show improved performance, especially when a reranker is applied, resulting in almost three correct variants among the top five hypotheses on average.", "description": "Table 7 shows our manual evaluation of the top 30 hypotheses for 10 random E2E test inputs generated by models trained with data synthesized from the two templates. As is evident from the first two rows, all models learned to generalize from the training data to produce correct texts for novel inputs consisting of unseen combinations of input attributes.. Yet, the picture is a bit different for the model trained on data generated by both templates. While the top two hypotheses are equally distributed between adhering to Template 1 and Template 2, more than 5% among the lower-ranked hypotheses constitute a template combination such as the example shown in the bottom part of Figure 2.. As can be seen in the final row of Table 7, this simple reranker successfully places correct hypotheses higher up in the ranking, improving the practical usability of the generation model by now offering almost three correct variants for each input among the top five hypotheses on average." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Manual evaluation of generated texts for 10 random test instances of a word-based model trained with synthetic training data from two templates. c@n: avg. number of correct texts (with respect to content and language) among the top n hypotheses.'\nProblem: 'How does the reranker affect the average number of correct hypotheses in the top 5 for models trained with both templates?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/21.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Manual evaluation of generated texts for 10 random test instances of a word-based model trained with synthetic training data from two templates. c@n: avg. number of correct texts (with respect to content and language) among the top n hypotheses." ], "reasoning": "The table shows that the model trained with both templates has an average of 2.2 correct hypotheses in the top 5, which increases to 2.7 when the reranker is applied.", "question": "How does the reranker affect the average number of correct hypotheses in the top 5 for models trained with both templates?", "answer": { "answer": "2.7" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe reranker improves the average number of correct hypotheses in the top 5 for models trained with both templates.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/21.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Manual evaluation of generated texts for 10 random test instances of a word-based model trained with synthetic training data from two templates. c@n: avg. number of correct texts (with respect to content and language) among the top n hypotheses." ], "reasoning": "The table indicates that the average number of correct hypotheses in the top 5 increases from 2.2 to 2.7 when the reranker is applied to models trained with both templates.", "statement": "The reranker improves the average number of correct hypotheses in the top 5 for models trained with both templates.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Experimental results of abstractive summarization on Gigaword test set with ROUGE metric. The top section is prefix baselines, the second section is recent unsupervised methods and ours, the third section is state-of-the-art supervised method along with our implementation of a seq-to-seq model with attention, and the bottom section is our model’s oracle performance. Wang and Lee (2018) is by author correspondence (scores differ because of evaluation setup). For another unsupervised work Fevry and Phang (2018), we attempted to replicate on our test set, but were unable to obtain results better than the baselines.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/22.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Experimental results of abstractive summarization on Gigaword test set with ROUGE metric. The top section is prefix baselines, the second section is recent unsupervised methods and ours, the third section is state-of-the-art supervised method along with our implementation of a seq-to-seq model with attention, and the bottom section is our model’s oracle performance. Wang and Lee (2018) is by author correspondence (scores differ because of evaluation setup). For another unsupervised work Fevry and Phang (2018), we attempted to replicate on our test set, but were unable to obtain results better than the baselines." ], "reasoning": "Our method outperforms commonly used prefix baselines and achieves comparable results to Wang and Lee (2018), with much higher oracle scores indicating better quality summaries.", "description": "The automatic evaluation scores are presented in Table 1 and Table 2.. Our method outperforms commonly used prefix baselines for this task which take the first 75 characters or 8 words of the source as a summary. Our system achieves comparable results to Wang and Lee (2018) a system based on both GANs and reinforcement training.. In Table 1, we also list scores of the stateof-the-art supervised model, an attention based. seq-to-seq model of our own implementation, as well as the oracle scores of our method obtained by choosing the best summary among all finished hypothesis from beam search.. The oracle scores are much higher, indicating that our unsupervised method does allow summaries of better quality," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Experimental results of abstractive summarization on Gigaword test set with ROUGE metric. The top section is prefix baselines, the second section is recent unsupervised methods and ours, the third section is state-of-the-art supervised method along with our implementation of a seq-to-seq model with attention, and the bottom section is our model’s oracle performance. Wang and Lee (2018) is by author correspondence (scores differ because of evaluation setup). For another unsupervised work Fevry and Phang (2018), we attempted to replicate on our test set, but were unable to obtain results better than the baselines.'\nProblem: 'Which model has the highest R2 score and what is the value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/22.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Experimental results of abstractive summarization on Gigaword test set with ROUGE metric. The top section is prefix baselines, the second section is recent unsupervised methods and ours, the third section is state-of-the-art supervised method along with our implementation of a seq-to-seq model with attention, and the bottom section is our model’s oracle performance. Wang and Lee (2018) is by author correspondence (scores differ because of evaluation setup). For another unsupervised work Fevry and Phang (2018), we attempted to replicate on our test set, but were unable to obtain results better than the baselines." ], "reasoning": "To find the model with the highest R2 score, compare the R2 values of all models listed in the table. Cao et al. (2018) has the highest R2 score with a value of 19.03.", "question": "Which model has the highest R2 score?", "answer": { "answer": "Cao et al. (2018)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe seq2seq model has a higher RL score than the Contextual Match model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/22.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Experimental results of abstractive summarization on Gigaword test set with ROUGE metric. The top section is prefix baselines, the second section is recent unsupervised methods and ours, the third section is state-of-the-art supervised method along with our implementation of a seq-to-seq model with attention, and the bottom section is our model’s oracle performance. Wang and Lee (2018) is by author correspondence (scores differ because of evaluation setup). For another unsupervised work Fevry and Phang (2018), we attempted to replicate on our test set, but were unable to obtain results better than the baselines." ], "reasoning": "The table shows that the seq2seq model has an RL score of 31.44, while the Contextual Match model has an RL score of 24.41. Therefore, the seq2seq model has a higher RL score.", "statement": "The seq2seq model has a higher RL score than the Contextual Match model.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Experimental results of extractive summarization on Google data set. F1 is the token overlapping score, and CR is the compression rate. F&A is an unsupervised baseline used in Filippova and Altun (2013), and the middle section is supervised results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/23.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experimental results of extractive summarization on Google data set. F1 is the token overlapping score, and CR is the compression rate. F&A is an unsupervised baseline used in Filippova and Altun (2013), and the middle section is supervised results." ], "reasoning": "Our method achieves a good compression rate and significantly improves the token-level F1 score over the previous unsupervised baseline.", "description": "For extractive sentence summarization, our method achieves good compression rate and significantly raises a previous unsupervised baseline on token level F1 score." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Experimental results of extractive summarization on Google data set. F1 is the token overlapping score, and CR is the compression rate. F&A is an unsupervised baseline used in Filippova and Altun (2013), and the middle section is supervised results.'\nProblem: 'Which model has the highest F1 score and what is its compression rate?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/23.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experimental results of extractive summarization on Google data set. F1 is the token overlapping score, and CR is the compression rate. F&A is an unsupervised baseline used in Filippova and Altun (2013), and the middle section is supervised results." ], "reasoning": "By examining the table, the model with the highest F1 score is Zhao et al. (2018) with an F1 score of 85.1. The compression rate (CR) for this model is 0.39.", "question": "Which model has the highest F1 score?", "answer": { "answer": "85.1" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe F&A Unsupervised model has the lowest F1 score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/23.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experimental results of extractive summarization on Google data set. F1 is the token overlapping score, and CR is the compression rate. F&A is an unsupervised baseline used in Filippova and Altun (2013), and the middle section is supervised results." ], "reasoning": "The table shows that the F&A Unsupervised model has an F1 score of 52.3, which is the lowest among all the models listed.", "statement": "The F&A Unsupervised model has the lowest F1 score.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison of different model choices. The top section evaluates the effects of contextual representation in the matching model, and the bottom section evaluates the effects of different smoothing methods in the fluency model.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/24.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of different model choices. The top section evaluates the effects of contextual representation in the matching model, and the bottom section evaluates the effects of different smoothing methods in the fluency model." ], "reasoning": "The cluster smoothing (CS) method shows effectiveness in vocabulary adaptive language models, while temperature smoothing (TEMP) is an option for abstractive datasets. Using word embeddings from the bottom layer of ELMo in contextual matching models significantly decreases performance.", "description": "Table 3 considers analysis of different aspects of the model.. First, we look at the. fluency model and compare the cluster smoothing (CS) approach with softmax temperature (TEMPx with x being the temperature) commonly used for generation in LM-integrated models (Chorowski and Jaitly, 2016) as well as no adjustment (NA). Second, we vary the 3-layer representation out of ELMo forward language model to do contextual matching (bot/mid/top: bottom/middle/top layer only, avg: average of 3 layers, cat: concatenation of all layers).. Results show the effectiveness of our cluster smoothing method for the vocabulary adaptive language model pfm, although temperature smoothing is an option for abstractive datasets.. When using word embeddings (bottom layer only from ELMo language model) in our contextual matching model pcm, the summarization performance drops significantly to below simple baselines as demonstrated by score decrease." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Comparison of different model choices. The top section evaluates the effects of contextual representation in the matching model, and the bottom section evaluates the effects of different smoothing methods in the fluency model.'\nProblem: 'Which model and approach combination achieved the highest abstractive R1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/24.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of different model choices. The top section evaluates the effects of contextual representation in the matching model, and the bottom section evaluates the effects of different smoothing methods in the fluency model." ], "reasoning": "To find the highest abstractive R1 score, we need to compare the values in the 'abstractive R1' column. The highest score is 26.48, achieved by the 'CS + cat' model.", "question": "Which model and approach combination achieved the highest abstractive R1 score?", "answer": "So, the answer is {\"answer\": \"CS + cat\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'TEMP10 + cat' model achieved the highest extractive CR score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/24.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of different model choices. The top section evaluates the effects of contextual representation in the matching model, and the bottom section evaluates the effects of different smoothing methods in the fluency model." ], "reasoning": "To verify this statement, we need to compare the values in the 'extractive CR' column. The highest score is 0.47, achieved by the 'TEMP10 + cat' model.", "statement": "The 'TEMP10 + cat' model achieved the highest extractive CR score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Official evaluation results of the submitted runs on the test set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/25.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Official evaluation results of the submitted runs on the test set." ], "reasoning": "The final submission includes the 1st and 2nd best performing models on the development datasets and the majority vote of 5 models.", "description": "We select the 1st and 2nd best performing models on the development datasets as well as the majority vote (mv) of 5 models for the final submission. The final results are shown in Table 3." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Official evaluation results of the submitted runs on the test set.'\nProblem: 'What is the highest classification score for sub-task 1.2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/25.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Official evaluation results of the submitted runs on the test set." ], "reasoning": "To find the highest classification score for sub-task 1.2, compare the scores in the 1st Class., 2nd Class., and mv Class. columns. The highest score is 83.2.", "question": "What is the highest classification score for sub-task 1.2?", "answer": { "answer": "83.2" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe majority vote classification score for sub-task 2 is higher than the 2nd best performing model's classification score for sub-task 1.1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/25.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Official evaluation results of the submitted runs on the test set." ], "reasoning": "The majority vote classification score for sub-task 2 is 28.3, while the 2nd best performing model's classification score for sub-task 1.1 is 74.7. Since 28.3 is not higher than 74.7, the statement is refuted.", "statement": "The majority vote classification score for sub-task 2 is higher than the 2nd best performing model's classification score for sub-task 1.1.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: F1 and exact match comparisons of predicted chunk sequences (from the parse decoder), ground-truth chunk sequences (from an external parser in the target language), and chunk sequences obtained after parsing the translation produced by the token decoder. First two columns show the improvement obtained by jointly training the two decoders. The third column shows that when the token decoder deviates from the predicted chunk sequence, it usually results in a translation that is closer to the ground-truth target syntax, while the fourth column shows that the token decoder closely follows the predicted chunk sequence.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/26.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: F1 and exact match comparisons of predicted chunk sequences (from the parse decoder), ground-truth chunk sequences (from an external parser in the target language), and chunk sequences obtained after parsing the translation produced by the token decoder. First two columns show the improvement obtained by jointly training the two decoders. The third column shows that when the token decoder deviates from the predicted chunk sequence, it usually results in a translation that is closer to the ground-truth target syntax, while the fourth column shows that the token decoder closely follows the predicted chunk sequence." ], "reasoning": "Joint training of the parse and token decoders improves chunk F1 from 65.4 to 69.6, and the token decoder's reliance on generated chunk sequences is indicated by a high F1 of 89.9 and 43.1% exact match.", "description": "We evaluate two configurations of the parse decoder, one in which it is trained separately from the token decoder (first column of Table 3), and the other where both decoders are trained jointly (second column of Ta. ble 3). We observe that joint training boosts the chunk F1 from 65.4 to 69.6, although, in both cases the F1 scores are relatively low, which matches our intuition as most source sentences can be translated into multiple target syntactic forms.. To measure how often the token decoder follows the predicted chunk sequence, we parse the generated translation and compute the F1 between the resulting chunk sequence and the parse decoder's prediction (fourth column of Table 3). Strong results of 89.9 F1 and 43.1% exact match indicate that the token decoder is heavily reliant on the generated chunk sequences.. The resulting F1 is indeed almost 10 points higher (third column of Table 3), indicating that the token decoder does have the ability to correct mistakes.. In Section 5.3 (see the final row of Table 3) we consider the effect of randomly sampling the max chunk size k during training. This provides a considerable boost to BLEU with a minimal impact to speedup." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: F1 and exact match comparisons of predicted chunk sequences (from the parse decoder), ground-truth chunk sequences (from an external parser in the target language), and chunk sequences obtained after parsing the translation produced by the token decoder. First two columns show the improvement obtained by jointly training the two decoders. The third column shows that when the token decoder deviates from the predicted chunk sequence, it usually results in a translation that is closer to the ground-truth target syntax, while the fourth column shows that the token decoder closely follows the predicted chunk sequence.'\nProblem: 'What is the F1 score when the parse decoder is trained separately from the token decoder?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/26.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: F1 and exact match comparisons of predicted chunk sequences (from the parse decoder), ground-truth chunk sequences (from an external parser in the target language), and chunk sequences obtained after parsing the translation produced by the token decoder. First two columns show the improvement obtained by jointly training the two decoders. The third column shows that when the token decoder deviates from the predicted chunk sequence, it usually results in a translation that is closer to the ground-truth target syntax, while the fourth column shows that the token decoder closely follows the predicted chunk sequence." ], "reasoning": "The table shows the F1 score for the parse decoder trained separately from the token decoder in the first column, which is 65.48.", "question": "What is the F1 score when the parse decoder is trained separately from the token decoder?", "answer": { "answer": "65.48" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nJoint training of the parse and token decoders results in an F1 score of 69.64.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/26.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: F1 and exact match comparisons of predicted chunk sequences (from the parse decoder), ground-truth chunk sequences (from an external parser in the target language), and chunk sequences obtained after parsing the translation produced by the token decoder. First two columns show the improvement obtained by jointly training the two decoders. The third column shows that when the token decoder deviates from the predicted chunk sequence, it usually results in a translation that is closer to the ground-truth target syntax, while the fourth column shows that the token decoder closely follows the predicted chunk sequence." ], "reasoning": "The table shows that the F1 score for joint training of the parse and token decoders is 69.64, as indicated in the second column.", "statement": "Joint training of the parse and token decoders results in an F1 score of 69.64.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: The models here all have a bi-directional GRU as the encoder (dimensionality 300 in each direction). The default way of producing the representation is a concatenation of outputs from a global mean-pooling and a global max-pooling, while “⋅-MaxOnly” refers to the model with only global max-pooling. Bold numbers are the best results among all presented models. We found that 1) inputting correct words to an autoregressive decoder is not necessary; 2) predict-all-words decoders work roughly the same as autoregressive decoders; 3) mean+max pooling provides stronger transferability than the max-pooling alone does. The table supports our choice of the predict-all-words CNN decoder and the way of producing vector representations from the bi-directional RNN encoder.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/27.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The models here all have a bi-directional GRU as the encoder (dimensionality 300 in each direction). The default way of producing the representation is a concatenation of outputs from a global mean-pooling and a global max-pooling, while “⋅-MaxOnly” refers to the model with only global max-pooling. Bold numbers are the best results among all presented models. We found that 1) inputting correct words to an autoregressive decoder is not necessary; 2) predict-all-words decoders work roughly the same as autoregressive decoders; 3) mean+max pooling provides stronger transferability than the max-pooling alone does. The table supports our choice of the predict-all-words CNN decoder and the way of producing vector representations from the bi-directional RNN encoder." ], "reasoning": "The performance of different decoding settings (RNN and CNN) does not significantly differ on selected downstream tasks, and the mean+max pooling in the RNN-CNN model provides stronger transferability than max pooling alone.", "description": "The results are presented in Table 1 (top two subparts). As we can see, the three decoding settings do not differ significantly in terms of the performance on selected downstream tasks, with RNN or CNN as the decoder.. The results are also presented in Table 1 (3rd and 4th subparts). The performance of the predict-allwords RNN decoder does not significantly differ from that of any one of the autoregressive RNN de. coders, and the same situation can be also observed in CNN decoders.. In our proposed RNN-CNN model, we empirically show that the mean+max pooling provides stronger transferability than the max pooling alone does, and the results are presented in the last two sections of Table 1." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: The models here all have a bi-directional GRU as the encoder (dimensionality 300 in each direction). The default way of producing the representation is a concatenation of outputs from a global mean-pooling and a global max-pooling, while “⋅-MaxOnly” refers to the model with only global max-pooling. Bold numbers are the best results among all presented models. We found that 1) inputting correct words to an autoregressive decoder is not necessary; 2) predict-all-words decoders work roughly the same as autoregressive decoders; 3) mean+max pooling provides stronger transferability than the max-pooling alone does. The table supports our choice of the predict-all-words CNN decoder and the way of producing vector representations from the bi-directional RNN encoder.'\nProblem: 'Which decoder setting provides the highest performance on the TREC task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/27.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The models here all have a bi-directional GRU as the encoder (dimensionality 300 in each direction). The default way of producing the representation is a concatenation of outputs from a global mean-pooling and a global max-pooling, while “⋅-MaxOnly” refers to the model with only global max-pooling. Bold numbers are the best results among all presented models. We found that 1) inputting correct words to an autoregressive decoder is not necessary; 2) predict-all-words decoders work roughly the same as autoregressive decoders; 3) mean+max pooling provides stronger transferability than the max-pooling alone does. The table supports our choice of the predict-all-words CNN decoder and the way of producing vector representations from the bi-directional RNN encoder." ], "reasoning": "By examining the TREC column, the highest performance is achieved by the CNN decoder with a score of 90.2.", "question": "Which decoder setting provides the highest performance on the TREC task?", "answer": { "answer": "CNN" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe predict-all-words RNN decoder outperforms the autoregressive RNN decoder on the SST task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/27.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The models here all have a bi-directional GRU as the encoder (dimensionality 300 in each direction). The default way of producing the representation is a concatenation of outputs from a global mean-pooling and a global max-pooling, while “⋅-MaxOnly” refers to the model with only global max-pooling. Bold numbers are the best results among all presented models. We found that 1) inputting correct words to an autoregressive decoder is not necessary; 2) predict-all-words decoders work roughly the same as autoregressive decoders; 3) mean+max pooling provides stronger transferability than the max-pooling alone does. The table supports our choice of the predict-all-words CNN decoder and the way of producing vector representations from the bi-directional RNN encoder." ], "reasoning": "The table shows that the predict-all-words RNN decoder has a score of 81.6 on the SST task, while the autoregressive RNN decoder has a score of 82.5, indicating that the statement is false.", "statement": "The predict-all-words RNN decoder outperforms the autoregressive RNN decoder on the SST task.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Architecture Comparison. As shown in the table, our designed asymmetric RNN-CNN model (row 1,9, and 12) works better than other asymmetric models (CNN-LSTM, row 11), and models with symmetric structure (RNN-RNN, row 5 and 10).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/28.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Architecture Comparison. As shown in the table, our designed asymmetric RNN-CNN model (row 1,9, and 12) works better than other asymmetric models (CNN-LSTM, row 11), and models with symmetric structure (RNN-RNN, row 5 and 10)." ], "reasoning": "Decoding longer target sequences increases training time but does not significantly improve performance, while increasing the dimensionality of the RNN encoder improves performance with less additional training time compared to increasing CNN decoder complexity.", "description": "We present the Table 4 in the supplementary material and we summarise it as follows: 1. Decoding the next sentence performed similarly to decoding the subsequent contiguous words. 2. Decoding the subsequent 30 words, which was adopted from the skip-thought training code3, gave reasonably good performance. More words for decoding didn't give us a significant performance gain, and took longer to train. 3. Adding more layers into the decoder and enlarging the dimension of the convolutional layers indeed sightly improved the performance on the three downstream tasks, but as training efficiency is one of our main concerns, it wasn't worth sacrificing training efficiency for the minor performance gain. 4. Increasing the dimensionality of the RNN encoder improved the model performance, and the additional training time required was less than needed for increasing the complexity in the CNN decoder. We report results from both smallest and largest models in Table 2.. As the transferability of the models trained in both cases perform similarly on the evaluation tasks (see rows 1 and 2 in Table 4), we focus on the simpler predictall-words CNN decoder that learns to reconstruct the next window of contiguous words.. As stated in rows 1, 3, and 4 in Table 4, decoding short target sequences results in a slightly lower Pearson score on SICK, and decoding longer target sequences lead to a longer training time.. We tweaked the CNN encoder, including different kernel size and activation function, and we report the best results of CNNCNN model at row 6 in Table 4.. The future predictor in (Gan et al., 2017) also applies a CNN as the encoder, but the decoder is still an RNN, listed at row 11 in Table 4. Compared to our designed CNN-CNN model, their CNN-LSTM model contains more parameters than our model does, but they have similar performance on the evaluation tasks, which is also worse than our RNNCNN model.. Clearly, we can tell from the comparison between rows 1, 9 and 12 in Table 4, increasing the dimensionality of the RNN encoder leads to better transferability of the model.. Compared with the model with larger-size CNN decoder, apparently, we can see that larger encoder size helps more than larger decoder size does (rows 7,8, and 9 in Table 4)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Architecture Comparison. As shown in the table, our designed asymmetric RNN-CNN model (row 1,9, and 12) works better than other asymmetric models (CNN-LSTM, row 11), and models with symmetric structure (RNN-RNN, row 5 and 10).'\nProblem: 'Which model configuration achieved the highest SICK-E score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/28.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Architecture Comparison. As shown in the table, our designed asymmetric RNN-CNN model (row 1,9, and 12) works better than other asymmetric models (CNN-LSTM, row 11), and models with symmetric structure (RNN-RNN, row 5 and 10)." ], "reasoning": "To find the model configuration with the highest SICK-E score, we need to compare the SICK-E scores across all rows in Table 4. The highest SICK-E score is 84.2, achieved by the RNN encoder with 2x600 dimensions and a CNN decoder.", "question": "Which encoder achieved the highest SICK-E score?", "answer": "So, the answer is {\"answer\": \"RNN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIncreasing the dimensionality of the RNN encoder leads to better transferability of the model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/28.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Architecture Comparison. As shown in the table, our designed asymmetric RNN-CNN model (row 1,9, and 12) works better than other asymmetric models (CNN-LSTM, row 11), and models with symmetric structure (RNN-RNN, row 5 and 10)." ], "reasoning": "The description and Table 4 indicate that increasing the dimensionality of the RNN encoder improves model performance on evaluation tasks, as seen in rows 1, 9, and 12.", "statement": "Increasing the dimensionality of the RNN encoder leads to better transferability of the model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: BLEU scores for training NMT models with full word and byte pair encoded vocabularies. Full word models limit vocabulary size to 50K. All models are trained with annealing Adam and scores are averaged over 3 optimizer runs.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/29.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU scores for training NMT models with full word and byte pair encoded vocabularies. Full word models limit vocabulary size to 50K. All models are trained with annealing Adam and scores are averaged over 3 optimizer runs." ], "reasoning": "Sub-word systems with BPE 32K outperform full-word systems in all tasks, despite having fewer parameters.", "description": "As shown in Table 2, sub-word systems outperform full-word systems across the board, despite having fewer total parameters. Systems built on larger data generally benefit from larger vocabularies while smaller systems perform better with smaller vocabularies." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: BLEU scores for training NMT models with full word and byte pair encoded vocabularies. Full word models limit vocabulary size to 50K. All models are trained with annealing Adam and scores are averaged over 3 optimizer runs.'\nProblem: 'Which sub-word system has the highest score for the IWSLT CS-EN task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/29.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU scores for training NMT models with full word and byte pair encoded vocabularies. Full word models limit vocabulary size to 50K. All models are trained with annealing Adam and scores are averaged over 3 optimizer runs." ], "reasoning": "By comparing the scores for the IWSLT CS-EN task, the BPE 16K system has the highest score of 23.0.", "question": "Which sub-word system has the highest score for the IWSLT CS-EN task?", "answer": "So, the answer is {\"answer\": \"BPE 16K\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BPE 32K system outperforms the full-word system in the WMT EN-FI task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/29.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU scores for training NMT models with full word and byte pair encoded vocabularies. Full word models limit vocabulary size to 50K. All models are trained with annealing Adam and scores are averaged over 3 optimizer runs." ], "reasoning": "The BPE 32K system has a score of 14.7, which is higher than the full-word system's score of 12.6 in the WMT EN-FI task.", "statement": "The BPE 32K system outperforms the full-word system in the WMT EN-FI task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Pre-selection results. PPL stands for test set perplexity, all models have ≈5M parameters.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/30.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Pre-selection results. PPL stands for test set perplexity, all models have ≈5M parameters." ], "reasoning": "Syllable-aware models outperform Char-CNN when limited to 5M parameters, with Syl-Concat, Syl-Sum, and Syl-CNN-3 being the best configurations.", "description": "The results of the pre-selection are reported in Table 1. All syllable-aware models comfortably outperform the Char-CNN when the budget is limited to 5M parameters. Surprisingly, a pure word-level model,6 LSTM-Word, also beats the character-aware one under such budget. The three best configurations are Syl-Concat, Syl-Sum, and Syl-CNN-" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Pre-selection results. PPL stands for test set perplexity, all models have ≈5M parameters.'\nProblem: 'Which model has the lowest PPL score under the Char-CNN category?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/30.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Pre-selection results. PPL stands for test set perplexity, all models have ≈5M parameters." ], "reasoning": "By examining the PPL scores under the Char-CNN category, the lowest score is 83.7 for the Syl-Concat model.", "question": "Which model has the lowest PPL score under the Char-CNN category?", "answer": { "answer": "Syl-Concat" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSyl-Sum has the lowest PPL score under the LSTM-Word category.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/30.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Pre-selection results. PPL stands for test set perplexity, all models have ≈5M parameters." ], "reasoning": "By examining the PPL scores under the LSTM-Word category, the lowest score is 84.6 for the Syl-Sum model.", "statement": "Syl-Sum has the lowest PPL score under the LSTM-Word category.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Evaluation of the syllable-aware models against Char-CNN. In each case the smallest model, Syl-Concat, has 18%–33% less parameters than Char-CNN and is trained 1.2–2.2 times faster (Appendix C).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/31.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation of the syllable-aware models against Char-CNN. In each case the smallest model, Syl-Concat, has 18%–33% less parameters than Char-CNN and is trained 1.2–2.2 times faster (Appendix C)." ], "reasoning": "The models show similar performance on small data sets, but Char-CNN scales better on medium-sized data. Syl-Concat is the most advantageous among the syllable-aware models due to its stable results and fewer parameters.", "description": "The results of evaluating these three models on small (1M tokens) and medium-sized (17M– 57M tokens) data sets against Char-CNN for different languages are provided in Table 3. The models demonstrate similar performance on small data, but Char-CNN scales significantly better on medium-sized data. From the three syllable-aware models, Syl-Concat looks the most advantageous as it demonstrates stable results and has the least. number of parameters. Therefore in what follows we will make a more detailed comparison of SylConcat with Char-CNN." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Evaluation of the syllable-aware models against Char-CNN. In each case the smallest model, Syl-Concat, has 18%–33% less parameters than Char-CNN and is trained 1.2–2.2 times faster (Appendix C).'\nProblem: 'Which model demonstrates the best performance on medium-sized data sets for the CS language?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/31.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation of the syllable-aware models against Char-CNN. In each case the smallest model, Syl-Concat, has 18%–33% less parameters than Char-CNN and is trained 1.2–2.2 times faster (Appendix C)." ], "reasoning": "To determine the best performance on medium-sized data sets for the CS language, we look at the 'DATA-L' row for the CS column. Char-CNN has a score of 392, Syl-Sum has 451, and Syl-Concat has 449. The lowest score indicates the best performance.", "question": "Which model demonstrates the best performance on medium-sized data sets for the CS language?", "answer": { "answer": "Char-CNN" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSyl-Concat has the least number of parameters among the syllable-aware models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/31.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation of the syllable-aware models against Char-CNN. In each case the smallest model, Syl-Concat, has 18%–33% less parameters than Char-CNN and is trained 1.2–2.2 times faster (Appendix C)." ], "reasoning": "The description states that Syl-Concat has the least number of parameters among the syllable-aware models, which is supported by the table showing stable results for Syl-Concat.", "statement": "Syl-Concat has the least number of parameters among the syllable-aware models.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Replacing LSTM with Variational RHN.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/32.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Replacing LSTM with Variational RHN." ], "reasoning": "Replacing the LSTM with a Variational RHN resulted in lower perplexities for both Char-CNN and Syl-Concat models, and increasing dLM from 439 to 650 improved Syl-Concat's performance.", "description": "To find out whether this was the case we replaced the LSTM by a Variational RHN (Zilly et al., 2017), and that resulted in a significant reduction of perplexities on PTB for both Char-CNN and Syl-Concat (Table 5). Moreover, increasing dLM from 439 to 650 did result in better performance for Syl-Concat." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Replacing LSTM with Variational RHN.'\nProblem: 'What is the perplexity of the RHN-Syl-Concat model with a dLM of 650?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/32.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Replacing LSTM with Variational RHN." ], "reasoning": "The table shows the perplexity (PPL) values for different models and configurations. For the RHN-Syl-Concat model with a dLM of 650, the perplexity is listed as 69.4.", "question": "What is the perplexity of the RHN-Syl-Concat model with a dLM of 650?", "answer": "So, the answer is {\"answer\": \"69.4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe RHN-Char-CNN model with a dLM of 650 has a perplexity of 67.6.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/32.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Replacing LSTM with Variational RHN." ], "reasoning": "The table lists the perplexity (PPL) for the RHN-Char-CNN model with a dLM of 650 as 67.6.", "statement": "The RHN-Char-CNN model with a dLM of 650 has a perplexity of 67.6.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Effect of adding titles to premises.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/33.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Effect of adding titles to premises." ], "reasoning": "Adding titles to ESIM on FEVER One increases Support Kappa by 52%, and Cohen's Kappa for Transformer on FEVER Title One is 68% higher than that for ESIM on FEVER One.", "description": "On a support basis, we find a 52% increase in Kappa by adding the titles.. Cohen's Kappa is 68% higher than that for ESIM." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Effect of adding titles to premises.'\nProblem: 'What is the increase in Support Kappa when adding titles to ESIM on FEVER One?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/33.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Effect of adding titles to premises." ], "reasoning": "The Support Kappa for ESIM on FEVER One is 0.260, and for ESIM on FEVER Title One, it is 0.394. The increase is 0.394 - 0.260 = 0.134.", "question": "What is the increase in Support Kappa when adding titles to ESIM on FEVER One?", "answer": "So, the answer is {\"answer\": \"0.134\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Support Accuracy for Transformer on FEVER Title One is higher than that for ESIM on FEVER Title One.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/33.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Effect of adding titles to premises." ], "reasoning": "The Support Accuracy for Transformer on FEVER Title One is 0.958, and for ESIM on FEVER Title One, it is 0.846. Since 0.958 is higher than 0.846, the statement is supported.", "statement": "The Support Accuracy for Transformer on FEVER Title One is higher than that for ESIM on FEVER Title One.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Concatenating evidence or not.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/34.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Concatenating evidence or not." ], "reasoning": "The Transformer model's performance on FEVER Title Five is so accurate that oracle guessing does not improve its results.", "description": "makes the FEVER Title Five Oracle performance better than FEVER Title Five.. The Transformer model is accurate enough that oracle guessing does not help." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Concatenating evidence or not.'\nProblem: 'What is the Support Accuracy of the Transformer model on FEVER Title Five?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/34.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Concatenating evidence or not." ], "reasoning": "To find the Claim Accuracy of the Transformer model on FEVER Title Five, locate the row corresponding to 'Transformer on FEVER Title Five' and check the value under the 'Claim Accuracy' column.", "question": "What is the Claim Accuracy of the Transformer model on FEVER Title Five?", "answer": { "answer": "0.801" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ESIM model on FEVER Title One has a higher Claim Accuracy than the Transformer model on FEVER Title Five.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/34.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Concatenating evidence or not." ], "reasoning": "Compare the Claim Accuracy of the ESIM model on FEVER Title One (0.639) with the Claim Accuracy of the Transformer model on FEVER Title Five (0.801). The statement is refuted as 0.639 is less than 0.801.", "statement": "The ESIM model on FEVER Title One has a higher Claim Accuracy than the Transformer model on FEVER Title Five.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Percentage of evidence retrieved from first half of development set. Single-evidence claims only.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/35.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Percentage of evidence retrieved from first half of development set. Single-evidence claims only." ], "reasoning": "The named entity retrieval strategy boosts the evidence retrieval rate to 80.8%, while the film retrievals raise it to 81.2%.", "description": "The named entity retrieval strategy boosts the evidence retrieval rate to 80.8%,. The film retrievals raise evidence retrieval to 81.2%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Percentage of evidence retrieved from first half of development set. Single-evidence claims only.'\nProblem: 'What is the retrieval rate when using entire articles along with named entities and film?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/35.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Percentage of evidence retrieved from first half of development set. Single-evidence claims only." ], "reasoning": "The table shows the retrieval rates for different systems. The retrieval rate for 'Entire Articles + NE + Film' is 90.1%.", "question": "What is the retrieval rate when using entire articles along with named entities and film?", "answer": "So, the answer is {\"answer\": \"90.1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUsing titles in TFIDF improves the retrieval rate to 68.3%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/35.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Percentage of evidence retrieved from first half of development set. Single-evidence claims only." ], "reasoning": "The table shows that the retrieval rate for '+ Titles in TFIDF' is 68.3%, which supports the statement.", "statement": "Using titles in TFIDF improves the retrieval rate to 68.3%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: FEVER Score of various systems. All use NE+Film retrieval.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/36.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: FEVER Score of various systems. All use NE+Film retrieval." ], "reasoning": "Limiting evidence to five statements reduces the FEVER score slightly, but reviewing extra evidence increases the score significantly.", "description": "Limiting evidence in this way when only five statements are retrieved (\"narrow evidence\" in Table 4) pushes FEVER score down very little, to .5550 from .5617 on the development set,. Indeed, when the system reviews the extra evidence, FEVER score goes up to .5844 on the development set." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: FEVER Score of various systems. All use NE+Film retrieval.'\nProblem: 'What is the FEVER score on the development set when the system reviews the extra evidence?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/36.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: FEVER Score of various systems. All use NE+Film retrieval." ], "reasoning": "The table shows that the FEVER score on the development set for 'FEVER Title One (Entire Articles)' is .5844, which is the score when the system reviews the extra evidence.", "question": "What is the FEVER score on the development set when the system reviews the extra evidence?", "answer": { "answer": "0.5844" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nLimiting evidence to five statements reduces the FEVER score on the development set to .5550.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/36.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: FEVER Score of various systems. All use NE+Film retrieval." ], "reasoning": "The table shows that 'FEVER Title One (Narrow Evidence)' has a FEVER score of .5550 on the development set, which supports the statement.", "statement": "Limiting evidence to five statements reduces the FEVER score on the development set to .5550.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Projection accuracy for the isolated example experiment mapping from 2000 → 2001.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/37.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Projection accuracy for the isolated example experiment mapping from 2000 → 2001." ], "reasoning": "The projection performance on all pairs, including ongoing and new conflicts, shows high results, with new conflicts also demonstrating encouraging performance.", "description": "Then, this projection is applied to the second model embeddings of the 47 locations, which are subject to armed conflicts in the year 2001 (38 after skipping pairs with outof-vocabulary elements). Table 2 demonstrates the resulting performance (reflecting how close the predicted vectors are to the actual armed groups active in this or that location). Note that out of 38 pairs from 2001, 31 were already present in the previous data set (ongoing conflicts). This explains why the evaluation on all the pairs gives high results. However, even for the new conflicts, the projection performance is encouraging." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Projection accuracy for the isolated example experiment mapping from 2000 → 2001.'\nProblem: 'What is the @5 performance for new conflicts?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/37.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Projection accuracy for the isolated example experiment mapping from 2000 → 2001." ], "reasoning": "The table shows the performance metrics for different pairs. For new conflicts, the @5 performance is listed in the second row under the @5 column.", "question": "What is the @5 performance for new conflicts?", "answer": "So, the answer is {\"answer\": \"28.6\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe @1 performance for all pairs is higher than the @10 performance for new conflicts.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/37.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Projection accuracy for the isolated example experiment mapping from 2000 → 2001." ], "reasoning": "The table shows that the @1 performance for all pairs is 44.7, which is higher than the @10 performance for new conflicts, which is 42.9.", "statement": "The @1 performance for all pairs is higher than the @10 performance for new conflicts.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Average accuracies of predicting next-year insurgents on the basis of locations, using projections trained on the conflicts from all the preceding years (up-to-now) or the preceding year only (previous). Results for 3 baselines are shown along with the proposed incremental dynamic approach.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/38.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average accuracies of predicting next-year insurgents on the basis of locations, using projections trained on the conflicts from all the preceding years (up-to-now) or the preceding year only (previous). Results for 3 baselines are shown along with the proposed incremental dynamic approach." ], "reasoning": "The incremental dynamic approach outperforms other baselines in the all pairs scenario, especially when including OOV words, due to its ability to incrementally update and expand vocabulary.", "description": "Table 3 presents the results for these experiments, as well as baselines (averaged across 15 years). For the proposed incr. dynamic approach, the performance of the previous projections is. comparable to that of the up-to-now projections on the accuracies @5 and @10, and is even higher on the accuracy @1 (statistically significant with t-test, p < 0.01). Thus, the single-year projections are somewhat more 'focused', while taking much less time to learn, because of less training pairs. The fact that our models were incrementally updated, not trained from scratch, is crucial. The results of the separate baseline look more like random jitter. The cumulative baseline results are slightly better, probably simply because they are trained on more data. However, they still perform much worse than the models trained using incremental updates. This is because the former models are not connected to each other, and thus are initialized with a different layout of words in the vector space. This gives rise to formally different directions of semantic relations in each yearly model (the relations themselves are still there, but they are rotated and scaled differently). The results for the incr. static baseline, when tested only on the words present in the test model vocabulary (the left part of the table), seem better than those of the proposed incr. dynamic approach. This stems from the fact that incremental updating with static vocabulary means that we never add new words to the models; thus, they contain only the vocabulary learned from the 1994 texts. The result is that at test time we skip many more pairs than with the other approaches (about 62% in average). Subsequently, the projections are tested only on a minor part of the test sets. Of course, skipping large parts of the data would be a major drawback for any realistic application, so the incr. static baseline is not really plausible. For comparison, the right part of Table 3 provides the accuracies for the setup in which all the pairs are evaluated (for pairs with OOV words the accuracy is always 0). Other tested approaches are not much affected by this change, but for incr. static the performance drops drastically. As a result, for the all pairs scenario, incremental updating with vocabulary expansion outperforms all the baselines (the differences are statistically significant with t-test, p < 0.01)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Average accuracies of predicting next-year insurgents on the basis of locations, using projections trained on the conflicts from all the preceding years (up-to-now) or the preceding year only (previous). Results for 3 baselines are shown along with the proposed incremental dynamic approach.'\nProblem: 'Which approach has the highest accuracy @10 for all pairs, including OOV up-to-now?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/38.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average accuracies of predicting next-year insurgents on the basis of locations, using projections trained on the conflicts from all the preceding years (up-to-now) or the preceding year only (previous). Results for 3 baselines are shown along with the proposed incremental dynamic approach." ], "reasoning": "To find the highest accuracy @10 for all pairs, including OOV up-to-now, we look at the column labeled '@10' under 'All pairs, including OOV up-to-now'. The highest value in this column is 34.2, which corresponds to the 'Incr. static' approach.", "question": "Which approach has the highest accuracy @10 for all pairs, including OOV up-to-now?", "answer": { "answer": "Incr. static" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe cumulative baseline performs better than the separate baseline in all scenarios.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/38.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average accuracies of predicting next-year insurgents on the basis of locations, using projections trained on the conflicts from all the preceding years (up-to-now) or the preceding year only (previous). Results for 3 baselines are shown along with the proposed incremental dynamic approach." ], "reasoning": "By comparing the cumulative and separate baselines across all columns, we see that the cumulative baseline consistently has higher accuracy values than the separate baseline.", "statement": "The cumulative baseline performs better than the separate baseline in all scenarios.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Evaluation of relative time-lines for each model and loss function, where L∗ indicates the (unweighted) sum of Lτ, Lτce, and Lτh.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/39.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluation of relative time-lines for each model and loss function, where L∗ indicates the (unweighted) sum of Lτ, Lτce, and Lτh." ], "reasoning": "Overall performance on TD‡ is higher than on TE3‡, with Lτ providing the most robust results, and C-TLM outperforming S-TLM on TE3‡.", "description": "We compared our three proposed models for the three loss functions Lτ , Lτ ce, and Lτ h, and their linear (unweighted) combination L∗, on TE3‡ and TD‡, for which the results are shown in Table 4.. A trend that can be observed is that overall performance on TD‡ is higher than that of TE3‡. If we compare loss functions Lτ , Lτ ce, and Lτh, and combination L∗, it can be noticed that, although all loss functions seem to give fairly similar performance, Lτ gives the most robust results (never lowest), especially noticeable for the smaller dataset TD‡.. The combination of losses L∗ shows mixed results, and has lower performance for S-TLM and C-TLM, but better performance for TL2RTL.. Moreover, we can clearly see that on TE3‡, CTLM performs better than the indirect models, across all loss functions.. On TD‡, the indirect models seem to perform slightly better.. the difference between C-TLM and S-TLM is small on the smaller TD‡. on TE3‡, the larger dataset, C-TLM clearly outperforms S-TLM across all loss functions," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Evaluation of relative time-lines for each model and loss function, where L∗ indicates the (unweighted) sum of Lτ, Lτce, and Lτh.'\nProblem: 'Which model and loss function combination has the highest F-score on TD‡?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/39.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluation of relative time-lines for each model and loss function, where L∗ indicates the (unweighted) sum of Lτ, Lτce, and Lτh." ], "reasoning": "By examining the F-scores on TD‡, the highest value is 62.3, which corresponds to the TL2RTL (L∗) model.", "question": "Which model and loss function combination has the highest F-score on TD‡?", "answer": "So, the answer is {\"answer\": \"TL2RTL (L∗)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nC-TLM (Lτ) has the highest F-score on TE3‡.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/39.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluation of relative time-lines for each model and loss function, where L∗ indicates the (unweighted) sum of Lτ, Lτce, and Lτh." ], "reasoning": "By examining the F-scores on TE3‡, the highest value is 52.8, which corresponds to TL2RTL (Lτ ce), not C-TLM (Lτ).", "statement": "C-TLM (Lτ) has the highest F-score on TE3‡.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Jensen-Shannon divergence between word distribution in all Onion drug sites, Legal and Illegal Onion drug sites, and eBay sites. Each domain was also split in half for within-domain comparison.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/40.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Jensen-Shannon divergence between word distribution in all Onion drug sites, Legal and Illegal Onion drug sites, and eBay sites. Each domain was also split in half for within-domain comparison." ], "reasoning": "The self-distance of the eBay, Legal Onion, and Illegal Onion corpora ranges from 0.40 to 0.45, while the distance between each pair ranges from 0.60 to 0.65, forming an equilateral triangle in word distribution space.", "description": "Table 1 presents our results. The self-distance f the eBay, Legal Onion and Illegal Onion corpora lies between 0.40 to 0.45 by the JensenShannon divergence, but the distance between each pair is 0.60 to 0.65, with the three approximately forming an equilateral triangle in the space of word distributions. Similar results are obtained using Variational distance, and are omitted for brevity." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Jensen-Shannon divergence between word distribution in all Onion drug sites, Legal and Illegal Onion drug sites, and eBay sites. Each domain was also split in half for within-domain comparison.'\nProblem: 'What is the self-distance of the eBay corpus according to the Jensen-Shannon divergence?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/40.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Jensen-Shannon divergence between word distribution in all Onion drug sites, Legal and Illegal Onion drug sites, and eBay sites. Each domain was also split in half for within-domain comparison." ], "reasoning": "The self-distance of the eBay corpus is found by looking at the intersection of 'eBay all' with itself in the table, which is 0.61.", "question": "What is the self-distance of the eBay corpus according to the Jensen-Shannon divergence?", "answer": "So, the answer is {\"answer\": \"0.61\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe distance between the Legal Onion and Illegal Onion corpora is 0.62.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/40.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Jensen-Shannon divergence between word distribution in all Onion drug sites, Legal and Illegal Onion drug sites, and eBay sites. Each domain was also split in half for within-domain comparison." ], "reasoning": "The distance between the Legal Onion and Illegal Onion corpora can be found at the intersection of 'Legal Onion all' and 'Illegal Onion all' in the table, which is 0.62.", "statement": "The distance between the Legal Onion and Illegal Onion corpora is 0.62.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Average percentage of wikifiable named entities in a website per domain, with standard error.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/41.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average percentage of wikifiable named entities in a website per domain, with standard error." ], "reasoning": "The Wikification success ratios for eBay and Illegal Onion named entities are comparable and relatively low, while Legal Onion sites have a much higher Wikification percentage.", "description": "each average. According to our results (Table 2), the Wikification success ratios of eBay and Illegal. Onion named entities is comparable and relatively low. However, sites selling legal drugs on Onion have a much higher Wikification percentage." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Average percentage of wikifiable named entities in a website per domain, with standard error.'\nProblem: 'Which named entity has the highest Wikification success ratio?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/41.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average percentage of wikifiable named entities in a website per domain, with standard error." ], "reasoning": "By comparing the % Wikifiable values, Legal Onion has the highest percentage at 50.8±2.31.", "question": "Which named entity has the highest Wikification success ratio?", "answer": "So, the answer is {\"answer\": \"Legal Onion\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Wikification success ratio for eBay is higher than that for Illegal Onion.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/41.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average percentage of wikifiable named entities in a website per domain, with standard error." ], "reasoning": "The table shows that eBay has a Wikification success ratio of 38.6±2.00, which is higher than Illegal Onion's 32.5±1.35.", "statement": "The Wikification success ratio for eBay is higher than that for Illegal Onion.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: F1 results for knowledge-based systems on the Raganato et al. (2017a) dataset. Top rows show conflicting results for UKB. † for results reported in Raganato et al. (2017a), ‡ for results reported in Chaplot and Sakajhutdinov (2018). Best results in bold. S2 stands for Senseval-2, S3 for Senseval-3, S07 for Semeval-2007, S13 for Semeval-2013 and S15 for Semeval-2015.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/42.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: F1 results for knowledge-based systems on the Raganato et al. (2017a) dataset. Top rows show conflicting results for UKB. † for results reported in Raganato et al. (2017a), ‡ for results reported in Chaplot and Sakajhutdinov (2018). Best results in bold. S2 stands for Senseval-2, S3 for Senseval-3, S07 for Semeval-2007, S13 for Semeval-2013 and S15 for Semeval-2015." ], "reasoning": "The table shows that UKB (this work) yields the best overall result, despite conflicting results in the top two rows.", "description": "The two top rows in Table 1 show conflicting results for UKB.. As the results show, that paper reports a suboptimal use of UKB.. the table also reports the. best performing knowledge-based systems on this dataset.. also report We (Chaplot and Sakajhutdinov, 2018), the latest work on this area, as well as the most frequent sense as given by WordNet counts. The table shows that UKB yields the best overall result." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: F1 results for knowledge-based systems on the Raganato et al. (2017a) dataset. Top rows show conflicting results for UKB. † for results reported in Raganato et al. (2017a), ‡ for results reported in Chaplot and Sakajhutdinov (2018). Best results in bold. S2 stands for Senseval-2, S3 for Senseval-3, S07 for Semeval-2007, S13 for Semeval-2013 and S15 for Semeval-2015.'\nProblem: 'Which system yields the best overall result according to the table?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/42.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: F1 results for knowledge-based systems on the Raganato et al. (2017a) dataset. Top rows show conflicting results for UKB. † for results reported in Raganato et al. (2017a), ‡ for results reported in Chaplot and Sakajhutdinov (2018). Best results in bold. S2 stands for Senseval-2, S3 for Senseval-3, S07 for Semeval-2007, S13 for Semeval-2013 and S15 for Semeval-2015." ], "reasoning": "The table shows various systems and their performance across different metrics. The 'All' column indicates the overall result, where UKB (this work) has the highest score of 67.3.", "question": "Which system yields the best overall result according to the table?", "answer": { "answer": "UKB (this work)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUKB (this work) has the highest score in the S15 metric.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/42.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: F1 results for knowledge-based systems on the Raganato et al. (2017a) dataset. Top rows show conflicting results for UKB. † for results reported in Raganato et al. (2017a), ‡ for results reported in Chaplot and Sakajhutdinov (2018). Best results in bold. S2 stands for Senseval-2, S3 for Senseval-3, S07 for Semeval-2007, S13 for Semeval-2013 and S15 for Semeval-2015." ], "reasoning": "The table shows that UKB (this work) has a score of 70.3 in the S15 metric, which is the highest among all listed systems.", "statement": "UKB (this work) has the highest score in the S15 metric.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: F1 results for supervised systems on the Raganato et al. (2017a) dataset. † for results reported in Raganato et al. (2017a). Best results in bold. Note that Raganato et al. (2017b) used S07 for development.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/43.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F1 results for supervised systems on the Raganato et al. (2017a) dataset. † for results reported in Raganato et al. (2017a). Best results in bold. Note that Raganato et al. (2017b) used S07 for development." ], "reasoning": "Supervised systems generally perform better than knowledge-based systems on the dataset, with only a small margin in some cases.", "description": "Table 2 reports the results of supervised systems on the same dataset, taken from the two works that use the dataset. As expected, supervised systems outperform knowledge-based systems, by a small margin in some of the cases." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: F1 results for supervised systems on the Raganato et al. (2017a) dataset. † for results reported in Raganato et al. (2017a). Best results in bold. Note that Raganato et al. (2017b) used S07 for development.'\nProblem: 'Which system has the highest score in the S2 column?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/43.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F1 results for supervised systems on the Raganato et al. (2017a) dataset. † for results reported in Raganato et al. (2017a). Best results in bold. Note that Raganato et al. (2017b) used S07 for development." ], "reasoning": "To find the highest score in the S2 column, compare the values in the S2 column for all systems. The highest value is 73.8 from Yuan et al. (2016).", "question": "Which system has the highest score in the S2 column?", "answer": "So, the answer is {\"answer\": \"Yuan et al. (2016)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRaganato et al. (2017b) has the lowest score in the S07 column.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/43.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F1 results for supervised systems on the Raganato et al. (2017a) dataset. † for results reported in Raganato et al. (2017a). Best results in bold. Note that Raganato et al. (2017b) used S07 for development." ], "reasoning": "To verify this statement, compare the scores in the S07 column. The lowest score is 60.0 from IMS Zhong and Ng (2010), not Raganato et al. (2017b) which has 64.8.", "statement": "Raganato et al. (2017b) has the lowest score in the S07 column.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Additional results on other settings of UKB. nf subscript stands for “no sense frequency”. Top rows use a single sentence as context, while the bottom rows correspond to extended context (cf. Sect. 3). Best results in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/44.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Additional results on other settings of UKB. nf subscript stands for “no sense frequency”. Top rows use a single sentence as context, while the bottom rows correspond to extended context (cf. Sect. 3). Best results in bold." ], "reasoning": "The table shows that using sense frequencies improves F1 scores by 7-8 percentage points, and the best algorithm is ppr_w2w, followed by dfs and ppr.", "description": "Ta. The table shows that the key factor is the use of sense frequencies, and systems that do not use them (those with a nf subscript) suffer a loss between 7 and 8 percentage points in F1.. The table also shows that extending the context is mildly effective.. Regarding the algorithm, the table confirms that the best method is ppr w2w, followed by the subgraph approach (dfs) and ppr." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Additional results on other settings of UKB. nf subscript stands for “no sense frequency”. Top rows use a single sentence as context, while the bottom rows correspond to extended context (cf. Sect. 3). Best results in bold.'\nProblem: 'What is the F1 score for the ppr_w2w algorithm with a single context sentence for S2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/44.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Additional results on other settings of UKB. nf subscript stands for “no sense frequency”. Top rows use a single sentence as context, while the bottom rows correspond to extended context (cf. Sect. 3). Best results in bold." ], "reasoning": "To find the F1 score for the ppr_w2w algorithm with a single context sentence for S2, locate the row for ppr_w2w and the column for S2 under 'Single context sentence'. The value is 69.0.", "question": "What is the F1 score for the ppr_w2w algorithm with a single context sentence for S2?", "answer": { "answer": "69.0" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe dfs_ppr algorithm has a higher F1 score than the ppr algorithm for S3 with a single context sentence.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/44.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Additional results on other settings of UKB. nf subscript stands for “no sense frequency”. Top rows use a single sentence as context, while the bottom rows correspond to extended context (cf. Sect. 3). Best results in bold." ], "reasoning": "To verify this statement, compare the F1 scores for dfs_ppr and ppr algorithms for S3 with a single context sentence. The F1 score for dfs_ppr is 63.5, and for ppr it is 66.5. Therefore, the statement is refuted.", "statement": "The dfs_ppr algorithm has a higher F1 score than the ppr algorithm for S3 with a single context sentence.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Accuracy (in %) of our model and other text classification models on both versions of SWBD.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/45.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy (in %) of our model and other text classification models on both versions of SWBD." ], "reasoning": "The table compares the performance of various models, including traditional methods like BoW and TF-IDF, as well as advanced models like CNN and HN-SA, on two datasets (SWBD and SWBD2).", "description": "We compare the performance of our model (Table 2) with traditional Bag of Words (BoW), TF-IDF, and n-grams features based classifiers. We also compare against averaged Skip-Gram (Mikolov et al., 2013), Doc2Vec (Le and Mikolov, 2014), CNN (Kim, 2014), Hierarchical Attention (HN-ATT) (Yang et al., 2016) and hierarchical network (HN) models." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Accuracy (in %) of our model and other text classification models on both versions of SWBD.'\nProblem: 'Which model achieved the highest accuracy on the SWBD2 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/45.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy (in %) of our model and other text classification models on both versions of SWBD." ], "reasoning": "To find the model with the highest accuracy on the SWBD2 dataset, we need to look at the SWBD2 column and identify the highest value. The highest value in the SWBD2 column is 95.92, achieved by the HN-SA model.", "question": "Which model achieved the highest accuracy on the SWBD2 dataset?", "answer": "So, the answer is {\"answer\": \"HN-SA\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe CNN model achieved the highest accuracy on the SWBD dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/45.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy (in %) of our model and other text classification models on both versions of SWBD." ], "reasoning": "To verify this statement, we need to check the SWBD column for the highest accuracy value. The highest value in the SWBD column is 89.47, achieved by the HN-SA model, not the CNN model.", "statement": "The CNN model achieved the highest accuracy on the SWBD dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Exact matching accuracy on SQL queries.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/46.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Exact matching accuracy on SQL queries." ], "reasoning": "IRNet significantly outperforms all baselines, achieving a 27.0% improvement over SyntaxSQLNet(augment) on the test set. Incorporating BERT further enhances the performance of both SyntaxSQLNet and IRNet.", "description": "Table 1 presents the exact matching accuracy of IRNet and various baselines on the development set and the test set. IRNet clearly outperforms all the baselines by a substantial margin. It obtains 27.0% absolute improvement over SyntaxSQLIt also obtains 19.5% absolute Net on test set. improvement over SyntaxSQLNet(augment) that performs large-scale data augmentation. When incorporating BERT, the performance of both SyntaxSQLNet and IRNet is substantially improved and the accuracy gap between them on both the development set and the test set is widened." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Exact matching accuracy on SQL queries.'\nProblem: 'What is the exact matching accuracy improvement of IRNet over SyntaxSQLNet(augment) on the test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/46.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Exact matching accuracy on SQL queries." ], "reasoning": "IRNet achieves 46.7% accuracy on the test set, while SyntaxSQLNet(augment) achieves 27.2%. The improvement is calculated as 46.7% - 27.2% = 19.5%.", "question": "What is the exact matching accuracy improvement of IRNet over SyntaxSQLNet(augment) on the test set?", "answer": { "answer": "19.5" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIRNet(BERT) achieves over 60% accuracy on the development set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/46.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Exact matching accuracy on SQL queries." ], "reasoning": "The table shows that IRNet(BERT) achieves 61.9% accuracy on the development set, which is over 60%.", "statement": "IRNet(BERT) achieves over 60% accuracy on the development set.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Exact matching accuracy of SyntaxSQLNet, SyntaxSQLNet(BERT), IRNet and IRNet(BERT) on test set by hardness level.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/47.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Exact matching accuracy of SyntaxSQLNet, SyntaxSQLNet(BERT), IRNet and IRNet(BERT) on test set by hardness level." ], "reasoning": "IRNet consistently outperforms SyntaxSQLNet across all difficulty levels, with or without BERT, achieving a 23.3% improvement in the Hard level.", "description": "As shown in Table 2, IRNet significantly outperforms SyntaxSQLNet in all four hardness levels with or without BERT. For example, compared with SyntaxSQLNet, IRNet obtains 23.3% absolute improvement in Hard level." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Exact matching accuracy of SyntaxSQLNet, SyntaxSQLNet(BERT), IRNet and IRNet(BERT) on test set by hardness level.'\nProblem: 'What is the percentage improvement of IRNet over SyntaxSQLNet in the Extra Hard level?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/47.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Exact matching accuracy of SyntaxSQLNet, SyntaxSQLNet(BERT), IRNet and IRNet(BERT) on test set by hardness level." ], "reasoning": "To find the percentage improvement, subtract the SyntaxSQLNet (BERT) score from the IRNet (BERT) score in the Extra Hard level. IRNet (BERT) has 25.3% and SyntaxSQLNet (BERT) has 8.6%. The improvement is 25.3% - 8.6% = 16.7%.", "question": "What is the percentage improvement of IRNet over SyntaxSQLNet in the Extra Hard level?", "answer": { "answer": "16.7" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIRNet achieves over 70% accuracy in the Easy level.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/47.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Exact matching accuracy of SyntaxSQLNet, SyntaxSQLNet(BERT), IRNet and IRNet(BERT) on test set by hardness level." ], "reasoning": "The table shows that IRNet achieves 70.1% accuracy in the Easy level, which is over 70%.", "statement": "IRNet achieves over 70% accuracy in the Easy level.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Exact matching accuracy on development set. The header ‘SQL’ means that the approaches are learned to generate SQL, while the header ‘SemQL’ indicates that they are learned to generate SemQL queries.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/48.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Exact matching accuracy on development set. The header ‘SQL’ means that the approaches are learned to generate SQL, while the header ‘SemQL’ indicates that they are learned to generate SemQL queries." ], "reasoning": "The base model outperforms SyntaxSQLNet and its variants, with schema linking providing significant improvements in accuracy and F1 score.", "description": "As shown in Table 3, there are at least 6.6% and up to 14.4% absolute improvements on accuracy of exact matching on the development set. For example, when SyntaxSQLNet is learned to generate SemQL queries instead of SQL queries, it registers 8.6% absolute improvement and even outperforms SyntaxSQLNet(augment) which performs largescale data augmentation.. Table 4 presents the ablation study results. It is clear that our base model significantly outperforms SyntaxSQLNet, SyntaxSQLNet( augment) and SyntaxSQLNet(BERT). Performing schema linking ('+SL') brings about 8.5% and 6.4% absolute improvement on IRNet and IRNet(BERT).. The F1 score on the WHERE clause increases by 12.5% when IRNet performs schema linking.. The number of examples suffering from this problem decreases by 70%, when using the memory augmented pointer network." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Exact matching accuracy on development set. The header ‘SQL’ means that the approaches are learned to generate SQL, while the header ‘SemQL’ indicates that they are learned to generate SemQL queries.'\nProblem: 'What is the absolute improvement in accuracy when SyntaxSQLNet is learned to generate SemQL queries instead of SQL queries?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/48.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Exact matching accuracy on development set. The header ‘SQL’ means that the approaches are learned to generate SQL, while the header ‘SemQL’ indicates that they are learned to generate SemQL queries." ], "reasoning": "The table shows that SyntaxSQLNet has an accuracy of 18.9% for SQL queries and 27.5% for SemQL queries, resulting in an absolute improvement of 8.6%.", "question": "What is the absolute improvement in accuracy when SyntaxSQLNet is learned to generate SemQL queries instead of SQL queries?", "answer": "So, the answer is {\"answer\": \"8.6\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPerforming schema linking brings about 8.5% absolute improvement on IRNet.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/48.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Exact matching accuracy on development set. The header ‘SQL’ means that the approaches are learned to generate SQL, while the header ‘SemQL’ indicates that they are learned to generate SemQL queries." ], "reasoning": "The description states that performing schema linking ('+SL') brings about 8.5% absolute improvement on IRNet, which is supported by the table.", "statement": "Performing schema linking brings about 8.5% absolute improvement on IRNet.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Classifiers’ accuracy on the Supports and Refutes cases from the FEVER Dev set and on the Generated pairs for the Symmetric Test Set in the setting of without (base) and with (r.w) re-weight.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/49.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Classifiers’ accuracy on the Supports and Refutes cases from the FEVER Dev set and on the Generated pairs for the Symmetric Test Set in the setting of without (base) and with (r.w) re-weight." ], "reasoning": "The re-weighting method improves the accuracy of ESIM and BERT models on the synthetic test set but reduces their accuracy on the FEVER DEV set.", "description": "Table 3 summarizes the performance of the three models on the SUPPORTS and REFUTES pairs from the FEVER DEV set and on the created SYMMETRIC TEST SET pairs.. All models perform relatively well on FEVER DEV but achieve less than 60% accuracy on the synthetic ones.. The re-weighting method increases the accuracy of the ESIM and BERT models by an absolute 3.4% and 3.3% respectively. One can notice that this improvement comes at a cost in the accuracy over the FEVER DEV pairs.. Applying the regularization method, using the same training data, helps to train a more robust model that performs better on our test set, where verification in context is a key requirement." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Classifiers’ accuracy on the Supports and Refutes cases from the FEVER Dev set and on the Generated pairs for the Symmetric Test Set in the setting of without (base) and with (r.w) re-weight.'\nProblem: 'What is the accuracy improvement of the BERT model on the Generated base set when using the re-weighting method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/49.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Classifiers’ accuracy on the Supports and Refutes cases from the FEVER Dev set and on the Generated pairs for the Symmetric Test Set in the setting of without (base) and with (r.w) re-weight." ], "reasoning": "The accuracy of the BERT model on the Generated base set is 58.3%, and with the re-weighting method, it is 61.6%. The improvement is 61.6% - 58.3% = 3.3%.", "question": "What is the accuracy improvement of the BERT model on the Generated base set when using the re-weighting method?", "answer": "So, the answer is {\"answer\": \"3.3\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ESIM model achieves higher accuracy on the Generated base set than the NSMN model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/49.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Classifiers’ accuracy on the Supports and Refutes cases from the FEVER Dev set and on the Generated pairs for the Symmetric Test Set in the setting of without (base) and with (r.w) re-weight." ], "reasoning": "The table shows that the ESIM model has an accuracy of 55.9% on the Generated base set, while the NSMN model has an accuracy of 58.7%. Therefore, the statement is refuted.", "statement": "The ESIM model achieves higher accuracy on the Generated base set than the NSMN model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Top 10 LMI-ranked bigrams in the train set of FEVER for Support.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/50.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Top 10 LMI-ranked bigrams in the train set of FEVER for Support." ], "reasoning": "The bigrams 'least one' and 'starred movie' are biased phrases in the training set that can help in predictions over the development set.", "description": "Table 7 and Table 8 summarize the top 10 bigrams for SUPPORT and NOT ENOUGH INFO. The correlation between the biased phrases in the two dataset splits is not as strong as in the REFUTE label, presented in the paper.. However, one can notice that some of the biased bigrams in the training set, such as \"least one\" and \"starred movie\", translate to cues that can help in predictions over the development set." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Top 10 LMI-ranked bigrams in the train set of FEVER for Support.'\nProblem: 'Which bigram has the highest Train LMI and what is its corresponding Development LMI?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/50.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Top 10 LMI-ranked bigrams in the train set of FEVER for Support." ], "reasoning": "The bigram 'united states' has the highest Train LMI of 271, and its corresponding Development LMI is 268.", "question": "What is Development LMI of bigram with the highest Train LMI?", "answer": "So, the answer is {\"answer\": \"268\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe bigram 'won award' has a higher Train LMI than 'american actor'.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/50.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Top 10 LMI-ranked bigrams in the train set of FEVER for Support." ], "reasoning": "The table shows that the Train LMI for 'won award' is 133, which is higher than the Train LMI for 'american actor' which is 126.", "statement": "The bigram 'won award' has a higher Train LMI than 'american actor'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: NewsQA to SQuAD. Exact match (EM) and span F1 results on SQuAD development set of a NewsQA BIDAF model baseline vs. one finetuned on SQuAD using the data generated by a 2-stage SynNet (Snet).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/51.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: NewsQA to SQuAD. Exact match (EM) and span F1 results on SQuAD development set of a NewsQA BIDAF model baseline vs. one finetuned on SQuAD using the data generated by a 2-stage SynNet (Snet)." ], "reasoning": "The SynNet improves the Mnewsqa system's EM by 1.6% and F1 by 0.7%.", "description": "report brief results on SQuAD (Table 3),. We also evaluate the SynNet on the NewsQAto-SQuAD direction. We directly apply the best setting from the other direction and report the result in Table 3.. The SynNet improves over the baseline by 1.6% in EM and 0.7% in F1. Limited by space, we leave out ablation studies in this direction." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: NewsQA to SQuAD. Exact match (EM) and span F1 results on SQuAD development set of a NewsQA BIDAF model baseline vs. one finetuned on SQuAD using the data generated by a 2-stage SynNet (Snet).'\nProblem: 'What is the improvement in F1 score when SynNet is added to Mnewsqa?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/51.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: NewsQA to SQuAD. Exact match (EM) and span F1 results on SQuAD development set of a NewsQA BIDAF model baseline vs. one finetuned on SQuAD using the data generated by a 2-stage SynNet (Snet)." ], "reasoning": "The F1 score for Mnewsqa is 60.8, and for Mnewsqa + Snet, it is 61.5. The improvement is 61.5 - 60.8 = 0.7.", "question": "What is the improvement in F1 score when SynNet is added to Mnewsqa?", "answer": "So, the answer is {\"answer\": \"0.7\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe SynNet improves the EM score of Mnewsqa by 1.6%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/51.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: NewsQA to SQuAD. Exact match (EM) and span F1 results on SQuAD development set of a NewsQA BIDAF model baseline vs. one finetuned on SQuAD using the data generated by a 2-stage SynNet (Snet)." ], "reasoning": "The EM score for Mnewsqa is 46.3, and for Mnewsqa + Snet, it is 47.9. The improvement is 47.9 - 46.3 = 1.6%.", "statement": "The SynNet improves the EM score of Mnewsqa by 1.6%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Performance on two datasets against the user simulator and human.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/52.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance on two datasets against the user simulator and human." ], "reasoning": "Equation (11) is effective in improving performance, as evidenced by the highest % Achieved in the second and last rows of Table 3.", "description": "Table 3 shows the performance of baselines against user simulator and human on the two datasets.. Equation (11)) is also proven effective in deliver. ing more achievement, which can be seen from the second and last rows of Table 3.. The results are consistent with those in Table 3." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Performance on two datasets against the user simulator and human.'\nProblem: 'Which model achieved the highest % Achieved on Dataset 2 vs. Simulator?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/52.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance on two datasets against the user simulator and human." ], "reasoning": "To find the model with the highest % Achieved on Dataset 2 vs. Simulator, look at the '% Achieved' column under 'Dataset 2 vs. Simulator'. The highest value is 83.58, achieved by the Seq2Seq(goal+look+state) model.", "question": "Which model achieved the highest % Achieved on Dataset 2 vs. Simulator?", "answer": "So, the answer is {\"answer\": \"Seq2Seq(goal+loo+state)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Seq2Seq(goal+look+state) model has the highest % Achieved on Dataset 1 vs. Human.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/52.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance on two datasets against the user simulator and human." ], "reasoning": "To verify this statement, check the '% Achieved' column under 'Dataset 1 vs. Human'. The highest value is 77.42, achieved by the Seq2Seq(goal+look+state) model.", "statement": "The Seq2Seq(goal+look+state) model has the highest % Achieved on Dataset 1 vs. Human.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Correlation between Attribution word importance with POS tags, Fertility, and Syntactic Depth. Fertility can be categorized into 4 types: one-to-many (“≥2”), one-to-one (“1”), many-to-one (“(0,1)”), and null-aligned (“0”). Syntactic depth shows the depth of a word in the dependency tree. A lower tree depth indicates closer to the root node in the dependency tree, which might indicate a more important word.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/53.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Correlation between Attribution word importance with POS tags, Fertility, and Syntactic Depth. Fertility can be categorized into 4 types: one-to-many (“≥2”), one-to-one (“1”), many-to-one (“(0,1)”), and null-aligned (“0”). Syntactic depth shows the depth of a word in the dependency tree. A lower tree depth indicates closer to the root node in the dependency tree, which might indicate a more important word." ], "reasoning": "The table shows the correlation between word importance and POS tags, fertility, and syntactic depth, indicating that words closer to the root in the dependency tree might be more important.", "description": "Table 2: Correlation between Attribution word importance with POS tags, Fertility, and Syntactic Depth. Fertility can be categorized into 4 types: one-to-many (\"≥ 2\"), one-to-one (\"1\"), many-to-one (\"(0, 1)\"), and null-aligned (\"0\"). Syntactic depth shows the depth of a word in the dependency tree. A lower tree depth indicates closer to the root node in the dependency tree, which might indicate a more important word." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Correlation between Attribution word importance with POS tags, Fertility, and Syntactic Depth. Fertility can be categorized into 4 types: one-to-many (“≥2”), one-to-one (“1”), many-to-one (“(0,1)”), and null-aligned (“0”). Syntactic depth shows the depth of a word in the dependency tree. A lower tree depth indicates closer to the root node in the dependency tree, which might indicate a more important word.'\nProblem: 'Which fertility type has the highest importance for Zh⇒En translation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/53.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Correlation between Attribution word importance with POS tags, Fertility, and Syntactic Depth. Fertility can be categorized into 4 types: one-to-many (“≥2”), one-to-one (“1”), many-to-one (“(0,1)”), and null-aligned (“0”). Syntactic depth shows the depth of a word in the dependency tree. A lower tree depth indicates closer to the root node in the dependency tree, which might indicate a more important word." ], "reasoning": "To find the fertility type with the highest importance for Zh⇒En translation, look at the 'Zh⇒En 21.0%' column under the 'Fertility' section and identify the highest percentage.", "question": "Which fertility type has the highest importance for Zh⇒En translation?", "answer": "So, the answer is {\"answer\": \"≥ 2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFor En⇒Ja translation, punctuation has the highest word importance.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/53.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Correlation between Attribution word importance with POS tags, Fertility, and Syntactic Depth. Fertility can be categorized into 4 types: one-to-many (“≥2”), one-to-one (“1”), many-to-one (“(0,1)”), and null-aligned (“0”). Syntactic depth shows the depth of a word in the dependency tree. A lower tree depth indicates closer to the root node in the dependency tree, which might indicate a more important word." ], "reasoning": "To verify this statement, look at the 'En⇒Ja 0.7%' column under the 'POS Tags' section and compare the percentages. Punctuation has the highest percentage (30.5%).", "statement": "For En⇒Ja translation, punctuation has the highest word importance.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Average accuracies and Macro-F1 scores over five runs with random initialization along with their standard deviations. Bold: best results or within std of them. ∗ indicates that the method’s result is significantly better than all baseline methods, † indicates that the method’s result is significantly better than all baselines methods that use the aspect-based data only, with p<0.05 according to a one-tailed unpaired t-test. The data annotations S, N and A indicate training with Sentence-level, Noisy sentence-level and Aspect-level data respectively. Numbers for TDLSTM+Att,ATAE-LSTM,MM,RAM and LSTM+SynATT+TarRep are from He et al. (2018a). Numbers for Semisupervised are from He et al. (2018b).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/54.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Average accuracies and Macro-F1 scores over five runs with random initialization along with their standard deviations. Bold: best results or within std of them. ∗ indicates that the method’s result is significantly better than all baseline methods, † indicates that the method’s result is significantly better than all baselines methods that use the aspect-based data only, with p<0.05 according to a one-tailed unpaired t-test. The data annotations S, N and A indicate training with Sentence-level, Noisy sentence-level and Aspect-level data respectively. Numbers for TDLSTM+Att,ATAE-LSTM,MM,RAM and LSTM+SynATT+TarRep are from He et al. (2018a). Numbers for Semisupervised are from He et al. (2018b)." ], "reasoning": "The table presents average accuracies and Macro-F1 scores over five runs with random initialization for various methods, highlighting the best results and significant improvements.", "description": "Table 1: Average accuracies and Macro-F1 scores over five runs with random initialization along with their standard deviations. Bold: best results or within std of them. ∗ indicates that the method's result is significantly better than all baseline methods, † indicates that the method's result is significantly better than all baselines methods that use the aspect-based data only, with p < 0.05 according to a one-tailed unpaired t-test. The data annotations S,. and A indicate training with Sentence-level, Noisy sentence-level and Aspect-level data respectively. Numbers for TDLSTM+Att,ATAE-LSTM,MM,RAM and LSTM+SynATT+TarRep are from (He et al., 2018a). Numbers for Semisupervised are from (He et al., 2018b)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Average accuracies and Macro-F1 scores over five runs with random initialization along with their standard deviations. Bold: best results or within std of them. ∗ indicates that the method’s result is significantly better than all baseline methods, † indicates that the method’s result is significantly better than all baselines methods that use the aspect-based data only, with p<0.05 according to a one-tailed unpaired t-test. The data annotations S, N and A indicate training with Sentence-level, Noisy sentence-level and Aspect-level data respectively. Numbers for TDLSTM+Att,ATAE-LSTM,MM,RAM and LSTM+SynATT+TarRep are from He et al. (2018a). Numbers for Semisupervised are from He et al. (2018b).'\nProblem: 'Which method achieved the highest accuracy on the SemEval-16 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/54.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Average accuracies and Macro-F1 scores over five runs with random initialization along with their standard deviations. Bold: best results or within std of them. ∗ indicates that the method’s result is significantly better than all baseline methods, † indicates that the method’s result is significantly better than all baselines methods that use the aspect-based data only, with p<0.05 according to a one-tailed unpaired t-test. The data annotations S, N and A indicate training with Sentence-level, Noisy sentence-level and Aspect-level data respectively. Numbers for TDLSTM+Att,ATAE-LSTM,MM,RAM and LSTM+SynATT+TarRep are from He et al. (2018a). Numbers for Semisupervised are from He et al. (2018b)." ], "reasoning": "To find the highest accuracy on the SemEval-16 dataset, look at the 'SemEval-16 Acc.' column and identify the highest value. The highest accuracy is 87.68, achieved by the BiLSTM-XR-Dev Estimation method.", "question": "Which method achieved the highest accuracy on the SemEval-16 dataset?", "answer": "So, the answer is {\"answer\": \"BiLSTM-XR-Dev Estimation\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BiLSTM-XR method achieved the highest Macro-F1 score on the SemEval-15 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/54.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Average accuracies and Macro-F1 scores over five runs with random initialization along with their standard deviations. Bold: best results or within std of them. ∗ indicates that the method’s result is significantly better than all baseline methods, † indicates that the method’s result is significantly better than all baselines methods that use the aspect-based data only, with p<0.05 according to a one-tailed unpaired t-test. The data annotations S, N and A indicate training with Sentence-level, Noisy sentence-level and Aspect-level data respectively. Numbers for TDLSTM+Att,ATAE-LSTM,MM,RAM and LSTM+SynATT+TarRep are from He et al. (2018a). Numbers for Semisupervised are from He et al. (2018b)." ], "reasoning": "To verify this statement, look at the 'SemEval-15 Macro-F1' column. The highest score is 68.74, achieved by the Semisupervised method, not the BiLSTM-XR method.", "statement": "The BiLSTM-XR method achieved the highest Macro-F1 score on the SemEval-15 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Ablation Studies. Exact match (EM) and span F1 results on NewsQA test set of a BIDAF model finetuned with a 2-stage SynNet. In study A, we vary k, the number of mini-batches from SQuAD for every batch in NewsQA. In study B, we set k=0, and vary the answer type and how much of the paragraph we use for question synthesis. 2−sent refers to using two sentences before answer span, while all refers to using the entire paragraph. Aner refers to using an NER system and Aor refers to using the human-annotated answers to generate questions.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/55.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation Studies. Exact match (EM) and span F1 results on NewsQA test set of a BIDAF model finetuned with a 2-stage SynNet. In study A, we vary k, the number of mini-batches from SQuAD for every batch in NewsQA. In study B, we set k=0, and vary the answer type and how much of the paragraph we use for question synthesis. 2−sent refers to using two sentences before answer span, while all refers to using the entire paragraph. Aner refers to using an NER system and Aor refers to using the human-annotated answers to generate questions." ], "reasoning": "Using human-annotated answers significantly boosts performance, and copying the entire paragraph for question generation improves results.", "description": "conduct ablation studies (Table 4),. To better understand how various components in our training procedure and model impact overall performance we conduct several ablation studies, as summarized in Table 4.. Results in Table 4(A) show that using human-annotated answers to generate questions leads to a significant performance boost over using answers from an answer generation module.. This supports the hypothesis that the answers humans choose to generate questions for provide important linguistic cues for finetuning the machine comprehension model.. To see how copying impacts performance, we explore using the entire paragraph to generate the question vs. only the two sentences before and one sentence after the answer span and report results in Table 4(B)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Ablation Studies. Exact match (EM) and span F1 results on NewsQA test set of a BIDAF model finetuned with a 2-stage SynNet. In study A, we vary k, the number of mini-batches from SQuAD for every batch in NewsQA. In study B, we set k=0, and vary the answer type and how much of the paragraph we use for question synthesis. 2−sent refers to using two sentences before answer span, while all refers to using the entire paragraph. Aner refers to using an NER system and Aor refers to using the human-annotated answers to generate questions.'\nProblem: 'What is the F1 score when using all sentences and the answer oracle in Table 4(B)?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/55.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation Studies. Exact match (EM) and span F1 results on NewsQA test set of a BIDAF model finetuned with a 2-stage SynNet. In study A, we vary k, the number of mini-batches from SQuAD for every batch in NewsQA. In study B, we set k=0, and vary the answer type and how much of the paragraph we use for question synthesis. 2−sent refers to using two sentences before answer span, while all refers to using the entire paragraph. Aner refers to using an NER system and Aor refers to using the human-annotated answers to generate questions." ], "reasoning": "To find the F1 score for using all sentences and the answer oracle, look at the last row under Table 4(B) where 'all + Aoracle' is listed.", "question": "What is the F1 score when using all sentences and the answer oracle in Table?", "answer": { "answer": "46.8" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUsing human-annotated answers with k=4 results in the highest EM score in Table 4(A).\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/55.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation Studies. Exact match (EM) and span F1 results on NewsQA test set of a BIDAF model finetuned with a 2-stage SynNet. In study A, we vary k, the number of mini-batches from SQuAD for every batch in NewsQA. In study B, we set k=0, and vary the answer type and how much of the paragraph we use for question synthesis. 2−sent refers to using two sentences before answer span, while all refers to using the entire paragraph. Aner refers to using an NER system and Aor refers to using the human-annotated answers to generate questions." ], "reasoning": "To verify this, compare the EM scores for k=0, k=2, and k=4 in Table 4(A). The highest EM score is 30.4 for k=4.", "statement": "Using human-annotated answers with k=4 results in the highest EM score in Table 4(A).", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Validation BLEU for English-Spanish models with transfer learning. We use the final three models in our submission.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/56.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Validation BLEU for English-Spanish models with transfer learning. We use the final three models in our submission." ], "reasoning": "The table shows that transfer learning improves BLEU scores by 1-2 points in the all-biomed domain for both es2en and en2es models.", "description": "Table 2 gives single model validation scores for es2en and en2es models with standard and iterative transfer learning. We find that the all-biomed domain gains 1-2 BLEU points from transfer learning." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Validation BLEU for English-Spanish models with transfer learning. We use the final three models in our submission.'\nProblem: 'What is the highest BLEU score achieved by the es2en Bio model with transfer learning?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/56.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Validation BLEU for English-Spanish models with transfer learning. We use the final three models in our submission." ], "reasoning": "The highest BLEU score for the es2en Bio model with transfer learning is found in the row 'Health → All-biomed → Bio', which is 38.0.", "question": "What is the highest BLEU score achieved by the es2en Bio model with transfer learning?", "answer": "So, the answer is {\"answer\": \"38.0\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe en2es Khresmoi model achieves a BLEU score of 45.2 with transfer learning.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/56.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Validation BLEU for English-Spanish models with transfer learning. We use the final three models in our submission." ], "reasoning": "The table shows that the en2es Khresmoi model achieves a BLEU score of 45.2 in the 'Health → All-biomed → Health' transfer learning schedule.", "statement": "The en2es Khresmoi model achieves a BLEU score of 45.2 with transfer learning.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Validation and test BLEU for models used in English-Spanish language pair submissions.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/57.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Validation and test BLEU for models used in English-Spanish language pair submissions." ], "reasoning": "Uniform ensembling generally performs well, providing a 0.5-1.2 BLEU improvement over strong single models, with small gains from Bayesian Interpolation on most validation sets.", "description": "For de2en and es2en, uniform ensembling performs similarly to the oracles, and performs similarly to BI.. We submitted three runs to the WMT19 biomedical task for each language pair: the best single all-biomed model, a uniform ensemble of models on two en-de and three es-en domains, and an ensemble with Bayesian Interpolation. Tables 3 and 4 give validation and test scores.. that a uniform multi-domain ensemble performs well, giving 0.5-1.2 BLEU improvement on the test set over strong single models.. We see small gains from using BI with ensembles on most validation sets, but only on en2es test.. we noted that, in general, we could predict BI (α = 0.5) performance by comparing the uniform ensemble with the oracle model performing best on each validation domain.. For en2es uniform ensembling underperforms the health and bio oracle models on their validation sets, and the uniform ensemble slightly underperforms BI on the test data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Validation and test BLEU for models used in English-Spanish language pair submissions.'\nProblem: 'What is the BLEU score improvement of the uniform ensemble over the best single model on the es2en Test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/57.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Validation and test BLEU for models used in English-Spanish language pair submissions." ], "reasoning": "The best single model for es2en Test is 'Health → All-biomed' with a score of 42.4. The uniform ensemble score is 43.0. The improvement is 43.0 - 42.4 = 0.6 BLEU.", "question": "What is the BLEU score improvement of the uniform ensemble over the best single model on the es2en Test set?", "answer": "So, the answer is {\"answer\": \"0.6\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe uniform ensemble performs better than the BI ensemble on the en2es Test set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/57.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Validation and test BLEU for models used in English-Spanish language pair submissions." ], "reasoning": "The uniform ensemble score for en2es Test is 45.4, while the BI ensemble score is 45.6. Therefore, the uniform ensemble does not perform better than the BI ensemble on the en2es Test set.", "statement": "The uniform ensemble performs better than the BI ensemble on the en2es Test set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Validation and test BLEU for models used in English-German language pair submissions.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/58.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Validation and test BLEU for models used in English-German language pair submissions." ], "reasoning": "BI ensembles show small gains on most validation sets but only on en2es test, while uniform ensembling is consistently better for en2de on dev sets and outperforms BI on test data.", "description": "We see small gains from using BI with ensembles on most validation sets, but only on en2es test.. For en2de, by contrast, uniform ensembling is consistently better than oracles on the dev sets, and outperforms BI on the test data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Validation and test BLEU for models used in English-German language pair submissions.'\nProblem: 'Which method performs best on the en2de Khresmoi validation set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/58.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Validation and test BLEU for models used in English-German language pair submissions." ], "reasoning": "To determine the best performing method on the en2de Khresmoi validation set, compare the scores of News, News → All-biomed, Uniform ensemble, and BI ensemble. The highest score indicates the best performance.", "question": "Which method performs best on the en2de Khresmoi validation set?", "answer": "So, the answer is {\"answer\": \"Uniform ensemble\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBI ensemble performs better than Uniform ensemble on the de2en Khresmoi validation set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/58.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Validation and test BLEU for models used in English-German language pair submissions." ], "reasoning": "Compare the scores of BI ensemble and Uniform ensemble on the de2en Khresmoi validation set. BI ensemble has a score of 45.4, which is higher than Uniform ensemble's score of 45.3.", "statement": "BI ensemble performs better than Uniform ensemble on the de2en Khresmoi validation set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Comparing uniform ensembles and BI with varying smoothing factor on the WMT19 test data. Small deviations from official test scores on submitted runs are due to tokenization differences. α=0.5 was chosen for submission based on results on available development data.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/59.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparing uniform ensembles and BI with varying smoothing factor on the WMT19 test data. Small deviations from official test scores on submitted runs are due to tokenization differences. α=0.5 was chosen for submission based on results on available development data." ], "reasoning": "BI with α = 0.1 matches or outperforms the uniform ensemble, and for en2es, it performs equally well as BI with α = 0.5.", "description": "Consequently in Table 5 we experiment with BI (α = 0.1). In this case BI matches or out-performs the uniform ensemble. Notably, for en2es, where BI (α = 0.5) performed well, taking α = 0.1 does not harm performance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Comparing uniform ensembles and BI with varying smoothing factor on the WMT19 test data. Small deviations from official test scores on submitted runs are due to tokenization differences. α=0.5 was chosen for submission based on results on available development data.'\nProblem: 'Which configuration has the highest score for en2de?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/59.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparing uniform ensembles and BI with varying smoothing factor on the WMT19 test data. Small deviations from official test scores on submitted runs are due to tokenization differences. α=0.5 was chosen for submission based on results on available development data." ], "reasoning": "To find the highest score for en2de, compare the values in the en2de column for all configurations. The highest value is 26.0 for BI (α = 0.1).", "question": "Which configuration has the highest score for en2de?", "answer": "So, the answer is {\"answer\": \"26.0\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBI with α = 0.1 outperforms the uniform ensemble for de2en.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/59.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparing uniform ensembles and BI with varying smoothing factor on the WMT19 test data. Small deviations from official test scores on submitted runs are due to tokenization differences. α=0.5 was chosen for submission based on results on available development data." ], "reasoning": "Compare the scores for de2en between the uniform ensemble and BI with α = 0.1. The score for the uniform ensemble is 28.3, while the score for BI with α = 0.1 is 28.5, indicating that BI with α = 0.1 outperforms the uniform ensemble.", "statement": "BI with α = 0.1 outperforms the uniform ensemble for de2en.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: BLEU Scores of Data Sets'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/60.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: BLEU Scores of Data Sets" ], "reasoning": "The BLEU scores for the synthetic eScape data set are significantly lower compared to the real-life data sets (development and training sets).", "description": "We then tested the BLEU scores between machine translation results and corresponding gold standard post-editing results on the original development set, the training set and the synthetic data as shown in Table 1.. Table 1 shows that there is a significant gap between the synthetic eScape data set (Negri et al., 2018) and the real-life data sets (the development set and the original training set from posteditors)," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: BLEU Scores of Data Sets'\nProblem: 'What is the BLEU score difference between the synthetic eScape data set and the original training set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/60.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: BLEU Scores of Data Sets" ], "reasoning": "The BLEU score for the eScape data set is 37.68, and for the original training set, it is 77.42. The difference is calculated as 77.42 - 37.68.", "question": "What is the BLEU score difference between the synthetic eScape data set and the original training set?", "answer": { "answer": "39.74" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BLEU score for the development set is higher than the BLEU score for the training set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/60.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: BLEU Scores of Data Sets" ], "reasoning": "The BLEU score for the development set is 77.15, while the BLEU score for the training set is 77.42. Since 77.15 is not higher than 77.42, the statement is refuted.", "statement": "The BLEU score for the development set is higher than the BLEU score for the training set.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results on the Test Set'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/61.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the Test Set" ], "reasoning": "The ensemble of 5 models did not significantly improve BLEU scores compared to individual models.", "description": " Even the ensemble of 5 models did not result in significant differences especially in BLEU scores." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Results on the Test Set'\nProblem: 'Which model had the lowest TER score and what was its BLEU score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/61.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the Test Set" ], "reasoning": "By comparing the TER scores, the Ensemble x5 model had the lowest TER score of 16.77. Its BLEU score was 75.03.", "question": "Which model had the lowest TER score?", "answer": "So, the answer is {\"answer\": \"Ensemble x5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Gaussian model had a higher BLEU score than the MT as PE model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/61.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the Test Set" ], "reasoning": "The table shows that the BLEU score for the Gaussian model is 75.03, which is higher than the BLEU score of 74.73 for the MT as PE model.", "statement": "The Gaussian model had a higher BLEU score than the MT as PE model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: BLEU Scores on the Development Set'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/62.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU Scores on the Development Set" ], "reasoning": "The multi-source transformer (Base) model achieved the highest single model BLEU score without joint training, and the APE approach did not significantly improve BLEU scores.", "description": "Table 2 shows that the performance got slightly hurt (comparing \"Processed MT\" with \"MT as PE\") with pre-processing and post-processing procedures which are normally applied in training seq2seq models for reducing vocabulary size. The multi-source transformer (Base) model achieved the highest single model BLEU score without joint training with the de-noising encoder task.. Even with the ensembled model, our APE approach does not significantly improve machine translation outputs measured in BLEU (+0.46)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: BLEU Scores on the Development Set'\nProblem: 'Which model achieved the highest single model BLEU score without joint training?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/62.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU Scores on the Development Set" ], "reasoning": "The description states that the multi-source transformer (Base) model achieved the highest single model BLEU score without joint training, and the table shows the BLEU score for the Base model is 77.13.", "question": "Which model achieved the highest single model BLEU score without joint training?", "answer": { "answer": "Base" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe APE approach significantly improved machine translation outputs measured in BLEU.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/62.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU Scores on the Development Set" ], "reasoning": "The description states that the APE approach does not significantly improve machine translation outputs measured in BLEU, and the table shows a small increase of +0.46 in BLEU score.", "statement": "The APE approach significantly improved machine translation outputs measured in BLEU.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Variation across conditions for a range of metrics (defined in § 3.4). Bold indicates a statistically significant difference compared to the baseline at the 0.05 level, and a † indicates significance at the 0.01 level, both after applying the Holm–-Bonferroni method across each row Holm (1979).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/63.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Variation across conditions for a range of metrics (defined in § 3.4). Bold indicates a statistically significant difference compared to the baseline at the 0.05 level, and a † indicates significance at the 0.01 level, both after applying the Holm–-Bonferroni method across each row Holm (1979)." ], "reasoning": "Priming workers with lexical examples significantly improves correctness but reduces diversity, while changing incentives has little impact on metrics.", "description": "We consider variations in instructions, incentives, data domains, and workflows. We manually analyzed paraphrases for correctness, grammaticality, and linguistic diversity. Our observations provide new insight into the trade-offs between accuracy and diversity in crowd responses that arise as a result of task design,. Our analysis shows that the most important factor is how workers are primed for a task, with the choice of examples and the prompt sentence affecting diversity and correctness significantly.. There was relatively little variation in grammaticality or time across the conditions.. Priming had a major impact, with the shift to lexical examples leading to a significant improvement in correctness, but much lower diversity. The surprising increase in correctness when providing no examples. changing the incentives by providing either a bonus for novelty, or no bonus at all, did not substantially impact any of the metrics.. Changing the number of paraphrases written by each worker did not significantly impact diversity (we worried that collecting more than one may lead to a decrease).. the One Paraphrase condition did have lower grammaticality,. Changing the source of the prompt sentence to create a chain of paraphrases led to a significant increase in diversity.. showing the answer to the question being para. phrased did not significantly affect correctness or diversity," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Variation across conditions for a range of metrics (defined in § 3.4). Bold indicates a statistically significant difference compared to the baseline at the 0.05 level, and a † indicates significance at the 0.01 level, both after applying the Holm–-Bonferroni method across each row Holm (1979).'\nProblem: 'Which condition had the highest correctness and what was its impact on diversity?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/63.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Variation across conditions for a range of metrics (defined in § 3.4). Bold indicates a statistically significant difference compared to the baseline at the 0.05 level, and a † indicates significance at the 0.01 level, both after applying the Holm–-Bonferroni method across each row Holm (1979)." ], "reasoning": "The Lexical Examples condition had the highest correctness at 90%, but it led to a lower diversity distinct score of 93 and a diversity PINC score of 55.", "question": "Which condition had the highest correctness?", "answer": "So, the answer is {\"answer\": \"Lexical Examples\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Chain condition had the lowest grammaticality score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/63.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Variation across conditions for a range of metrics (defined in § 3.4). Bold indicates a statistically significant difference compared to the baseline at the 0.05 level, and a † indicates significance at the 0.01 level, both after applying the Holm–-Bonferroni method across each row Holm (1979)." ], "reasoning": "The table shows that the Chain condition had a grammaticality score of 89%, which is not the lowest. The Ubuntu condition had the lowest grammaticality score at 92%.", "statement": "The Chain condition had the lowest grammaticality score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Frame evaluation results on the triples from the FrameNet 1.7 corpus Baker et al. (1998). The results are sorted by the descending order of the Frame F1-score. Best results are boldfaced.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/64.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Frame evaluation results on the triples from the FrameNet 1.7 corpus Baker et al. (1998). The results are sorted by the descending order of the Frame F1-score. Best results are boldfaced." ], "reasoning": "Triframes based on WATSET clustering achieved the highest scores in both Verb F1 and overall Frame F1, outperforming other methods.", "description": "In Table 3 and Figure 1, the results of the experiment are presented. Triframes based on WATSET clustering outperformed the other methods on both Verb F1 and overall Frame F1.. the use of the WATSET fuzzy clustering algorithm that splits the hubs by disambiguating them leads to the best results (see Table 3)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Frame evaluation results on the triples from the FrameNet 1.7 corpus Baker et al. (1998). The results are sorted by the descending order of the Frame F1-score. Best results are boldfaced.'\nProblem: 'Which method achieved the highest score in Subject F1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/64.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Frame evaluation results on the triples from the FrameNet 1.7 corpus Baker et al. (1998). The results are sorted by the descending order of the Frame F1-score. Best results are boldfaced." ], "reasoning": "To determine the method with the highest score in Subject F1, we need to compare the Subject F1 scores of all methods listed in the table. The highest score is 66.61, achieved by NOAC Egunrov et al. (2017).", "question": "Which method achieved the highest score in Subject F1?", "answer": "So, the answer is {\"answer\": \"NOAC Egunrov et al. (2017)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTriadic k-Means achieved the highest score in Object F1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/64.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Frame evaluation results on the triples from the FrameNet 1.7 corpus Baker et al. (1998). The results are sorted by the descending order of the Frame F1-score. Best results are boldfaced." ], "reasoning": "To verify this statement, we need to compare the Object F1 scores of all methods listed in the table. The highest score in Object F1 is 67.18, achieved by NOAC Egunrov et al. (2017), not Triadic k-Means.", "statement": "Triadic k-Means achieved the highest score in Object F1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Evaluation results on the dataset of polysemous verb classes by Korhonen et al. (2003).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/65.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluation results on the dataset of polysemous verb classes by Korhonen et al. (2003)." ], "reasoning": "Table 4 shows that LDA-Frames achieved the highest F1-score on the second dataset, with our approach performing comparably.", "description": "Table 4 presents results on the second dataset for the best models identified on the first dataset. The LDA-Frames yielded the best results with our approach performing comparably in terms of the F1-score." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Evaluation results on the dataset of polysemous verb classes by Korhonen et al. (2003).'\nProblem: 'Which method had the highest niPU score and what was its F1-score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/65.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluation results on the dataset of polysemous verb classes by Korhonen et al. (2003)." ], "reasoning": "The method with the highest niPU score is 'Whole' with a score of 79.09. Its corresponding F1-score is 36.99.", "question": "Which method had the highest niPU score?", "answer": "So, the answer is {\"answer\": \"Whole\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Triadic k-Means method had a higher F1-score than the Triadic Spectral method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/65.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluation results on the dataset of polysemous verb classes by Korhonen et al. (2003)." ], "reasoning": "The F1-score for Triadic k-Means is 35.96, while the F1-score for Triadic Spectral is 36.86. Therefore, the statement is incorrect.", "statement": "The Triadic k-Means method had a higher F1-score than the Triadic Spectral method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: French-English Performance. Baseline indicates current state of the art performance.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/66.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: French-English Performance. Baseline indicates current state of the art performance." ], "reasoning": "Using global structure in the RR_FR_2step method significantly improves both Max F1 and 11-point IAP performance measures compared to the baseline.", "description": "Table 1 shows these performance measures for French-English,. using global structure greatly improves upon the state of the art baseline performance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: French-English Performance. Baseline indicates current state of the art performance.'\nProblem: 'What is the improvement in Max F1 from the Baseline to the RR_FR_2step method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/66.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: French-English Performance. Baseline indicates current state of the art performance." ], "reasoning": "To find the improvement in Max F1, subtract the Baseline Max F1 value (54.92) from the RR_FR_2step Max F1 value (69.72).", "question": "What is the improvement in Max F1 from the Baseline to the RR_FR_2step method?", "answer": "So, the answer is {\"answer\": \"14.8\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe RR_FR_1step method has a higher 11-point IAP than the RR method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/66.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: French-English Performance. Baseline indicates current state of the art performance." ], "reasoning": "The table shows that the 11-point IAP for the RR_FR_1step method is 64.42, while for the RR method it is 59.62, indicating that the statement is true.", "statement": "The RR_FR_1step method has a higher 11-point IAP than the RR method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: French-English Performance (large data). Baseline indicates state of the art performance.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/67.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: French-English Performance (large data). Baseline indicates state of the art performance." ], "reasoning": "The reverse rank and forward rank methods significantly improve performance over the baseline in large data conditions.", "description": "Tables 4 through 6 show the summary metrics for the three language pairs for the large data experiments.. We can see that the reverse rank and forward rank methods of taking into account the global structure of interactions among predictions is still helpful, providing large improvements in performance even in this challenging large data condition over strong state of the art baselines" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: French-English Performance (large data). Baseline indicates state of the art performance.'\nProblem: 'Which method achieved the highest Max F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/67.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: French-English Performance (large data). Baseline indicates state of the art performance." ], "reasoning": "By comparing the Max F1 scores of all methods, the RR_FR_1step method has the highest score of 65.87.", "question": "Which method achieved the highest Max F1 score?", "answer": "So, the answer is {\"answer\": \"RR_FR_1step\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe RR method outperforms the baseline in both Max F1 and 11-point IAP metrics.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/67.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: French-English Performance (large data). Baseline indicates state of the art performance." ], "reasoning": "The table shows that the RR method has a Max F1 score of 60.88 and an 11-point IAP of 58.79, both higher than the baseline scores of 55.08 and 51.35 respectively.", "statement": "The RR method outperforms the baseline in both Max F1 and 11-point IAP metrics.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: List of the target NER data sets and their specifications. Size is shown in number of tokens.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/68.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: List of the target NER data sets and their specifications. Size is shown in number of tokens." ], "reasoning": "The table lists six target NER data sets, each covering various fields and tenors, with details on their size, entity types, and descriptions.", "description": "five source and six target NER data sets, each selected to provide a range of fields (i.e., biology, computer science, medications, local business) and tenors (i.e., encyclopedia articles, journal articles, experimental protocols, online reviews).. We use five data sets as source data, covering a range of fields (i.e., clinical, biomedical, local business and Wiki with diverse fields) and tenors (i.e., popular reporting, notes, scholarly publications, online reviews and encyclopedia).. Details of these target data are listed in Table 2." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: List of the target NER data sets and their specifications. Size is shown in number of tokens.'\nProblem: 'Which data set has the largest size and what is its field?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/68.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: List of the target NER data sets and their specifications. Size is shown in number of tokens." ], "reasoning": "By examining the 'Size' column, the largest data set is JNLPBA with 593,590 entries. According to the 'Description' column, its field is biology.", "question": "Which data set has the largest size and what is its field?", "answer": { "answer": "JNLPBA" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ScienceIE data set covers the field of biology.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/68.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: List of the target NER data sets and their specifications. Size is shown in number of tokens." ], "reasoning": "The table shows that the ScienceIE data set covers Computer Science, Material Sciences, and Physics, not biology.", "statement": "The ScienceIE data set covers the field of biology.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Correlation coefficients between similarity measures and the effectiveness of pretrained models. The coefficients vary between -1 (negative correlation) and 1 (positive correlation). Zero means no correlation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/69.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Correlation coefficients between similarity measures and the effectiveness of pretrained models. The coefficients vary between -1 (negative correlation) and 1 (positive correlation). Zero means no correlation." ], "reasoning": "VCcR is the most informative factor in predicting the effectiveness of pretrained word vectors given a target data set, as shown by the highest value in the Word vectors column.", "description": "The results in Table 4 show that our proposed similarity measures are predictive of the effectiveness of the pretraining data.. VCcR is the most informative factor in predicting the effectiveness of pretrained word vectors given a target data set." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Correlation coefficients between similarity measures and the effectiveness of pretrained models. The coefficients vary between -1 (negative correlation) and 1 (positive correlation). Zero means no correlation.'\nProblem: 'Which similarity measure is the most predictive of the effectiveness of pretrained word vectors?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/69.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Correlation coefficients between similarity measures and the effectiveness of pretrained models. The coefficients vary between -1 (negative correlation) and 1 (positive correlation). Zero means no correlation." ], "reasoning": "The table shows that TVcC has the highest value (0.469) in the Word vectors column, indicating it is the most predictive measure.", "question": "Which similarity measure is the most predictive of the effectiveness of pretrained word vectors?", "answer": "So, the answer is {\"answer\": \"TVcC\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPPL is the least predictive measure for the effectiveness of pretrained word vectors.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/69.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Correlation coefficients between similarity measures and the effectiveness of pretrained models. The coefficients vary between -1 (negative correlation) and 1 (positive correlation). Zero means no correlation." ], "reasoning": "The table shows that PPL has the lowest value (-0.398) in the Word vectors column, indicating it is the least predictive measure.", "statement": "PPL is the least predictive measure for the effectiveness of pretrained word vectors.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Comparison between our best performance pretrained models and the publicly available ones, which are pretrained on much larger corpora.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/70.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison between our best performance pretrained models and the publicly available ones, which are pretrained on much larger corpora." ], "reasoning": "On JNLPBA, ScienceIE, and WetLab, LMs pretrained on small similar sources perform better, while word vectors pretrained on small similar sources perform better on CRAFT, JNLPBA, and ScienceIE.", "description": "We find that word vectors and LMs pretrained on small similar sources can achieve competitive or even better performance than the ones pretrained on larger sources (Table 5).. On JNLPBA, ScienceIE and Wetlab, LMs pretrained on the small similar source perform better, while word vectors pretrained on the small similar source perform better on CRAFT, JNLPBA, and ScienceIE." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Comparison between our best performance pretrained models and the publicly available ones, which are pretrained on much larger corpora.'\nProblem: 'Which model performs best on the ScienceIE dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/70.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison between our best performance pretrained models and the publicly available ones, which are pretrained on much larger corpora." ], "reasoning": "To determine the best-performing model on the ScienceIE dataset, compare the scores of all models listed in the table for ScienceIE. The highest score is 42.07, achieved by LMs Ours.", "question": "Which model performs best on the ScienceIE dataset?", "answer": "So, the answer is {\"answer\": \"LMs Ours\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nWord vectors pretrained on small similar sources perform better on the CRAFT dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/70.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison between our best performance pretrained models and the publicly available ones, which are pretrained on much larger corpora." ], "reasoning": "The table shows that word vectors pretrained on small similar sources (Word vectors Ours) have a score of 75.45 on the CRAFT dataset, which is higher than the score of 74.22 for Word vectors GloVe.", "statement": "Word vectors pretrained on small similar sources perform better on the CRAFT dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Impact of hyper-parameter setting on the effectiveness of pretrained word vectors. ‘Opt’ is hyper-parameter setting proposed in (Chiu et al., 2016), whereas ‘Def’ is the default setting in word2vec.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/71.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Impact of hyper-parameter setting on the effectiveness of pretrained word vectors. ‘Opt’ is hyper-parameter setting proposed in (Chiu et al., 2016), whereas ‘Def’ is the default setting in word2vec." ], "reasoning": "The optimized hyper-parameter setting generally outperforms the default setting except for the Wiki-ScienceIE and MIMIC-WetLab pairs.", "description": "Our results suggest that this hyper-parameter setting can overall (except Wiki-ScienceIE and MIMIC-WetLab pairs) produce better performance compare to the default setting (Table 6)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Impact of hyper-parameter setting on the effectiveness of pretrained word vectors. ‘Opt’ is hyper-parameter setting proposed in (Chiu et al., 2016), whereas ‘Def’ is the default setting in word2vec.'\nProblem: 'Which dataset pair shows the highest improvement in performance with the optimized setting compared to the default setting?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/71.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Impact of hyper-parameter setting on the effectiveness of pretrained word vectors. ‘Opt’ is hyper-parameter setting proposed in (Chiu et al., 2016), whereas ‘Def’ is the default setting in word2vec." ], "reasoning": "To find the highest improvement, compare the differences between the optimized and default settings for each dataset pair. The largest positive difference indicates the highest improvement.", "question": "Which dataset pair shows the highest improvement in performance with the optimized setting compared to the default setting?", "answer": "So, the answer is {\"answer\": \"PubMed-ScienceIE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe optimized setting improves performance for the Yelp-WetLab pair.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/71.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Impact of hyper-parameter setting on the effectiveness of pretrained word vectors. ‘Opt’ is hyper-parameter setting proposed in (Chiu et al., 2016), whereas ‘Def’ is the default setting in word2vec." ], "reasoning": "Compare the performance of the default and optimized settings for the Yelp-WetLab pair. The optimized setting (78.78) is higher than the default setting (78.48), indicating an improvement.", "statement": "The optimized setting improves performance for the Yelp-WetLab pair.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Types of discrepancy in context-agnostic translation caused by deixis (excluding anaphora)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/72.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Types of discrepancy in context-agnostic translation caused by deixis (excluding anaphora)" ], "reasoning": "The most frequent error category related to deixis in the annotated corpus is the inconsistency of T-V forms when translating second person pronouns, accounting for 67% of errors.", "description": "Most errors in our annotated corpus are related to person deixis, specifically gender marking in the Russian translation, and the T-V distinction between informal and formal you (Latin \"tu\" and \"vos\").. From Table 3, we see that the most frequent error category related to deixis in our annotated corpus is the inconsistency of T-V forms when translating second person pronouns." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Types of discrepancy in context-agnostic translation caused by deixis (excluding anaphora)'\nProblem: 'What percentage of errors in the annotated corpus are related to the T-V distinction?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/72.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Types of discrepancy in context-agnostic translation caused by deixis (excluding anaphora)" ], "reasoning": "The table shows the frequency of different types of discrepancies. The T-V distinction has the highest frequency at 67%.", "question": "What percentage of errors in the annotated corpus are related to the T-V distinction?", "answer": "So, the answer is {\"answer\": \"67\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe most frequent error category in the annotated corpus is related to speaker/addressee gender.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/72.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Types of discrepancy in context-agnostic translation caused by deixis (excluding anaphora)" ], "reasoning": "The table shows that the T-V distinction has the highest frequency of errors at 67%, while speaker/addressee gender errors are lower at 22% and 9%.", "statement": "The most frequent error category in the annotated corpus is related to speaker/addressee gender.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Evaluation on subsets of THYME Dev (in F-measure). The subsets of Event×Event (EE) and Timex3×Event (TE) relation pairs are of sizes 3.3k and 2.7k respectively. The intervals 0-100, 100-500 and 500+ are subsets reflecting average argument token frequency in the training data (of sizes 2.2k, 2.2k and 1.8k respectively).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/73.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation on subsets of THYME Dev (in F-measure). The subsets of Event×Event (EE) and Timex3×Event (TE) relation pairs are of sizes 3.3k and 2.7k respectively. The intervals 0-100, 100-500 and 500+ are subsets reflecting average argument token frequency in the training data (of sizes 2.2k, 2.2k and 1.8k respectively)." ], "reasoning": "The combined loss models (SG or SGLR) improve EE relation recognition but slightly reduce TE relation performance compared to pre-trained embeddings.", "description": "In Table 1 we can see that also for our model, EE relations are harder to recognize than the TE relations, as all models achieve higher scores for TE compared to EE relations.. What is interesting to see is that when training with the combined loss (SG or SGLR) we obtain a clear improvement on the more difficult EE relations, and perform slightly worse on TE relations compared to using pre-trained embeddings (the three upper settings).. What can be observed is that the RC+SG model performs best for low-frequency words, and RC+SGLR performs. best for the higher frequency ranges.. When evaluating on the full Dev set, both combined loss settings outperform the baselines consistently." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Evaluation on subsets of THYME Dev (in F-measure). The subsets of Event×Event (EE) and Timex3×Event (TE) relation pairs are of sizes 3.3k and 2.7k respectively. The intervals 0-100, 100-500 and 500+ are subsets reflecting average argument token frequency in the training data (of sizes 2.2k, 2.2k and 1.8k respectively).'\nProblem: 'Which model performs best for low-frequency words and how does it compare to the best model for high-frequency words?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/73.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation on subsets of THYME Dev (in F-measure). The subsets of Event×Event (EE) and Timex3×Event (TE) relation pairs are of sizes 3.3k and 2.7k respectively. The intervals 0-100, 100-500 and 500+ are subsets reflecting average argument token frequency in the training data (of sizes 2.2k, 2.2k and 1.8k respectively)." ], "reasoning": "The RC+SG model performs best for low-frequency words (0-100) with a score of 46.4, while the RC+SGLR model performs best for high-frequency words (500+) with a score of 68.1.", "question": "Which model performs best for low-frequency words?", "answer": "So, the answer is {\"answer\": \"RC+SG\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe RC+SG model achieves the highest score for EE relations.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/73.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation on subsets of THYME Dev (in F-measure). The subsets of Event×Event (EE) and Timex3×Event (TE) relation pairs are of sizes 3.3k and 2.7k respectively. The intervals 0-100, 100-500 and 500+ are subsets reflecting average argument token frequency in the training data (of sizes 2.2k, 2.2k and 1.8k respectively)." ], "reasoning": "The table shows that the RC+SGLR model achieves the highest score for EE relations with a score of 51.7, not the RC+SG model.", "statement": "The RC+SG model achieves the highest score for EE relations.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: THYME test set results, reporting precision (P), recall (R) and F-measure (F), macro-averaged over three runs. The standard deviation for F is also given.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/74.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: THYME test set results, reporting precision (P), recall (R) and F-measure (F), macro-averaged over three runs. The standard deviation for F is also given." ], "reasoning": "Initializing the model with pre-trained embeddings improves F-measure by 1.1 points compared to random initialization, and fixing the embeddings further improves it by 0.9 points. Adding the SGLR loss increases F-measure by 1.6 points over fixed embeddings, and the SG loss extension yields the best results with a 1.9 point gain.", "description": "Table 2 shows that initializing the model with the pre-trained embeddings gives a significant 4 1.1 point increase in F-measure compared to random initialization, due to an increase in precision.. Fixing the embeddings gives slightly better performance than using them as initialization, an increase of 0.9 point in F-measure, mostly due to higher recall.. When extending the loss with the SGLR loss, we gain6 1.6 in F-measure compared to fixing the word embeddings, and also surpass the state of the art by 0.4 even without specialized resources.. If we train our model using the SG loss extension we obtain the best results, and gain6 1.9 points in F-measure compared to using pre-trained fixed word embeddings.. This setting also exceeds the state of the art (Lin et al., 2017) by 0.7 points in F-measure, due to a gain of 1.2 points in recall, again without using any specialized clinical NLP tools for feature engineering, in contrast to all state-of-the-art baselines." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: THYME test set results, reporting precision (P), recall (R) and F-measure (F), macro-averaged over three runs. The standard deviation for F is also given.'\nProblem: 'What is the F-measure improvement when using the SG loss extension compared to pre-trained fixed word embeddings?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/74.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: THYME test set results, reporting precision (P), recall (R) and F-measure (F), macro-averaged over three runs. The standard deviation for F is also given." ], "reasoning": "The F-measure for RC (SG fixed) is 60.9, and for RC + SG it is 62.8. The improvement is 62.8 - 60.9 = 1.9 points.", "question": "What is the F-measure improvement when using the SG loss extension compared to pre-trained fixed word embeddings?", "answer": { "answer": "1.9" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUsing the SG loss extension surpasses the state of the art by 0.7 points in F-measure.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/74.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: THYME test set results, reporting precision (P), recall (R) and F-measure (F), macro-averaged over three runs. The standard deviation for F is also given." ], "reasoning": "The F-measure for RC + SG is 62.8, which is 0.7 points higher than the state-of-the-art F-measure of 62.1 by Lin et al. (2017).", "statement": "Using the SG loss extension surpasses the state of the art by 0.7 points in F-measure.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Error analysis on 50 FP and 50 FN (random from test) for different settings. Clause boundaries are: newlines and sub-clause or sentence boundaries. Error categories are not mutually exclusive.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/75.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Error analysis on 50 FP and 50 FN (random from test) for different settings. Clause boundaries are: newlines and sub-clause or sentence boundaries. Error categories are not mutually exclusive." ], "reasoning": "All models struggle with distant relations crossing sentence or clause boundaries, and frequent arguments are a dominant error category. RC+SG has fewer errors for infrequent arguments.", "description": "From Table 3 we can see that all models have difficulties with distant relations that cross sentence or clause boundaries (CCR).. Furthermore, arguments that are frequent in the supervised data (> 250) are a dominant error category.. Furthermore it can be noticed that RC+SG has less errors for infrequent arguments (< 10) in the supervised data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Error analysis on 50 FP and 50 FN (random from test) for different settings. Clause boundaries are: newlines and sub-clause or sentence boundaries. Error categories are not mutually exclusive.'\nProblem: 'Which model has the least errors for infrequent arguments (<10) and how many errors does it have?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/75.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Error analysis on 50 FP and 50 FN (random from test) for different settings. Clause boundaries are: newlines and sub-clause or sentence boundaries. Error categories are not mutually exclusive." ], "reasoning": "The table shows the number of errors for infrequent arguments (<10) for three models. RC+SG has 11 errors, RC (SG fixed) has 15 errors, and RC (SG init.) has 26 errors. Therefore, RC+SG has the least errors for infrequent arguments.", "question": "Which model has the least errors for infrequent arguments (<10)?", "answer": "So, the answer is {\"answer\": \"RC+SG\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRC (SG fixed) has the most errors for frequent arguments (>250).\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/75.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Error analysis on 50 FP and 50 FN (random from test) for different settings. Clause boundaries are: newlines and sub-clause or sentence boundaries. Error categories are not mutually exclusive." ], "reasoning": "The table shows the number of errors for frequent arguments (>250) for three models. RC (SG fixed) has 50 errors, RC+SG has 37 errors, and RC (SG init.) has 40 errors. Therefore, RC (SG fixed) has the most errors for frequent arguments.", "statement": "RC (SG fixed) has the most errors for frequent arguments (>250).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results for all the models on the three datasets in our experiment. Marked with ∗ are the results which significantly improve over LING and LING+random (p<0.05, also for the following results); ⋄ indicates a significant improvement over LING+PV; † a significant improvement over LING+N2V.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/76.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results for all the models on the three datasets in our experiment. Marked with ∗ are the results which significantly improve over LING and LING+random (p<0.05, also for the following results); ⋄ indicates a significant improvement over LING+PV; † a significant improvement over LING+N2V." ], "reasoning": "The results show that social information improves performance on Stance and Hate Speech detection but has no effect on Sentiment Analysis. LING+N2V outperforms LING+PV for Stance detection, while their performance is comparable for Hate Speech detection.", "description": "In Table 2 we report the results, that we compute as the average of ten runs with random parameter initialization.9 We use the unpaired Welch's t test to check for statistically significant difference between models.. The results show that social information helps improve the performance on Stance and Hate Speech detection, while it has no effect for Sentiment Analysis.. LING+random never improves over LING:. We find that both PV and N2V user representations lead to an improvement over LING.. where LING+N2V outperforms LING+PV,. while for Hate Speech the performance of the two models is comparable (the difference between LING+PV and LING+N2V is not statistically significative due to the high variance of the LING+PV results - see extended results table in the supplementary material).. our model outperforms any other model on both Stance and Hate Speech detection." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results for all the models on the three datasets in our experiment. Marked with ∗ are the results which significantly improve over LING and LING+random (p<0.05, also for the following results); ⋄ indicates a significant improvement over LING+PV; † a significant improvement over LING+N2V.'\nProblem: 'Which model shows the highest performance for Stance detection?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/76.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results for all the models on the three datasets in our experiment. Marked with ∗ are the results which significantly improve over LING and LING+random (p<0.05, also for the following results); ⋄ indicates a significant improvement over LING+PV; † a significant improvement over LING+N2V." ], "reasoning": "By comparing the Stance detection scores in the table, LING+GAT has the highest score of 0.640.", "question": "Which model shows the highest performance for Stance detection?", "answer": "So, the answer is {\"answer\": \"LING+GAT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nLING+random improves over LING for Sentiment Analysis.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/76.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results for all the models on the three datasets in our experiment. Marked with ∗ are the results which significantly improve over LING and LING+random (p<0.05, also for the following results); ⋄ indicates a significant improvement over LING+PV; † a significant improvement over LING+N2V." ], "reasoning": "The table shows that LING has a Sentiment score of 0.676, while LING+random has a lower score of 0.657, indicating that LING+random does not improve over LING for Sentiment Analysis.", "statement": "LING+random improves over LING for Sentiment Analysis.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Overview of annotated parallel sentences per language pair'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/77.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Overview of annotated parallel sentences per language pair" ], "reasoning": "The table provides the number of annotated parallel sentences for various language pairs, with the highest count for EN-ES and the lowest for EN-BG.", "description": "An overview of the language pairs as well as the amount of annotated parallel sentences per language pair is given in Table 1.. Overview of annotated parallel sentences per language pair" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Overview of annotated parallel sentences per language pair'\nProblem: 'Which language pair has the highest number of annotated parallel sentences?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/77.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Overview of annotated parallel sentences per language pair" ], "reasoning": "To find the language pair with the highest number of annotated parallel sentences, compare the values in the '# sents' columns. EN-ES has the highest count with 1,419,507 sentences.", "question": "Which language pair has the highest number of annotated parallel sentences?", "answer": "So, the answer is {\"answer\": \"EN-ES\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nEN-RO has more annotated parallel sentences than EN-LV.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/77.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Overview of annotated parallel sentences per language pair" ], "reasoning": "The table shows that EN-RO has 303,396 annotated parallel sentences, while EN-LV has 487,287. Therefore, EN-RO has fewer sentences than EN-LV.", "statement": "EN-RO has more annotated parallel sentences than EN-LV.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Types of discrepancy in context-agnostic translation caused by ellipsis'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/78.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Types of discrepancy in context-agnostic translation caused by ellipsis" ], "reasoning": "The two most frequent types of ambiguity caused by elliptical structures are wrong morphological form and wrong verb (VP-ellipsis), with frequencies of 66% and 20% respectively.", "description": "We classified ellipsis examples which lead to errors in sentence-level translations by the type of error they cause. Results are provided in Table 4.. From Table 4, we see that the two most frequent types of ambiguity caused by the presence of an elliptical structure have different nature, hence we construct individual test sets for each of them." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Types of discrepancy in context-agnostic translation caused by ellipsis'\nProblem: 'What is the combined frequency of wrong morphological form and wrong verb (VP-ellipsis) discrepancies?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/78.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Types of discrepancy in context-agnostic translation caused by ellipsis" ], "reasoning": "The table shows that wrong morphological form has a frequency of 66% and wrong verb (VP-ellipsis) has a frequency of 20%. Adding these two frequencies together gives the combined frequency.", "question": "What is the combined frequency of wrong morphological form and wrong verb (VP-ellipsis) discrepancies?", "answer": "So, the answer is {\"answer\": \"86\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nWrong morphological form is the least frequent type of discrepancy.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/78.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Types of discrepancy in context-agnostic translation caused by ellipsis" ], "reasoning": "The table shows that wrong morphological form has a frequency of 66%, which is higher than the frequencies of wrong verb (20%) and other error (14%). Therefore, wrong morphological form is not the least frequent type of discrepancy.", "statement": "Wrong morphological form is the least frequent type of discrepancy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Results. Dev Acc. represents accuracy on SST-2 dev set. F - M represents difference between means of predicted positive class probabilities for sentences with female nouns and sentences with male nouns. ** denotes statistical significance with p<0.01 (after applying Bonferroni correction).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/79.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results. Dev Acc. represents accuracy on SST-2 dev set. F - M represents difference between means of predicted positive class probabilities for sentences with female nouns and sentences with male nouns. ** denotes statistical significance with p<0.01 (after applying Bonferroni correction)." ], "reasoning": "M.1 and M.2 show higher predicted positive class probabilities for sentences with female nouns, while M.3 shows higher predicted positive class probabilities for sentences with male nouns.", "description": "The main results of our experiments are shown in Table 1.. We notice that M.1 (Bag-of-words + Logistic Regression) and M.2 (BiLSTM) show a statistically significant difference between the two genders, with higher predicted positive class probabilities for sentences with female nouns.. On the contrary, M.3 (BERT) shows that sentences with male nouns have a statistically significant higher predicted positive class probability than sentences with female nouns." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Results. Dev Acc. represents accuracy on SST-2 dev set. F - M represents difference between means of predicted positive class probabilities for sentences with female nouns and sentences with male nouns. ** denotes statistical significance with p<0.01 (after applying Bonferroni correction).'\nProblem: 'Which model shows the highest development accuracy and what is the gender bias direction?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/79.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results. Dev Acc. represents accuracy on SST-2 dev set. F - M represents difference between means of predicted positive class probabilities for sentences with female nouns and sentences with male nouns. ** denotes statistical significance with p<0.01 (after applying Bonferroni correction)." ], "reasoning": "M.3 (BERT) shows the highest development accuracy of 0.930 and has a statistically significant higher predicted positive class probability for sentences with male nouns.", "question": "Which model shows the highest development accuracy?", "answer": "So, the answer is {\"answer\": \"M.3 (BERT)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nM.2 (BiLSTM) has a higher development accuracy than M.1 (BoW+LogReg).\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/79.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results. Dev Acc. represents accuracy on SST-2 dev set. F - M represents difference between means of predicted positive class probabilities for sentences with female nouns and sentences with male nouns. ** denotes statistical significance with p<0.01 (after applying Bonferroni correction)." ], "reasoning": "The table shows that M.2 (BiLSTM) has a development accuracy of 0.841, which is higher than M.1 (BoW+LogReg) with a development accuracy of 0.827.", "statement": "M.2 (BiLSTM) has a higher development accuracy than M.1 (BoW+LogReg).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Average cosine similarity score and Delta-E distance over 5 runs. A smaller Delta-E distance means a less significant difference between two colors. Bold: best performance. Hard: the hard ensemble model. WM18∗: the performance from WM18 paper. See Supplementary Material for example outputs and ensemble analysis.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/80.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Average cosine similarity score and Delta-E distance over 5 runs. A smaller Delta-E distance means a less significant difference between two colors. Bold: best performance. Hard: the hard ensemble model. WM18∗: the performance from WM18 paper. See Supplementary Material for example outputs and ensemble analysis." ], "reasoning": "The RGB model outperforms WM18 under all conditions, while the HSV model is superior for most test conditions according to cosine similarity. However, the RGB model and ensemble perform better according to Delta-E.", "description": "Because of the small size of the dataset, we report the average performance over 5 runs with different random seeds.. Table 1 shows the results. Compared with WM18, our RGB model outperforms under all conditions.. According to the cosine similarity, the HSV model is superior for most test conditions (confirming our hypothesis about simpler modifier behaviour in this space). However for Delta-E, the RGB model and ensemble perform better. Unlike cosine, Delta-E is sensitive to differences in vector length, and we would argue it is the most appropriate metric because lengths are critical to measuring the extent of lightness and darkness of colors. Accordingly the HSV model does worse under this metric, as it more directly models the direction of color modifiers, but as a consequence this leads to errors in its length predictions. Over- all the ensemble does well according to both met rics, and has the best performance for several test conditions with Delta-E." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Average cosine similarity score and Delta-E distance over 5 runs. A smaller Delta-E distance means a less significant difference between two colors. Bold: best performance. Hard: the hard ensemble model. WM18∗: the performance from WM18 paper. See Supplementary Material for example outputs and ensemble analysis.'\nProblem: 'Which model has the best overall performance according to Delta-E?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/80.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Average cosine similarity score and Delta-E distance over 5 runs. A smaller Delta-E distance means a less significant difference between two colors. Bold: best performance. Hard: the hard ensemble model. WM18∗: the performance from WM18 paper. See Supplementary Material for example outputs and ensemble analysis." ], "reasoning": "To determine the best overall performance according to Delta-E, we need to compare the 'Overall' Delta-E values for each model. The RGB model has a Delta-E of 5.412, WM18 has 5.595, HSV has 7.487, and the ensemble has 5.777. The RGB model has the lowest Delta-E value, indicating the best performance.", "question": "Which model has the best overall performance according to Delta-E?", "answer": "So, the answer is {\"answer\": \"RGB\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe HSV model has the best performance for unseen pairings according to cosine similarity.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/80.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Average cosine similarity score and Delta-E distance over 5 runs. A smaller Delta-E distance means a less significant difference between two colors. Bold: best performance. Hard: the hard ensemble model. WM18∗: the performance from WM18 paper. See Supplementary Material for example outputs and ensemble analysis." ], "reasoning": "To verify this statement, we need to check the 'Unseen Pairings' row under the 'Cosine Similarity' columns. The HSV model has a cosine similarity of 0.843, which is higher than RGB (0.799), WM18 (0.771), and the ensemble (0.797). Therefore, the statement is supported.", "statement": "The HSV model has the best performance for unseen pairings according to cosine similarity.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: BLEU scores. CADec trained with p=0.5. Scores for CADec are not statistically different from the baseline (6m).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/81.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: BLEU scores. CADec trained with p=0.5. Scores for CADec are not statistically different from the baseline (6m)." ], "reasoning": "Our model's BLEU score is comparable to the baseline despite using less data, while the concatenation baseline performs worse, and CADec's performance remains consistent regardless of context sentences.", "description": "BLEU scores for our model and the baselines are given in Table 6.5 For context-aware models, all sentences in a group were translated, and then only the current sentence is evaluated. We also report BLEU for the context-agnostic baseline trained only on 1.5m dataset to show how the performance is influenced by the amount of data. We observe that our model is no worse in BLEU than the baseline despite the second-pass model. being trained only on a fraction of the data. In contrast, the concatenation baseline, trained on a mixture of data with and without context is about 1 BLEU below the context-agnostic baseline and our model when using all 3 context sentences. CADec's performance remains the same independently from the number of context sentences (1, 2 or 3) as measured with BLEU. s-hier-to-2.tied performs worst in terms of BLEU, but note that this is a shallow recurrent model, while others are Transformer-based. It also suffers from the asymmetric data setting, like the concatenation baseline." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: BLEU scores. CADec trained with p=0.5. Scores for CADec are not statistically different from the baseline (6m).'\nProblem: 'Which model has the lowest BLEU score and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/81.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: BLEU scores. CADec trained with p=0.5. Scores for CADec are not statistically different from the baseline (6m)." ], "reasoning": "By examining the BLEU scores in the table, the model with the lowest BLEU score is s-hier-to-2.tied with a value of 26.68.", "question": "Which model has the lowest BLEU score?", "answer": "Thus, the answer is {\"answer\": \"s-hier-to-2.tied\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe CADec model performs better than the baseline (6m) model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/81.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: BLEU scores. CADec trained with p=0.5. Scores for CADec are not statistically different from the baseline (6m)." ], "reasoning": "The table shows that the BLEU score for the CADec model is 32.38, which is slightly lower than the baseline (6m) model's score of 32.40.", "statement": "The CADec model performs better than the baseline (6m) model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: F&C dataset size. All Labels represent the original dataset with all the labels. Subset Labels are the subset labels which are inferable by the resource.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/82.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F&C dataset size. All Labels represent the original dataset with all the labels. Subset Labels are the subset labels which are inferable by the resource." ], "reasoning": "The NO-LEAK F&C split sizes are 712 for Train, 3,000 for Dev, and 4,497 for Test, totaling 8,209.", "description": "we reorganized the train/dev/test sets, forming new splits, which we refer to as NO-LEAK F&C. The new split sizes can be found in Table 2. We re-ran the current models on NO-LEAK F&C and, as expected, we observe a drop of 5-6% in accuracy: from the original 76% accuracy on the dev/test sets, to 70% and 71% accuracy, respectively." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: F&C dataset size. All Labels represent the original dataset with all the labels. Subset Labels are the subset labels which are inferable by the resource.'\nProblem: 'What is the total number of instances in the Subset Labels Clean F&C split?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/82.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F&C dataset size. All Labels represent the original dataset with all the labels. Subset Labels are the subset labels which are inferable by the resource." ], "reasoning": "To find the total number of instances in the Subset Labels Clean F&C split, sum the Train, Dev, and Test instances: 173 + 1,268 + 1,523.", "question": "What is the total number of instances in the Subset Labels Clean F&C split?", "answer": "So, the answer is {\"answer\": \"2,964\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe total number of instances in the Orig F&C split for Subset Labels is 7,322.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/82.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F&C dataset size. All Labels represent the original dataset with all the labels. Subset Labels are the subset labels which are inferable by the resource." ], "reasoning": "The table shows that the Orig F&C split for Subset Labels has 361 Train, 3,311 Dev, and 3,650 Test instances, which sum to 7,322.", "statement": "The total number of instances in the Orig F&C split for Subset Labels is 7,322.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Results on the noun comparison datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/83.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on the noun comparison datasets." ], "reasoning": "The table shows that the new dataset is more challenging, with lower accuracy scores compared to the cleaned Forbes and Choi dataset.", "description": "The left column of Table 4 presents results for the cleaned version of the Forbes and Choi (2017) dataset.. Results on the new objects comparison dataset we created are shown in the rightmost column of Table 4.. We get better results than previous methods on this dataset: 63% and 61% accuracy on the dev/test sets compared to 60% and 57%. These relatively low results on this new dataset indicate that it is more challenging." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Results on the noun comparison datasets.'\nProblem: 'Which model achieved the highest accuracy on the F&C Clean Test dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/83.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on the noun comparison datasets." ], "reasoning": "By comparing the accuracy scores on the F&C Clean Test dataset, the highest score is 0.87 achieved by Yang et al. (PCE LSTM).", "question": "Which model achieved the highest accuracy on the F&C Clean Test dataset?", "answer": "So, the answer is {\"answer\": \"Yang et al. (PCE LSTM)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe DoQ model achieved the highest accuracy on the New Data Dev dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/83.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on the noun comparison datasets." ], "reasoning": "The table shows that the DoQ model and its variations (DoQ + 10-distance and DoQ + 3-distance) all achieved the same highest accuracy of 0.62 on the New Data Dev dataset.", "statement": "The DoQ model achieved the highest accuracy on the New Data Dev dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results on the Relative dataset. Yang et al. (2018) result was achieved by running their model on their training set, and using it as a transfer method on Relative. Finally, we present our own predictions, with different thresholds, which surpass previous work.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/84.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on the Relative dataset. Yang et al. (2018) result was achieved by running their model on their training set, and using it as a transfer method on Relative. Finally, we present our own predictions, with different thresholds, which surpass previous work." ], "reasoning": "The method by Yang et al. (2018) achieved 85.8% accuracy, surpassing the previous best score of 83.5% by Bagherinezhad et al. (2016). Our method achieved a new state-of-the-art result of 87.7% accuracy.", "description": "noun comparatives is on RELATIVE (Bagherinezhad et al., 2016), presented in Table 5.. We report the results of the original work, where the best score used a combination of visual and textual signals, achieving 83.5% accuracy. We also tested the method by Yang et al. (2018) on this dataset.. The accuracy achieved by this method is 85.8%, surpassing the previous method by more than 2 points. We evaluated our method on this dataset, achieving a new state-of-the-art result of 87.7% accuracy with k = 10 as a filter method." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Results on the Relative dataset. Yang et al. (2018) result was achieved by running their model on their training set, and using it as a transfer method on Relative. Finally, we present our own predictions, with different thresholds, which surpass previous work.'\nProblem: 'Which method achieved the highest accuracy on the RELATIVE dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/84.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on the Relative dataset. Yang et al. (2018) result was achieved by running their model on their training set, and using it as a transfer method on Relative. Finally, we present our own predictions, with different thresholds, which surpass previous work." ], "reasoning": "The table shows various methods and their accuracies. The highest accuracy is 0.877, achieved by the 'DoQ + 10-distance' method.", "question": "Which method achieved the highest accuracy on the RELATIVE dataset?", "answer": "So, the answer is {\"answer\": \"DoQ + 10-distance\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe method by Yang et al. (2018) achieved the highest accuracy on the RELATIVE dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/84.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on the Relative dataset. Yang et al. (2018) result was achieved by running their model on their training set, and using it as a transfer method on Relative. Finally, we present our own predictions, with different thresholds, which surpass previous work." ], "reasoning": "The table shows that the highest accuracy is 0.877, achieved by the 'DoQ + 10-distance' method, not by Yang et al. (2018), which achieved 0.858.", "statement": "The method by Yang et al. (2018) achieved the highest accuracy on the RELATIVE dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Intrinsic Evaluation. Accuracy of the number of objects which our proposed median fall into range of the object, given the dimension.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/85.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Intrinsic Evaluation. Accuracy of the number of objects which our proposed median fall into range of the object, given the dimension." ], "reasoning": "The total agreement score is 69%, with specific agreements for MASS, LENGTH, SPEED, and CURRENCY being 61%, 79%, 77%, and 58% respectively. Re-annotation of the currency category by U.S. annotators resulted in a higher agreement score of 76%.", "description": "The results of the intrinsic evaluation on a sample of DOQ are shown in Table 7. The total agreement is 69%, while the specific agreements for MASS, LENGTH, SPEED and CURRENCY are 61%, 79%, 77% and 58% respectively.. We re-annotated the samples in the currency category with annotators from the U.S. and found a much higher agreement score: 76%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Intrinsic Evaluation. Accuracy of the number of objects which our proposed median fall into range of the object, given the dimension.'\nProblem: 'What is the difference in agreement scores for the currency category between Indian and U.S. annotators?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/85.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Intrinsic Evaluation. Accuracy of the number of objects which our proposed median fall into range of the object, given the dimension." ], "reasoning": "The agreement score for the currency category by Indian annotators is 58%, while the score by U.S. annotators is 76%. The difference is calculated as 76% - 58%.", "question": "What is the difference in agreement scores for the currency category between Indian and U.S. annotators?", "answer": "So, the answer is {\"answer\": \"18\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe agreement score for the length category is higher than that for the speed category.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/85.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Intrinsic Evaluation. Accuracy of the number of objects which our proposed median fall into range of the object, given the dimension." ], "reasoning": "According to the table, the agreement score for the length category is 79%, while for the speed category it is 77%. Since 79% is higher than 77%, the statement is true.", "statement": "The agreement score for the length category is higher than that for the speed category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Significant components of our logistic regression model using the Coh-Metrix features. Variables are also separated by their association with either satire or fake news. Bold: the remaining features following the step-wise backward elimination. Note: *** p < 0.001, ** p < 0.01, * p < 0.05.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/86.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Significant components of our logistic regression model using the Coh-Metrix features. Variables are also separated by their association with either satire or fake news. Bold: the remaining features following the step-wise backward elimination. Note: *** p < 0.001, ** p < 0.01, * p < 0.05." ], "reasoning": "Significant features in the logistic regression model include both surface level features like sentence length and semantic features like LSA overlaps, with satire articles being more sophisticated than fake news articles.", "description": "as our dependent variable. Significant features of the logistic regression model are shown in Table 1 with the respective significance levels. We also run a step-wise backward elimination regression.. Those components that are also significant in the step-wise model appear in bold.. Observing the significant features, in bold in Table 1, we see a combination of surface level related features, such as sentence length and average word frequency, as well as semantic features including LSA (Latent Semantic Analysis) overlaps between verbs and between adjacent sentences. Semantic features which are associated with the gist representation of content are particularly interesting to see among the predictors since based on Fuzzytrace theory (Reyna, 2012), a well-known theory of decision making under risk, gist representation of content drives individual's decision to spread misinformation online. Also among the significant features, we observe the causal connectives, that are proven to be important in text comprehension, and two indices related to the text easability and readability, both suggesting that satire articles are more sophisticated, or less easy to read, than fake news articles." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Significant components of our logistic regression model using the Coh-Metrix features. Variables are also separated by their association with either satire or fake news. Bold: the remaining features following the step-wise backward elimination. Note: *** p < 0.001, ** p < 0.01, * p < 0.05.'\nProblem: 'Which feature has the highest estimate value for satire associated articles?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/86.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Significant components of our logistic regression model using the Coh-Metrix features. Variables are also separated by their association with either satire or fake news. Bold: the remaining features following the step-wise backward elimination. Note: *** p < 0.001, ** p < 0.01, * p < 0.05." ], "reasoning": "By examining the 'estimate' column for satire associated articles, the highest value is 1.80 for the feature 'First person singular pronoun incidence'.", "question": "Which feature has the highest estimate value for satire associated articles?", "answer": "So, the answer is {\"answer\": \"First person singular pronoun incidence\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe feature 'Adverb incidence' is associated with satire articles.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/86.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Significant components of our logistic regression model using the Coh-Metrix features. Variables are also separated by their association with either satire or fake news. Bold: the remaining features following the step-wise backward elimination. Note: *** p < 0.001, ** p < 0.01, * p < 0.05." ], "reasoning": "The table shows that 'Adverb incidence' (RC59) is associated with fake news articles, not satire articles.", "statement": "The feature 'Adverb incidence' is associated with satire articles.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results of classification between fake news and satire articles using BERT pre-trained models, based on the headline, body and full text. Bold: best performing model. P: Precision, and R: Recall'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/87.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of classification between fake news and satire articles using BERT pre-trained models, based on the headline, body and full text. Bold: best performing model. P: Precision, and R: Recall" ], "reasoning": "The model based on the full text of headline and body gives the best performance, with the highest F1 score, combining the strengths of both precision and recall.", "description": "text of a story, and in combination. Results are shown in Table 2. The models based on the headline and text body give a similar F1 score. However, while the headline model performs poorly on precision, perhaps due to the short text, the model based on the text body performs poorly on recall. The model based on the full text of headline and body gives the best performance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results of classification between fake news and satire articles using BERT pre-trained models, based on the headline, body and full text. Bold: best performing model. P: Precision, and R: Recall'\nProblem: 'Which model has the highest precision?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/87.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of classification between fake news and satire articles using BERT pre-trained models, based on the headline, body and full text. Bold: best performing model. P: Precision, and R: Recall" ], "reasoning": "By comparing the precision (P) values in the table, the model based on the headline and text body has the highest precision of 0.81.", "question": "Which model has the highest precision?", "answer": { "answer": "Headline + text body" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model based on the text body only has the highest recall.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/87.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of classification between fake news and satire articles using BERT pre-trained models, based on the headline, body and full text. Bold: best performing model. P: Precision, and R: Recall" ], "reasoning": "The table shows that the model based on the headline only has the highest recall (R) value of 0.89, not the text body only model.", "statement": "The model based on the text body only has the highest recall.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Accuracy for deixis and lexical cohesion.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/88.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Accuracy for deixis and lexical cohesion." ], "reasoning": "For deixis, both concat and CADec models show significant improvements over the baseline, with concat improving by 33.5 percentage points and CADec by 31.6 percentage points. For lexical cohesion, CADec shows a notable improvement of 12.2 percentage points over the baseline, while concat performs similarly to the baseline.", "description": "For all tasks, we observe a large improvement from using context. For deixis, the concatenation model (concat) and CADec improve over the baseline by 33.5 and 31.6 percentage points, respectively. On the lexical cohesion test set, CADec shows a large improvement over the context-agnostic baseline (12.2 percentage points), while concat performs similarly to the baseline.. When looking only at the scores where the latest relevant context is in the model's context window (column 2 in Table 7), s-hier-to-2.tied outperforms the concatenation baseline for lexical cohesion, but remains behind the performance of CADec." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Accuracy for deixis and lexical cohesion.'\nProblem: 'Which model shows the highest improvement over the baseline for deixis when considering the latest relevant context in the 1st column?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/88.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Accuracy for deixis and lexical cohesion." ], "reasoning": "To determine the highest improvement, compare the scores of all models in the 'latest relevant context 1st' column for deixis. The concat model has the highest score of 88.8, which is an improvement over the baseline score of 50.0.", "question": "Which model shows the highest improvement over the baseline for deixis when considering the latest relevant context in the 1st column?", "answer": { "answer": "concat" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe s-hier-to-2.tied model outperforms the baseline for lexical cohesion in the latest relevant context 1st column.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/88.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Accuracy for deixis and lexical cohesion." ], "reasoning": "In the 'latest relevant context 1st' column for lexical cohesion, the s-hier-to-2.tied model has a score of 53.0, which is higher than the baseline score of 46.1.", "statement": "The s-hier-to-2.tied model outperforms the baseline for lexical cohesion in the latest relevant context 1st column.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Summary of results of classification between fake news and satire articles using the baseline Multinomial Naive Bayes method, the linguistic cues of text coherence and semantic representation with a pre-trained BERT model. Statistically significant differences with the baseline are marked with ’*’. Bold: best performing model. P: Precision, and R: Recall. For Coh-Metrix, we report the mean Precision, Recall, and F1 on the test set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/89.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Summary of results of classification between fake news and satire articles using the baseline Multinomial Naive Bayes method, the linguistic cues of text coherence and semantic representation with a pre-trained BERT model. Statistically significant differences with the baseline are marked with ’*’. Bold: best performing model. P: Precision, and R: Recall. For Coh-Metrix, we report the mean Precision, Recall, and F1 on the test set." ], "reasoning": "Both BERT and Coh-Metrix significantly outperform the baseline on the F1 score, with BERT achieving the best result.", "description": "Table 3 provides a summary of the results. We compare the results of our methods of the pre-trained BERT, using both the headline and text body, and the Coh-Mertix approach, to the language-based baseline with Multinomial Naive Bayes from (Golbeck et al., 2018)2. Both the semantic cues with BERT and the linguistic cues with Coh-Metrix significantly outperform the baseline on the F1 score. The two-tailed paired t-test with a 0.05 significance level was used for testing statistical significance of performance differences. The best result is given by the BERT model. Overall, these results provide an answer to research question RQ1 regarding the existence of semantic and linguistic difference between fake news and satire." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Summary of results of classification between fake news and satire articles using the baseline Multinomial Naive Bayes method, the linguistic cues of text coherence and semantic representation with a pre-trained BERT model. Statistically significant differences with the baseline are marked with ’*’. Bold: best performing model. P: Precision, and R: Recall. For Coh-Metrix, we report the mean Precision, Recall, and F1 on the test set.'\nProblem: 'Which method achieved the highest F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/89.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Summary of results of classification between fake news and satire articles using the baseline Multinomial Naive Bayes method, the linguistic cues of text coherence and semantic representation with a pre-trained BERT model. Statistically significant differences with the baseline are marked with ’*’. Bold: best performing model. P: Precision, and R: Recall. For Coh-Metrix, we report the mean Precision, Recall, and F1 on the test set." ], "reasoning": "The table shows the F1 scores for Baseline, Coh-Metrix, and Pre-trained BERT. The highest F1 score is 0.78, achieved by Pre-trained BERT.", "question": "Which method achieved the highest F1 score?", "answer": "So, the answer is {\"answer\": \"Pre-trained BERT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Baseline method achieved the highest precision.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/89.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Summary of results of classification between fake news and satire articles using the baseline Multinomial Naive Bayes method, the linguistic cues of text coherence and semantic representation with a pre-trained BERT model. Statistically significant differences with the baseline are marked with ’*’. Bold: best performing model. P: Precision, and R: Recall. For Coh-Metrix, we report the mean Precision, Recall, and F1 on the test set." ], "reasoning": "The table shows the precision (P) scores for Baseline, Coh-Metrix, and Pre-trained BERT. The highest precision score is 0.81, achieved by Pre-trained BERT, not the Baseline method.", "statement": "The Baseline method achieved the highest precision.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: F1 scores on AIDA-B (test set).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/90.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: F1 scores on AIDA-B (test set)." ], "reasoning": "The ment-norm model achieves the highest micro F1 score on AIDA-B, outperforming all other methods, including rel-norm and previous state-of-the-art methods.", "description": "Table 1 shows micro F1 scores on AIDA-B of the SOTA methods and ours, which all use Wikipedia and YAGO mention-entity index. To our knowledge, ours are the only (unsupervisedly) inducing and employing more than one relations on this dataset. The others use only one relation, coreference, which is given by simple heuristics or supervised third-party resolvers. All four our models outperform any previous method, with ment-norm achieving the best results, 0.85% higher than that of Ganea and Hofmann (2017).. The experimental results show that ment-norm outperforms rel-norm, and that mention padding plays an important role." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: F1 scores on AIDA-B (test set).'\nProblem: 'Which method achieved the highest micro F1 score on AIDA-B and by how much did it outperform Ganea and Hofmann (2017)?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/90.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: F1 scores on AIDA-B (test set)." ], "reasoning": "The table shows that the ment-norm method achieved the highest micro F1 score on AIDA-B with a score of 93.07±0.27. According to the description, ment-norm outperformed Ganea and Hofmann (2017) by 0.85%.", "question": "Which method achieved the highest micro F1 score on AIDA-B?", "answer": "So, the answer is {\"answer\": \"ment-norm\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ment-norm model achieved a micro F1 score of 93.07 on AIDA-B.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/90.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: F1 scores on AIDA-B (test set)." ], "reasoning": "The table shows that the ment-norm model achieved a micro F1 score of 93.07±0.27 on AIDA-B.", "statement": "The ment-norm model achieved a micro F1 score of 93.07 on AIDA-B.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: F1 scores on five out-domain test sets. Underlined scores show cases where the corresponding model outperforms the baseline.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/91.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F1 scores on five out-domain test sets. Underlined scores show cases where the corresponding model outperforms the baseline." ], "reasoning": "Ment-norm achieves the highest F1 scores on MSNBC and ACE2004, and on average, its F1 score is 0.3% higher than Ganea and Hofmann (2017) but 0.2% lower than Guo and Barbosa (2016).", "description": "Table 2 shows micro F1 scores on 5 out-domain test sets. Besides ours, only Cheng and Roth (2013) employs several mention relations. Mentnorm achieves the highest F1 scores on MSNBC and ACE2004. On average, ment-norm's F1 score is 0.3% higher than that of Ganea and Hofmann (2017), but 0.2% lower than Guo and Barbosa (2016)'s. It is worth noting that Guo and Barbosa (2016) performs exceptionally well on WIKI, but substantially worse than ment-norm on all other datasets. Our other three models, however, have lower average F1 scores compared to the best previous model.. The experimental results show that ment-norm outperforms rel-norm, and that mention padding plays an important role." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: F1 scores on five out-domain test sets. Underlined scores show cases where the corresponding model outperforms the baseline.'\nProblem: 'Which method has the highest average F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/91.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F1 scores on five out-domain test sets. Underlined scores show cases where the corresponding model outperforms the baseline." ], "reasoning": "By examining the 'Avg' column in the table, we see that 'guorobust' has the highest average F1 score of 85.7.", "question": "Which method has the highest average F1 score?", "answer": { "answer": "guorobust" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMent-norm achieves the highest F1 score on AQUAINT.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/91.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F1 scores on five out-domain test sets. Underlined scores show cases where the corresponding model outperforms the baseline." ], "reasoning": "The table shows that 'cheng-roth:2013:EMNLP' achieves the highest F1 score on AQUAINT with a score of 90, not ment-norm.", "statement": "Ment-norm achieves the highest F1 score on AQUAINT.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Performance of the proposed LSTM-based variants with the traditional cross-validation setup. Due to overlapping sub-dialogues in the train and test sets, the performance of the LSTM-based models achieve unrealistically high performance.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/92.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of the proposed LSTM-based variants with the traditional cross-validation setup. Due to overlapping sub-dialogues in the train and test sets, the performance of the LSTM-based models achieve unrealistically high performance." ], "reasoning": "The results in Table 2 show that BiLSTM and LSTM models have the highest performance metrics, with BiLSTM achieving the highest ρ value.", "description": "The results are presented in Table 2.. the results in Table 2 show very high performance, which is likely to further increase with ongoing training." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Performance of the proposed LSTM-based variants with the traditional cross-validation setup. Due to overlapping sub-dialogues in the train and test sets, the performance of the LSTM-based models achieve unrealistically high performance.'\nProblem: 'Which model has the highest ρ value and what is it?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/92.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of the proposed LSTM-based variants with the traditional cross-validation setup. Due to overlapping sub-dialogues in the train and test sets, the performance of the LSTM-based models achieve unrealistically high performance." ], "reasoning": "By examining the ρ column, the BiLSTM model has the highest ρ value of 0.92.", "question": "Which model has the highest ρ value and what is it?", "answer": "So, the answer is {\"answer\": \"0.92\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LSTM+att model has the highest UAR value.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/92.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of the proposed LSTM-based variants with the traditional cross-validation setup. Due to overlapping sub-dialogues in the train and test sets, the performance of the LSTM-based models achieve unrealistically high performance." ], "reasoning": "By examining the UAR column, the LSTM+att model has a UAR value of 0.74, which is not the highest. Both LSTM and BiLSTM have higher UAR values of 0.78.", "statement": "The LSTM+att model has the highest UAR value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: Accuracy on ellipsis test set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/93.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Accuracy on ellipsis test set." ], "reasoning": "Both models, concat and CADec, show significant improvements over the baseline, with concat excelling in inflection tasks and CADec in VP ellipsis tasks.", "description": "For ellipsis, both models improve substantially over the baseline (by 19-51 percentage points), with concat stronger for inflection tasks and CADec stronger for VPellipsis." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 8: Accuracy on ellipsis test set.'\nProblem: 'Which model shows the highest improvement in VP ellipsis tasks compared to the baseline?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/93.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Accuracy on ellipsis test set." ], "reasoning": "To find the model with the highest improvement in VP ellipsis tasks, compare the VP ellipsis scores of all models with the baseline score of 28.4. CADec has the highest score of 80.0.", "question": "Which model shows the highest improvement in VP ellipsis tasks compared to the baseline?", "answer": "So, the answer is {\"answer\": \"CADec\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe concat model shows the highest improvement in inflection tasks.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/93.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Accuracy on ellipsis test set." ], "reasoning": "To verify this statement, compare the inflection scores of all models. The concat model has the highest inflection score of 76.2, which is higher than the other models.", "statement": "The concat model shows the highest improvement in inflection tasks.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Performance of the proposed LSTM-based variants with the dialogue-wise cross-validation setup. The models by Rach et al. (2017) and Ultes et al. (2015) have been re-implemented. The BiLSTM with attention mechanism performs best in all evaluation metrics.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/94.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of the proposed LSTM-based variants with the dialogue-wise cross-validation setup. The models by Rach et al. (2017) and Ultes et al. (2015) have been re-implemented. The BiLSTM with attention mechanism performs best in all evaluation metrics." ], "reasoning": "The table shows the absolute improvement of UAR and eA measures over the SVM-based approach for different models, with BiLSTM+att achieving the highest values.", "description": "All results of these experiments are presented in Table 3 with the absolute improvement of the two main measures UAR and eA over the SVM-based approach" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Performance of the proposed LSTM-based variants with the dialogue-wise cross-validation setup. The models by Rach et al. (2017) and Ultes et al. (2015) have been re-implemented. The BiLSTM with attention mechanism performs best in all evaluation metrics.'\nProblem: 'Which model achieved the highest UAR and eA values?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/94.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of the proposed LSTM-based variants with the dialogue-wise cross-validation setup. The models by Rach et al. (2017) and Ultes et al. (2015) have been re-implemented. The BiLSTM with attention mechanism performs best in all evaluation metrics." ], "reasoning": "By examining the table, the BiLSTM+att model has the highest UAR value of 0.54 and the highest eA value of 0.94.", "question": "Which model achieved the highest UAR and eA values?", "answer": "So, the answer is {\"answer\": \"BiLSTM+att\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LSTM model achieved a UAR value of 0.51.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/94.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of the proposed LSTM-based variants with the dialogue-wise cross-validation setup. The models by Rach et al. (2017) and Ultes et al. (2015) have been re-implemented. The BiLSTM with attention mechanism performs best in all evaluation metrics." ], "reasoning": "The table shows that the LSTM model has a UAR value of 0.51.", "statement": "The LSTM model achieved a UAR value of 0.51.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Evaluation of our models for NER performance with our dataset. We report F1-measure results over the test portion of our dataset averaged over 10 replications of the training with the same hyper parameters.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/95.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation of our models for NER performance with our dataset. We report F1-measure results over the test portion of our dataset averaged over 10 replications of the training with the same hyper parameters." ], "reasoning": "The mean NER performance increases in joint models, with the best results achieved by ext_m_feat. However, the difference between ext_m_feat and j_multi is not statistically significant.", "description": "The results are shown in Table 1. We see that the mean NER performance increases in joint models.. As one can see from the table, it achieved the best results compared to our joint models. However, we cannot confirm the difference between EXT M FEAT and J MULTI models as the calculated p is well above .05." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Evaluation of our models for NER performance with our dataset. We report F1-measure results over the test portion of our dataset averaged over 10 replications of the training with the same hyper parameters.'\nProblem: 'Which model achieved the highest mean F1-measure in this work?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/95.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation of our models for NER performance with our dataset. We report F1-measure results over the test portion of our dataset averaged over 10 replications of the training with the same hyper parameters." ], "reasoning": "By examining the table, we see that the ext_m_feat model achieved the highest mean F1-measure with a value of 83.47.", "question": "Which model achieved the highest mean F1-measure in this work?", "answer": "So, the answer is {\"answer\": \"ext_m_feat\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe j_multi model achieved the highest mean F1-measure in this work.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/95.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation of our models for NER performance with our dataset. We report F1-measure results over the test portion of our dataset averaged over 10 replications of the training with the same hyper parameters." ], "reasoning": "The table shows that the ext_m_feat model achieved the highest mean F1-measure with a value of 83.47, while the j_multi model achieved a mean F1-measure of 83.21.", "statement": "The j_multi model achieved the highest mean F1-measure in this work.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Evaluation of our models for MD performance. As in the NER evaluation, we report accuracies over the test dataset averaged over 10 replications of the training.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/96.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation of our models for MD performance. As in the NER evaluation, we report accuracies over the test dataset averaged over 10 replications of the training." ], "reasoning": "Despite training with fewer parameters, our MD performance is close to the state-of-the-art, although joint learning did not enhance MD performance as it did for NER.", "description": "As can be seen from Table 2, we are very close to the state of the. art MD performance even if we only trained with a low number of parameters as stated in the beginning of this section. We have to also note that in contrast with the NER task, the MD task did not enjoy a performance increase from joint learning." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Evaluation of our models for MD performance. As in the NER evaluation, we report accuracies over the test dataset averaged over 10 replications of the training.'\nProblem: 'What is the mean accuracy of the MD model in this work?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/96.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation of our models for MD performance. As in the NER evaluation, we report accuracies over the test dataset averaged over 10 replications of the training." ], "reasoning": "The table lists the mean accuracy of various models, including the MD model, which has a mean accuracy of 88.61.", "question": "What is the mean accuracy of the MD model in this work?", "answer": "So, the answer is {\"answer\": \"88.61\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe MD model in this work achieved a mean accuracy higher than 89.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/96.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation of our models for MD performance. As in the NER evaluation, we report accuracies over the test dataset averaged over 10 replications of the training." ], "reasoning": "The table shows that the MD model in this work has a mean accuracy of 88.61, which is lower than 89.", "statement": "The MD model in this work achieved a mean accuracy higher than 89.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Performance of our models (bottom four) along with the state-of-the-art baseline models (top four). Baseline results for fastText, GloVe are obtained from Romanov and Shivade (2018). Adding knowledge graph information to the base models showed an absolute improvement of 4.97% in case of fastText and 1.36% in case of GloVe. The baseline model utilizing BioELMo as base embeddings Jin et al. (2019) showed an accuracy of 78.2%. On adding knowledge graph information, we were able to improve these results to 78.76% and on further addition of sentiment information, the accuracy rose to 79.04%'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/97.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of our models (bottom four) along with the state-of-the-art baseline models (top four). Baseline results for fastText, GloVe are obtained from Romanov and Shivade (2018). Adding knowledge graph information to the base models showed an absolute improvement of 4.97% in case of fastText and 1.36% in case of GloVe. The baseline model utilizing BioELMo as base embeddings Jin et al. (2019) showed an accuracy of 78.2%. On adding knowledge graph information, we were able to improve these results to 78.76% and on further addition of sentiment information, the accuracy rose to 79.04%" ], "reasoning": "The table shows that incorporating knowledge graph embeddings improves model performance, with BioELMo w/ KG+Sentiment achieving the highest accuracy of 79.04%.", "description": "We report accuracy as the performance metric. Table 1 represents the performance comparison of our proposed models and the baselines, which shows that incorporation of knowledge graph embeddings helps to improve the model performance.. All results are summarized in Table 1." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Performance of our models (bottom four) along with the state-of-the-art baseline models (top four). Baseline results for fastText, GloVe are obtained from Romanov and Shivade (2018). Adding knowledge graph information to the base models showed an absolute improvement of 4.97% in case of fastText and 1.36% in case of GloVe. The baseline model utilizing BioELMo as base embeddings Jin et al. (2019) showed an accuracy of 78.2%. On adding knowledge graph information, we were able to improve these results to 78.76% and on further addition of sentiment information, the accuracy rose to 79.04%'\nProblem: 'Which model achieved the highest accuracy and what was the accuracy percentage?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/97.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of our models (bottom four) along with the state-of-the-art baseline models (top four). Baseline results for fastText, GloVe are obtained from Romanov and Shivade (2018). Adding knowledge graph information to the base models showed an absolute improvement of 4.97% in case of fastText and 1.36% in case of GloVe. The baseline model utilizing BioELMo as base embeddings Jin et al. (2019) showed an accuracy of 78.2%. On adding knowledge graph information, we were able to improve these results to 78.76% and on further addition of sentiment information, the accuracy rose to 79.04%" ], "reasoning": "By comparing the accuracy percentages of all models listed in the table, BioELMo w/ KG+Sentiment has the highest accuracy at 79.04%.", "question": "Which model achieved the highest accuracy?", "answer": "So, the answer is {\"answer\": \"BioELMo w/ KG+Sentiment\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model GloVe w/ KG+Sentiment achieved an accuracy of 74.46%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/97.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of our models (bottom four) along with the state-of-the-art baseline models (top four). Baseline results for fastText, GloVe are obtained from Romanov and Shivade (2018). Adding knowledge graph information to the base models showed an absolute improvement of 4.97% in case of fastText and 1.36% in case of GloVe. The baseline model utilizing BioELMo as base embeddings Jin et al. (2019) showed an accuracy of 78.2%. On adding knowledge graph information, we were able to improve these results to 78.76% and on further addition of sentiment information, the accuracy rose to 79.04%" ], "reasoning": "The table lists the accuracy of GloVe w/ KG+Sentiment as 74.46%, which matches the statement.", "statement": "The model GloVe w/ KG+Sentiment achieved an accuracy of 74.46%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 9: Results for different probabilities of using corrupted reference at training time. BLEU for 3 context sentences. For ellipsis, we show inflection/VP scores.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/98.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Results for different probabilities of using corrupted reference at training time. BLEU for 3 context sentences. For ellipsis, we show inflection/VP scores." ], "reasoning": "The denoising process improves almost all tasks except for deixis, with the largest improvement seen in lexical cohesion.", "description": "Results for different values of p are given in Table 9. All models have about the same BLEU, not statistically significantly different from the baseline, but they are quite different in terms of incorporating context. The denoising positively influences almost all tasks except for deixis, yielding the largest improvement on lexical cohesion." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 9: Results for different probabilities of using corrupted reference at training time. BLEU for 3 context sentences. For ellipsis, we show inflection/VP scores.'\nProblem: 'Which task shows the largest improvement when denoising is applied?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/98.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Results for different probabilities of using corrupted reference at training time. BLEU for 3 context sentences. For ellipsis, we show inflection/VP scores." ], "reasoning": "The table shows that the highest BLEU is achieved when p value is 0.75.", "question": "Which p value achieved the highest BLEU?", "answer": "So, the answer is {\"answer\": \"0.75\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BLEU score significantly improves with denoising.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/98.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Results for different probabilities of using corrupted reference at training time. BLEU for 3 context sentences. For ellipsis, we show inflection/VP scores." ], "reasoning": "The description states that all models have about the same BLEU, not statistically significantly different from the baseline. The table shows BLEU scores ranging from 32.31 to 32.45, which are very close to each other.", "statement": "The BLEU score significantly improves with denoising.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: The accuracy of the traditional classifier in Phase 2 given documents from seen classes only.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/99.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The accuracy of the traditional classifier in Phase 2 given documents from seen classes only." ], "reasoning": "The table shows that the traditional CNN classifier achieved high accuracy rates across different datasets and unseen rates, with the highest accuracy being 0.993 for DBpedia 50%.", "description": "The purpose of Table 5 is to show that the traditional CNN classifier in Phase 2 was highly accurate." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: The accuracy of the traditional classifier in Phase 2 given documents from seen classes only.'\nProblem: 'What is the accuracy of the traditional CNN classifier for the 20news dataset with a 50% unseen rate?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/99.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The accuracy of the traditional classifier in Phase 2 given documents from seen classes only." ], "reasoning": "The table lists the accuracy rates for different datasets and unseen rates. For the 20news dataset with a 50% unseen rate, the accuracy is 0.878.", "question": "What is the accuracy of the traditional CNN classifier for the 20news dataset with a 50% unseen rate?", "answer": "So, the answer is {\"answer\": \"0.878\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe traditional CNN classifier achieved an accuracy of 0.861 for the DBpedia dataset with a 25% unseen rate.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/99.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The accuracy of the traditional classifier in Phase 2 given documents from seen classes only." ], "reasoning": "The table shows that the accuracy for the DBpedia dataset with a 25% unseen rate is 0.992, not 0.861.", "statement": "The traditional CNN classifier achieved an accuracy of 0.861 for the DBpedia dataset with a 25% unseen rate.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Rank-1 recognition accuracies on the UND-X1 dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/300.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Rank-1 recognition accuracies on the UND-X1 dataset" ], "reasoning": "The proposed method with Up Convolution + DoG Filter outperforms DPM on the UND-X1 dataset in all gallery settings, showing a 3.5% improvement in accuracy.", "description": "Table II shows that our proposed method outperforms DPM on the UND-X1 dataset in all the gallery settings. We improved the state-of-the-art by an absolute increase of 14% in accuracy on the Carl dataset and by 3.5% on the UND-X1 dataset when all the samples of the subjects are used in the gallery." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Rank-1 recognition accuracies on the UND-X1 dataset'\nProblem: 'What is the accuracy improvement of the proposed method over DPM on the UND-X1 dataset when all samples of the subjects are used in the gallery?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/300.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Rank-1 recognition accuracies on the UND-X1 dataset" ], "reasoning": "The table shows that the accuracy of the proposed method with Up Convolution + DoG Filter is 87.2%, while DPM has an accuracy of 83.73%. The improvement is calculated as 87.2% - 83.73% = 3.47%.", "question": "What is the accuracy improvement of the proposed method over Deep Perceptual Mapping on the UND-X1 dataset when all samples of the subjects are used in the gallery?", "answer": "So, the answer is {\"answer\": \"3.47\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed method with Up Convolution + DoG Filter has the highest accuracy on the UND-X1 dataset when all samples of the subjects are used in the gallery.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/300.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Rank-1 recognition accuracies on the UND-X1 dataset" ], "reasoning": "The table shows that the proposed method with Up Convolution + DoG Filter has an accuracy of 87.2%, which is higher than all other models listed, including DPM, which has an accuracy of 83.73%.", "statement": "The proposed method with Up Convolution + DoG Filter has the highest accuracy on the UND-X1 dataset when all samples of the subjects are used in the gallery.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Mean time to generate 200 samples in scenes from each dataset, benchmarked on a computer with a 2.7 GHz Intel Core i5 CPU and 8 GB of RAM. Speedup factors are indicated in brackets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/301.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Mean time to generate 200 samples in scenes from each dataset, benchmarked on a computer with a 2.7 GHz Intel Core i5 CPU and 8 GB of RAM. Speedup factors are indicated in brackets." ], "reasoning": "Our methods are significantly faster than SGAN, and the zbest configuration is slightly faster than the full configuration.", "description": "The results are summarized in Table 2.. Our methods are significantly faster to sample from than. SGAN. Further, our hypothesis that the zbest configuration will be slightly faster holds true." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Mean time to generate 200 samples in scenes from each dataset, benchmarked on a computer with a 2.7 GHz Intel Core i5 CPU and 8 GB of RAM. Speedup factors are indicated in brackets.'\nProblem: 'Which dataset shows the highest speedup factor when using 'Ours (Full)' compared to SGAN?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/301.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Mean time to generate 200 samples in scenes from each dataset, benchmarked on a computer with a 2.7 GHz Intel Core i5 CPU and 8 GB of RAM. Speedup factors are indicated in brackets." ], "reasoning": "To determine the highest speedup factor, compare the speedup factors for 'Ours (Full)' across all datasets. The Hotel dataset has the highest speedup factor of 81x.", "question": "Which dataset shows the highest speedup factor when using 'Ours (Full)' compared to SGAN?", "answer": "So, the answer is {\"answer\": \"Hotel\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Univ dataset has a speedup factor of 24x when using 'Ours (zbest)'.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/301.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Mean time to generate 200 samples in scenes from each dataset, benchmarked on a computer with a 2.7 GHz Intel Core i5 CPU and 8 GB of RAM. Speedup factors are indicated in brackets." ], "reasoning": "The table shows that the Univ dataset has a speedup factor of 24x when using 'Ours (zbest)'.", "statement": "The Univ dataset has a speedup factor of 24x when using 'Ours (zbest)'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: This table reports the mean PSNR and the mean SSIM for some configurations of the loss function, the use of our attention mechanism, and the network architecture. Default configuration represents the model that we use to compare with the state-of-the-art literature.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/302.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: This table reports the mean PSNR and the mean SSIM for some configurations of the loss function, the use of our attention mechanism, and the network architecture. Default configuration represents the model that we use to compare with the state-of-the-art literature." ], "reasoning": "The table compares the mean PSNR and SSIM for different configurations of the loss function, attention mechanism, and network architecture, with the default configuration used for state-of-the-art comparisons.", "description": "Table 2: This table reports the mean PSNR and the mean SSIM for some configurations of the loss function, the use of our attention mechanism, and the network architecture. Default configuration represents the model that we use to compare with the state-of-the-art literature." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: This table reports the mean PSNR and the mean SSIM for some configurations of the loss function, the use of our attention mechanism, and the network architecture. Default configuration represents the model that we use to compare with the state-of-the-art literature.'\nProblem: 'Which configuration has the highest mean SSIM?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/302.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: This table reports the mean PSNR and the mean SSIM for some configurations of the loss function, the use of our attention mechanism, and the network architecture. Default configuration represents the model that we use to compare with the state-of-the-art literature." ], "reasoning": "To find the configuration with the highest mean SSIM, compare the SSIM values for all configurations. The highest SSIM value is 0.684, which corresponds to the Default (RM+AM) configuration.", "question": "Which configuration has the highest mean SSIM?", "answer": "So, the answer is {\"answer\": \"Default (RM+AM)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe U-Net configuration has the lowest mean PSNR.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/302.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: This table reports the mean PSNR and the mean SSIM for some configurations of the loss function, the use of our attention mechanism, and the network architecture. Default configuration represents the model that we use to compare with the state-of-the-art literature." ], "reasoning": "To verify this statement, compare the PSNR values for all configurations. The lowest PSNR value is 14.81, which corresponds to the U-Net configuration.", "statement": "The U-Net configuration has the lowest mean PSNR.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Comparison of adversarial learning techniques trained and attacked under the same budget. The table also shows the performance difference between RF-Treant and the best competitor.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/303.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Comparison of adversarial learning techniques trained and attacked under the same budget. The table also shows the performance difference between RF-Treant and the best competitor." ], "reasoning": "Our method consistently outperforms competitors across all measures and datasets, with the performance gap widening as the attacker's strength increases.", "description": "Table III shows the results obtained by the different adversarial learning techniques for the different training/test budgets. It is clear how our method outperforms its competitors, basically for all measures and datasets. Most importantly, the superiority of our approach becomes even more pronounced as the strength of the attacker grows. For example, the percentage improvement in ROC AUC over AB on the credit dataset amounts to 2.1% for budget 10, while this improvement grows to 19.6% for budget 60. It is also worth noticing that the performance of RT is consistently worse than that of AB." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Comparison of adversarial learning techniques trained and attacked under the same budget. The table also shows the performance difference between RF-Treant and the best competitor.'\nProblem: 'What is the percentage improvement in ROC AUC over AB on the credit dataset for budget 60?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/303.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Comparison of adversarial learning techniques trained and attacked under the same budget. The table also shows the performance difference between RF-Treant and the best competitor." ], "reasoning": "The table shows that for the credit dataset with budget 60, the ROC AUC for AB is 0.622 and for RF-Treant it is 0.744. The percentage improvement is calculated as ((0.744 - 0.622) / 0.622) * 100 = 19.6%.", "question": "What is the percentage improvement in ROC AUC over AB on the credit dataset for budget 60?", "answer": "So, the answer is {\"answer\": \"19.6%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe performance of RT is consistently worse than that of AB.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/303.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Comparison of adversarial learning techniques trained and attacked under the same budget. The table also shows the performance difference between RF-Treant and the best competitor." ], "reasoning": "The table shows that for all datasets and budgets, the accuracy, F1, and ROC AUC values for RT are lower than those for AB, supporting the statement.", "statement": "The performance of RT is consistently worse than that of AB.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Average performance (and standard deviations) on discrete and continuous control unseen tasks over the last 50 episodes.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/304.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Average performance (and standard deviations) on discrete and continuous control unseen tasks over the last 50 episodes." ], "reasoning": "Using the advisor with PPO showed significant improvements in both discrete and continuous control tasks, outperforming MAML in most cases, especially in pole-balancing and Hopper tasks.", "description": "Table 1: Average performance (and standard deviations) on discrete and continuous control unseen tasks over the last 50 episodes.. In the discrete case, MAML showed a clear improvement over starting from a random initial policy. However, using the advisor with PPO resulted in a clear improvement in pole-balancing and, in the case of animat, training the advisor with REINFORCE led to an almost 50% improvement over MAML. In the case of continuous control, the first test corresponds to a continuous version of pole-balancing. The second and third set of tasks correspond to the \"Hopper\" and \"Ant\" problem classes, where the task variations were obtained by modifying the length and size of the limbs and body. In all continuous control tasks, using the advisor and MAML led to an clear improvement in performance in the alloted time. In the case of pole-balancing using the advisor led the agent to accumulate almost twice as much reward as MAML, and in the case of Hopper, the advisor led to accumulating 4 times the reward. On the other had, MAML achieved a higher average return in the Ant problem class, but showing high variance. An important takeaway from these results is that in all cases, using the advisor resulted in a clear improvement in performance over a limited number of episodes. This does not necessarily mean that the agent can reach a better policy over an arbitrarily long period of time, but rather that it is able to reach a certain performance level much quicker." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Average performance (and standard deviations) on discrete and continuous control unseen tasks over the last 50 episodes.'\nProblem: 'Which method achieved the highest average return in the continuous control Ant problem class?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/304.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Average performance (and standard deviations) on discrete and continuous control unseen tasks over the last 50 episodes." ], "reasoning": "The table shows that in the Ant problem class, MAML achieved an average return of 113.33±64.48, which is higher than the returns achieved by other methods.", "question": "Which method achieved the highest average return in the continuous control Ant problem class?", "answer": { "answer": "MAML" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUsing the advisor with PPO resulted in the highest average return in the discrete pole-balancing task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/304.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Average performance (and standard deviations) on discrete and continuous control unseen tasks over the last 50 episodes." ], "reasoning": "The table shows that in the discrete pole-balancing task, using the advisor with PPO (PPO+Advisor) resulted in an average return of 46.29±6.30, which is higher than the returns achieved by other methods.", "statement": "Using the advisor with PPO resulted in the highest average return in the discrete pole-balancing task.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Performance Evaluation of IDS on CAN-intrusion Dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/305.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Performance Evaluation of IDS on CAN-intrusion Dataset" ], "reasoning": "Tree-based algorithms DT, RF, and ET were selected for the stacking ensemble model due to their high accuracy and lower execution time, resulting in 100% accuracy, detection rate, and F1 score.", "description": "The results of testing different algorithms on CAN-intrusion data set and CICIDS2017 data set are shown in Tables III and IV, respectively. According to Table III, when testing on the CAN-intrusion data set, the tree-based algorithms including DT, RF, ET, and XGBoost used in the proposed system are 2.5% more accurate than KNN and 3.4% more accurate than SVM used in . In addition, multi-threading is enabled in DT, RF, ET, and XGBoost, resulting in a lower execution time compared with KNN and SVM. Since XGBoost has the lowest accuracy and longest execution time among the four tree-based ML models, only the other three algorithms, DT, RF, and ET were selected into the stacking ensemble model, and the single model with best performance, RF, was selected to be the meta-classifier in the second layer. After using stacking to combine the three tree-based models, the accuracy, detection rate and F1 score reaches 100%, which means that all the trained attacks can be detected.. From Tables III and IV, it can be seen that RF and XGBoost are the singular models with best accuracy on CAN-intrusion data set and CICIDS2017 data set, respectively. As single base models have lower execution time than the ensemble model, these two singular models were also tested after feature selection. For CAN-intrusion data set, the top-4 important features, CAN ID, 'DATA', 'DATA', 'DATA' were selected for the tests. The results of RF and stacking on CANintrusion data set after feature selection, named \"FS RF\" and \"FS Stacking\", can be seen from Table III." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Performance Evaluation of IDS on CAN-intrusion Dataset'\nProblem: 'Which method has the highest accuracy and what is its execution time?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/305.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Performance Evaluation of IDS on CAN-intrusion Dataset" ], "reasoning": "From the table, the method with the highest accuracy is Stacking with 100%. Its execution time is 1237.1 seconds.", "question": "Which method has the highest accuracy and what is its execution time?", "answer": { "answer": "1237.1" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nXGBoost has the lowest accuracy among the tree-based algorithms.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/305.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Performance Evaluation of IDS on CAN-intrusion Dataset" ], "reasoning": "The table shows that XGBoost has an accuracy of 99.98%, which is lower than DT, RF, and ET, confirming the statement.", "statement": "XGBoost has the lowest accuracy among the tree-based algorithms.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Performance Evaluation of IDS on CICIDS2017 Dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/306.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Performance Evaluation of IDS on CICIDS2017 Dataset" ], "reasoning": "Tree-based algorithms in the CICIDS2017 dataset show higher accuracy, detection rate, and F1 score than KNN and SVM, except for ET, leading to the selection of DT, RF, and XGBoost for the stacking method, with XGBoost as the meta-classifier.", "description": "Similarly in CICIDS2017 data set, as shown in Table IV, most of the used tree-based algorithms shows 1.8-3.2% higher accuracy, detection rate and F1 score than KNN and SVM except for ET. Hence, only DT, RF, and XGBoost were chosen in the proposed stacking method, and XGBoost was selected to be the meta-classifier of the stacking model. Although the execution time of stacking is longer than any singular tree-based models, it reaches the highest accuracy among the models (99.86%).. For CICIDS2017 data set, 36 out of 78 features were selected and the accuracy of XGBoost and stacking models only decreased by 0.08% and 0.04% while saving 39.2% and 38.6% of the execution time, respectively, as shown in Table IV." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Performance Evaluation of IDS on CICIDS2017 Dataset'\nProblem: 'Which method has the highest accuracy in the CICIDS2017 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/306.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Performance Evaluation of IDS on CICIDS2017 Dataset" ], "reasoning": "The table shows the accuracy of various methods. The highest accuracy is 99.86%, achieved by the Stacking method.", "question": "Which method has the highest accuracy in the CICIDS2017 dataset?", "answer": { "answer": "Stacking" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe execution time of the FS XGBoost method is less than 1000 seconds.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/306.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Performance Evaluation of IDS on CICIDS2017 Dataset" ], "reasoning": "The table shows that the execution time of the FS XGBoost method is 995.9 seconds, which is less than 1000 seconds.", "statement": "The execution time of the FS XGBoost method is less than 1000 seconds.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: Top-3 Feature Importance by Each Attack'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/307.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Top-3 Feature Importance by Each Attack" ], "reasoning": "The table shows the top-3 important features and their weights for different types of attacks, highlighting the significance of destination port, packet size, and packet length in various directions.", "description": "The list of top-3 important features and their corresponding weights of each attack is shown in Table V. As Table V shows, the destination port can reflect a DoS, brute force, web attack, and botnet attack. The size of packets is another important feature. For example, the average packet size indicates a DoS attack, port scan attack, and web attack. The packet length in the forward direction is related to port scan, brute-force, and infiltration attacks, while the packet length in the backward. direction reflects a DoS, web attack, and botnet. In addition, the variance of the length of the packets in both forward and backward directions reflects the brute force attack, and the count of pushing flags indicates port scan attack. After getting the feature importance list of each attack, the IDSs with other aims like designing a dedicated system for detecting a single type of attack can be developed by selecting the important features based on the list." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: Top-3 Feature Importance by Each Attack'\nProblem: 'Which feature has the highest weight for detecting a brute-force attack?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/307.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Top-3 Feature Importance by Each Attack" ], "reasoning": "To find the feature with the highest weight for detecting a prot-scan attack, look at the 'Port-Scan' row and identify the feature with the highest weight. The highest weight is 0.3728 for the 'Port Scan' feature.", "question": "Which feature has the highest weight for detecting a port-scan attack?", "answer": "So, the answer is {\"answer\": \"Destination Port\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average packet size is an important feature for detecting a DoS attack.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/307.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Top-3 Feature Importance by Each Attack" ], "reasoning": "The table lists 'Average Packet Size' as one of the top-3 important features for detecting a DoS attack with a weight of 0.1211.", "statement": "The average packet size is an important feature for detecting a DoS attack.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Median Uncertainty Value in Different Scenarios'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/308.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Median Uncertainty Value in Different Scenarios" ], "reasoning": "Frames taken in novel environments and under unseen weather conditions have higher average uncertainty values compared to those from seen weather and environments.", "description": "The median uncertainty measures under different commands are shown in Table I.. The general trend is that frames taken in novel environment and under unseen weather have a higher average uncertainty value than those from seen weather and environment." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Median Uncertainty Value in Different Scenarios'\nProblem: 'What is the average median uncertainty value for Town2 under unseen weather conditions?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/308.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Median Uncertainty Value in Different Scenarios" ], "reasoning": "To find the average median uncertainty value for Town2 under unseen weather conditions, look at the values for 'Median Uncertainty Value Follow', 'Median Uncertainty Value Left', 'Median Uncertainty Value Right', and 'Median Uncertainty Value Straight' for Town2 under unseen weather. The values are 0.740, 0.809, 0.952, and 0.753 respectively. The average is (0.740 + 0.809 + 0.952 + 0.753) / 4 = 0.8135.", "question": "What is the average median uncertainty value for Town2 under unseen weather conditions?", "answer": "So, the answer is {\"answer\": \"0.8135\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe median uncertainty value for following in Town1 under seen weather conditions is lower than that in Town2 under unseen weather conditions.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/308.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Median Uncertainty Value in Different Scenarios" ], "reasoning": "The median uncertainty value for following in Town1 under seen weather conditions is 0.694. The median uncertainty value for following in Town2 under unseen weather conditions is 0.740. Since 0.694 is less than 0.740, the statement is supported.", "statement": "The median uncertainty value for following in Town1 under seen weather conditions is lower than that in Town2 under unseen weather conditions.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Performance Comparison on Intersections Benchmark. Avg success rate and distance traveled between infractions are reported. Distance traveled between infractions can be higher for a model with lower success rate due to its failure in learning turning behaviors.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/309.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Performance Comparison on Intersections Benchmark. Avg success rate and distance traveled between infractions are reported. Distance traveled between infractions can be higher for a model with lower success rate due to its failure in learning turning behaviors." ], "reasoning": "The model trained with the active filter dataset outperformed both the baseline and the model using all the passive data.", "description": "The performance8 of the models trained with different datasets is shown in Table II.. The model trained with the active filter dataset outperformed that with baseline as we hypothesized.. the model trained with active filter also achieved better performance than the model using all the passive data," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Performance Comparison on Intersections Benchmark. Avg success rate and distance traveled between infractions are reported. Distance traveled between infractions can be higher for a model with lower success rate due to its failure in learning turning behaviors.'\nProblem: 'Which dataset achieved the highest success rate in Town 1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/309.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Performance Comparison on Intersections Benchmark. Avg success rate and distance traveled between infractions are reported. Distance traveled between infractions can be higher for a model with lower success rate due to its failure in learning turning behaviors." ], "reasoning": "To find the dataset with the highest success rate in Town 1, compare the success rates for Town 1 across all datasets. The highest value is 0.74, achieved by the UAIL dataset.", "question": "Which dataset achieved the highest success rate in Town 1?", "answer": "So, the answer is {\"answer\": \"UAIL\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model trained with the active filter dataset achieved the highest success rate in Town 2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/309.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Performance Comparison on Intersections Benchmark. Avg success rate and distance traveled between infractions are reported. Distance traveled between infractions can be higher for a model with lower success rate due to its failure in learning turning behaviors." ], "reasoning": "To verify this statement, compare the success rates for Town 2 across all datasets. The highest success rate in Town 2 is 0.61, achieved by the UAIL dataset, not the active filter dataset.", "statement": "The model trained with the active filter dataset achieved the highest success rate in Town 2.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison of average prediction errors for competing methods (in meters)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/310.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of average prediction errors for competing methods (in meters)" ], "reasoning": "The MobileNet-v2 LSTM model with fading and state inputs achieved the best performance across all metrics, outperforming other CNN architectures and the baseline models.", "description": "In Table 1 we report error metrics relevant for motion prediction: displacement errors, as well as along-track and cross-track errors , averaged over the prediction horizon.. Considering the baselines, we see that the linear model. easily outperformed the baseline UKF, which simply propagates an initial actor state. Moreover, using the map information through the lane-assoc model we gained significant improvements, especially in the cross-track which is already at the level of the best deep models.. We then conducted an ablation study using the feedforward architecture from Figure 2 and AlexNet as a base CNN, running experiments with varying input complexity (upper half of Table 1). When we provide neither fading nor state inputs the model performs worse than UKF, as the network does not have enough information to estimate current state of an actor from the raster. Interestingly, when we include fading the model starts to outperform the baseline by a large margin, indicating that actor state can be inferred solely from providing past positions through fading. If instead of fading we directly provide state estimates we get even better performance, as the state info is already distilled and does not need to be estimated from raster. Furthermore, using raster with fading together with state inputs leads to additional performance boost, suggesting that fading carries additional info not available through the state itself, and that the raster and other external inputs can be seamlessly combined through the proposed architecture to improve accuracy.. Next, we compared popular CNN architectures as base CNNs. As seen in the bottom half of Table 1, we found that VGG and ResNet models provide improvements over the baseline AlexNet, as observed previously . It is interesting to note that only starting with these models did we outperform the baseline lane-assoc model in terms of all the relevant metrics. However, both models are outperformed by the novel MNv2 architecture that combines a number of deep learning ideas under one roof (e.g., bottleneck layers, residual connections, depthwise convolutions). Taking the best performing MNv2 as a base and extending the output layer by adding uncertainty led to further improvements.. Lastly, using LSTM decoder at the output, as described in Section 3.3, led to the best results. In our task the future states depend on the past ones, which can be captured by the recurrent architecture." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison of average prediction errors for competing methods (in meters)'\nProblem: 'Which model achieved the lowest cross-track error and what was the value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/310.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of average prediction errors for competing methods (in meters)" ], "reasoning": "By examining the table, we see that the MobileNet-v2 LSTM model with fading and state inputs achieved the lowest cross-track error with a value of 0.14.", "question": "Which model achieved the lowest cross-track error?", "answer": "So, the answer is {\"answer\": \"MobileNet-v2 LSTM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe AlexNet model with fading and state inputs outperformed the Linear model in terms of displacement error.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/310.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of average prediction errors for competing methods (in meters)" ], "reasoning": "The table shows that the AlexNet model with fading and state inputs had a displacement error of 0.86, while the Linear model had a displacement error of 1.19. Therefore, the statement is supported.", "statement": "The AlexNet model with fading and state inputs outperformed the Linear model in terms of displacement error.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Comparison between purchased fake downloads injection services on our honeypot App. Portal website: download comes from App market portal website. Update: download comes from updating the App. Null: no download source record.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/311.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Comparison between purchased fake downloads injection services on our honeypot App. Portal website: download comes from App market portal website. Update: download comes from updating the App. Null: no download source record." ], "reasoning": "Fake downloads from Farm 1 and Farm 4 come from the App market portal website, while Farm 2 simulates device IDs and injects fake downloads via updates.", "description": "As Table II shows, we finally contact with fraudster agencies via their websites, e-commerce platforms like Taobao, or online chatting services like QQ.. Table II shows the main attributes of four purchased download injection services. We could see that fake downloads injected by four farms all have distinct IP addresses but vary in downloading source and device ID. Regular downloads usually come from App market clients on smart devices, while Farm 1 and Farm 4 both inject fake downloads from the App market portal website. The mechanism is simply clicking the honeypot App downloading URL listed at the App market portal website. Moreover, to generate valid device IDs. Farm 2 can simulate the device ID information, but it injects the fake downloads via repeatedly updating the honeypot App, which is not an ordinary behavior. they both fail" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Comparison between purchased fake downloads injection services on our honeypot App. Portal website: download comes from App market portal website. Update: download comes from updating the App. Null: no download source record.'\nProblem: 'Which farm injects fake downloads via repeatedly updating the honeypot App?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/311.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Comparison between purchased fake downloads injection services on our honeypot App. Portal website: download comes from App market portal website. Update: download comes from updating the App. Null: no download source record." ], "reasoning": "According to the description, Farm 2 injects fake downloads via repeatedly updating the honeypot App.", "question": "Which farm injects fake downloads via repeatedly updating the honeypot App?", "answer": { "answer": "Farm 2" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFarm 3 injects fake downloads from the App market portal website.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/311.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Comparison between purchased fake downloads injection services on our honeypot App. Portal website: download comes from App market portal website. Update: download comes from updating the App. Null: no download source record." ], "reasoning": "The table shows that Farm 3's source is 'Null', not the App market portal website.", "statement": "Farm 3 injects fake downloads from the App market portal website.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Testing results of different types of features with XGBoost.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/312.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Testing results of different types of features with XGBoost." ], "reasoning": "Aggregating all features together achieves 100% in all metrics, validating the rationale behind the feature selection.", "description": "Table III shows the testing result. We note that the device features set could identify more fake downloads, but it has a lower precision, which means it classifies more regular downloads as fake. A high false positive rate will make the legitimate app marketers compromise mobile App markets, and may further reduce the markets' revenue. Though the features proposed by us cannot beat the performance of previous features, aggregating all features together could hit 100% among all metrics. It validates the rationale behind our feature selection." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Testing results of different types of features with XGBoost.'\nProblem: 'Which feature type has the highest precision?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/312.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Testing results of different types of features with XGBoost." ], "reasoning": "By examining the table, the 'All' feature type has the highest precision value of 0.994.", "question": "Which feature type has the highest precision?", "answer": "So, the answer is {\"answer\": \"All\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'Device' feature type has the highest recall.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/312.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Testing results of different types of features with XGBoost." ], "reasoning": "The table shows that the 'All' feature type has the highest recall value of 0.992, not the 'Device' feature type.", "statement": "The 'Device' feature type has the highest recall.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Isomorphic metrics for Top-10 data sets based on the proportion of isomorphic graphs I%. IP% is the proportion of isomorphic pairs of graphs, Mismatched % is the proportion of mismatched labels.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/313.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Isomorphic metrics for Top-10 data sets based on the proportion of isomorphic graphs I%. IP% is the proportion of isomorphic pairs of graphs, Mismatched % is the proportion of mismatched labels." ], "reasoning": "Table 2 shows metrics for the top-10 data sets, sorted by the proportion of isomorphic graphs (I%). It highlights that almost all graphs in these data sets have other isomorphic graphs, and the percentage of mismatched labels can be high.", "description": "The metrics are presented in Table 2 for top-10 data sets. The graphs in Table 2 are sorted by the proportion of isomorphic graphs I%. The results for the first Top-10 data sets are somewhat surprising: almost all graphs in the selected data sets have other isomorphic graphs.. iso(i) the instances contain target labels that are different than those that it has seen, as we show in Table 2 the percentage of mismatched labels can be high in some data sets;" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Isomorphic metrics for Top-10 data sets based on the proportion of isomorphic graphs I%. IP% is the proportion of isomorphic pairs of graphs, Mismatched % is the proportion of mismatched labels.'\nProblem: 'Which data set has the highest percentage of mismatched labels?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/313.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Isomorphic metrics for Top-10 data sets based on the proportion of isomorphic graphs I%. IP% is the proportion of isomorphic pairs of graphs, Mismatched % is the proportion of mismatched labels." ], "reasoning": "To find the data set with the highest percentage of mismatched labels, look at the 'Mismatched %' column and identify the highest value.", "question": "Which data set has the highest percentage of mismatched labels?", "answer": "So, the answer is {\"answer\": \"Letter-low\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe data set 'COIL-RAG' has the highest number of isomorphic graphs.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/313.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Isomorphic metrics for Top-10 data sets based on the proportion of isomorphic graphs I%. IP% is the proportion of isomorphic pairs of graphs, Mismatched % is the proportion of mismatched labels." ], "reasoning": "To verify this statement, check the 'Iso. graphs, I' column and compare the values. 'COIL-RAG' has 3890 isomorphic graphs, which is the highest among the listed data sets.", "statement": "The data set 'COIL-RAG' has the highest number of isomorphic graphs.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Isomorphic metrics with node labels for Top-10 data sets based on the proportion of isomorphic graphs I%. IP% is the proportion of isomorphic pairs of graphs, Mismatched % is the proportion of mismatched labels.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/314.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Isomorphic metrics with node labels for Top-10 data sets based on the proportion of isomorphic graphs I%. IP% is the proportion of isomorphic pairs of graphs, Mismatched % is the proportion of mismatched labels." ], "reasoning": "The proportion of isomorphic graphs has remained relatively stable for the first six data sets but has significantly changed for the remaining data sets.", "description": "While for the first six data sets the proportion of isomorphic graphs has not changed much, it is clearly the case for the remaining data sets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Isomorphic metrics with node labels for Top-10 data sets based on the proportion of isomorphic graphs I%. IP% is the proportion of isomorphic pairs of graphs, Mismatched % is the proportion of mismatched labels.'\nProblem: 'Which data set has the highest percentage of mismatched graphs among the first six data sets?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/314.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Isomorphic metrics with node labels for Top-10 data sets based on the proportion of isomorphic graphs I%. IP% is the proportion of isomorphic pairs of graphs, Mismatched % is the proportion of mismatched labels." ], "reasoning": "To find the data set with the highest percentage of mismatched graphs among the first six data sets, we need to compare the 'Mismatched %' values for the first six rows. The second highest value among these is 98.68% for COX2_MD.", "question": "Which data set has the second highest percentage of mismatched graphs among the first six data sets?", "answer": "So, the answer is {\"answer\": \"COX2_MD\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe data set with the smallest number of isomorphic graphs has the highest percentage of mismatched graphs.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/314.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Isomorphic metrics with node labels for Top-10 data sets based on the proportion of isomorphic graphs I%. IP% is the proportion of isomorphic pairs of graphs, Mismatched % is the proportion of mismatched labels." ], "reasoning": "The data set with the smallest number of isomorphic graphs is DHFR with 98 isomorphic graphs. The 'Mismatched %' for DHFR is 3.97%, which is not the highest percentage of mismatched graphs. Therefore, the statement is refuted.", "statement": "The data set with the smallest number of isomorphic graphs has the highest percentage of mismatched graphs.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Mean classification accuracy for test sets Ytest and Yiso (in brackets) in 10-fold cross-validation. Top-1 result in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/315.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Mean classification accuracy for test sets Ytest and Yiso (in brackets) in 10-fold cross-validation. Top-1 result in bold." ], "reasoning": "The peering model on homogeneous data consistently outperforms other models, aligning with Theorem 6.1 which guarantees acc(Yiso) = 1.", "description": "In experiments, we compare neural network model (NN) with graph kernels, Weisfeiler-Lehman (WL) and vertex histogram (V) . For each model we consider two modifications: one for peering model on homogeneous Yiso (e.g. NN-PH) and one for peering model on all Yiso (e.g. NN-P). We show accuracy on Ytest and on Yiso (in brackets) in Table 4. Experimentation details can be found in Appendix G. From Table 4 we can conclude that peering model on homogeneous data is always the top performer. This is aligned with the result of Theorem 6.1, which guarantees that acc(Yiso) = 1, but it is an interesting observation if we compare it to the peering model on all isomorphic instances Yiso (-P models). Moreover, the latter model often loses even to the original model, where no information from the train set is explicitly taken into the test set." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Mean classification accuracy for test sets Ytest and Yiso (in brackets) in 10-fold cross-validation. Top-1 result in bold.'\nProblem: 'Which model has the highest accuracy on the IMDB-B dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/315.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Mean classification accuracy for test sets Ytest and Yiso (in brackets) in 10-fold cross-validation. Top-1 result in bold." ], "reasoning": "By examining the table, the highest accuracy on the IMDB-B dataset is achieved by the WL-PH model with an accuracy of 0.907.", "question": "Which model has the highest accuracy on the IMDB-B dataset?", "answer": "So, the answer is {\"answer\": \"WL-PH\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe NN-PH model has the highest accuracy on the AIDS dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/315.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Mean classification accuracy for test sets Ytest and Yiso (in brackets) in 10-fold cross-validation. Top-1 result in bold." ], "reasoning": "The table shows that the NN-PH model has an accuracy of 0.996 on the AIDS dataset, which is the highest among all models listed.", "statement": "The NN-PH model has the highest accuracy on the AIDS dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Average accuracy of UHC methods over different combinations of HC configurations, datasets, and unified classifier models. (Underline bold: Best method. Bold: Methods which are not statistically significantly different from the best method.)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/316.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average accuracy of UHC methods over different combinations of HC configurations, datasets, and unified classifier models. (Underline bold: Best method. Bold: Methods which are not statistically significantly different from the best method.)" ], "reasoning": "All proposed methods perform significantly better than SD, with MF-LF-BS consistently performing well across different settings, and balancing soft labels generally improving accuracy.", "description": "Table 2 shows the results for this experiment. Each column shows the average accuracy of each method under each experiment setting, where the best performing method is shown in underlined bold.. All proposed methods perform significantly better than SD. We can see that all methods in (A), (B), and (C). of Table 2 outperform SD by a large margin of 9-15%.. On the other hand, our proposed methods achieve significantly better results and almost reach the same accuracy as SPV with a gap of 1-4%.. MF-LF-BS performs well in all cases. We can see that different algorithms perform best under different settings, but MF-LF-BS always performs best or has no statistical difference from the best methods.. Balancing soft labels helps improve accuracy. While the improvement may be marginal (less than 1.5%), we can see that 'BS' methods in (C) consistently outperform their 'E' counterparts in (A). Surprisingly, SD-BS, which is SD with balanced soft labels, also significantly improved over SD by more than 10%.. All methods perform rather well. We can see that all methods, including SD, achieve about the same accuracy (within ~1% range).. Not balancing soft labels performs better. We note that balancing soft labels tends to slightly deteriorate the accuracy. This is the opposite result from the random classes case. Here, even SD-BS which receive an accuracy boost in the random classes case also performs worse than its counterpart SD.. Distillation may outperform its supervised counterparts. For LSUN and Places365 datasets, we see that many times distillation methods performs better than SPV. Especially for the case of VGG16, we see SPV consistently perform worse than other methods by 1 to 3% in most of the trials." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Average accuracy of UHC methods over different combinations of HC configurations, datasets, and unified classifier models. (Underline bold: Best method. Bold: Methods which are not statistically significantly different from the best method.)'\nProblem: 'Which method achieves the highest accuracy for the Completely Overlapping Classes LSUN dataset using VGG16?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/316.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average accuracy of UHC methods over different combinations of HC configurations, datasets, and unified classifier models. (Underline bold: Best method. Bold: Methods which are not statistically significantly different from the best method.)" ], "reasoning": "To find the method with the highest accuracy for the Completely Overlapping Classes LSUN dataset using VGG16, we look at the column corresponding to 'Completely Overlapping Classes LSUN' and the row for 'VGG16'. The highest value in this column is .7057, achieved by MF-LF-E.", "question": "Which method achieves the highest accuracy for the Completely Overlapping Classes LSUN dataset using VGG16?", "answer": "So, the answer is {\"answer\": \"MF-LF-E\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBalancing soft labels always improves accuracy.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/316.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average accuracy of UHC methods over different combinations of HC configurations, datasets, and unified classifier models. (Underline bold: Best method. Bold: Methods which are not statistically significantly different from the best method.)" ], "reasoning": "The description states that balancing soft labels helps improve accuracy, but it also mentions that not balancing soft labels performs better in some cases. Therefore, the statement that balancing soft labels always improves accuracy is refuted.", "statement": "Balancing soft labels always improves accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: performance of different data augmentation'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/317.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: performance of different data augmentation" ], "reasoning": "The seven-DoF augmentation method consistently performs best across all testing data sets, especially in those with larger distortion.", "description": "The performance of different data augmentation methods are shown in Table I. While the fixed z-aug performs worst, the seven-DoF augmentation we proposed almost performs best in all testing data sets. Compared with other augmentation methods, the seven-DoF has significant advantages, especially in testing data sets with larger distortion (smaller f value)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: performance of different data augmentation'\nProblem: 'Which data augmentation method performs best in the testing data set with f=200?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/317.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: performance of different data augmentation" ], "reasoning": "By examining the table, the highest mIoU value for f=200 is 0.6093, which corresponds to the seven-DoF augmentation method.", "question": "Which data augmentation method performs best in the testing data set with f=200?", "answer": "So, the answer is {\"answer\": \"Seven-DoF aug\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe fixed z-aug method performs worst in all testing data sets.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/317.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: performance of different data augmentation" ], "reasoning": "The table shows that the fixed z-aug method has the lowest mIoU values across all testing data sets compared to other methods.", "statement": "The fixed z-aug method performs worst in all testing data sets.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Rounded most important bandwidth means for classification per region in nanometers'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/318.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Rounded most important bandwidth means for classification per region in nanometers" ], "reasoning": "The table shows the most important wavelength bands for classification in different hand skin regions, with the importance determined by the frequency of occurrence in 100 algorithm executions.", "description": "Table 1: Rounded most important bandwidth means for classification per region in nanometers. Table 1presents the wavelength bands found to be the most important for the classification task. It is worth noting that the importance of the band is directly linked to the number of times it has been counted among the 100 executions of the algorithm." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Rounded most important bandwidth means for classification per region in nanometers'\nProblem: 'What is the third most important wavelength band for the Metacarpal region?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/318.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Rounded most important bandwidth means for classification per region in nanometers" ], "reasoning": "To find the third most important wavelength band for the Metacarpal region, look at the 'Third Band' column in the row corresponding to the Metacarpal region.", "question": "What is the third most important wavelength band for the Metacarpal region?", "answer": "So, the answer is {\"answer\": \"1690\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe first band for the Proximal Phalanges region is 1270 nanometers.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/318.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Rounded most important bandwidth means for classification per region in nanometers" ], "reasoning": "To verify this statement, check the 'First band' column in the row corresponding to the Proximal Phalanges region. The value is 1270 nanometers.", "statement": "The first band for the Proximal Phalanges region is 1270 nanometers.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Multi-task average test accuracy. Mean and standard deviation over 5 train-test splits.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/319.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Multi-task average test accuracy. Mean and standard deviation over 5 train-test splits." ], "reasoning": "The table shows the average categorical accuracy over all tasks of the respective dataset, measured over 5 random 25% train test splits, with mean and standard deviation reported.", "description": "In Table 2 we show the advantages given by the multi-task learning framework in the federated setting. In the table we measure the average categorical accuracy over all tasks of the respective dataset. Every experiment has been repeated over 5 random 25% train test splits, and we report mean and standard deviation over the runs." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Multi-task average test accuracy. Mean and standard deviation over 5 train-test splits.'\nProblem: 'Which method achieved the highest average categorical accuracy on the FEMNIST dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/319.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Multi-task average test accuracy. Mean and standard deviation over 5 train-test splits." ], "reasoning": "To find the method with the highest average categorical accuracy on the FEMNIST dataset, compare the accuracy values in the FEMNIST column. The highest value is 0.56±0.01, achieved by the Virtual method.", "question": "Which method achieved the highest average categorical accuracy on the FEMNIST dataset?", "answer": "So, the answer is {\"answer\": \"Virtual\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Local method achieved the highest accuracy on the HAR dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/319.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Multi-task average test accuracy. Mean and standard deviation over 5 train-test splits." ], "reasoning": "To verify this statement, compare the accuracy values in the HAR column. The highest value is 0.944±0.004, achieved by both FedAvg and Virtual methods, not the Local method.", "statement": "The Local method achieved the highest accuracy on the HAR dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Results of constrained one-pixel adversarial perturbations on 100 images from CIFAR-10 dataset. For both constrained and unconstrained perturbations, the total time in seconds, average time in seconds, and the average number of DE iterations are presented. Five runs for constrained and unconstrained one-pixel adversarial perturbations show that constrained perturbations converge faster.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/320.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of constrained one-pixel adversarial perturbations on 100 images from CIFAR-10 dataset. For both constrained and unconstrained perturbations, the total time in seconds, average time in seconds, and the average number of DE iterations are presented. Five runs for constrained and unconstrained one-pixel adversarial perturbations show that constrained perturbations converge faster." ], "reasoning": "Constrained one-pixel adversarial perturbations on CIFAR-10 converge faster and require fewer iterations compared to unconstrained perturbations.", "description": "Table 1: Results of constrained one-pixel adversarial perturbations on 100 images from CIFAR-10 dataset. For both constrained and unconstrained perturbations, the total time in seconds, average time in seconds, and the average number of DE iterations are presented. Five runs for constrained and unconstrained one-pixel adversarial perturbations show that constrained perturbations converge faster.. The results for the CIFAR-10 and NIPS-W datasets are summarized in Table 1 and Table 2 respectively.. It can be clearly observed that across different runs and on average, the constrained one-pixel adversarial perturbations converged faster with comparatively less number of iterations when compared to conventional one-pixel adversarial perturbations.. This effect is more pronounced for CIFAR10, with the spatially constrained one-pixel adversarial perturbations converging 6× faster than conventional attacks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Results of constrained one-pixel adversarial perturbations on 100 images from CIFAR-10 dataset. For both constrained and unconstrained perturbations, the total time in seconds, average time in seconds, and the average number of DE iterations are presented. Five runs for constrained and unconstrained one-pixel adversarial perturbations show that constrained perturbations converge faster.'\nProblem: 'What is the average number of iterations for constrained one-pixel adversarial perturbations?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/320.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of constrained one-pixel adversarial perturbations on 100 images from CIFAR-10 dataset. For both constrained and unconstrained perturbations, the total time in seconds, average time in seconds, and the average number of DE iterations are presented. Five runs for constrained and unconstrained one-pixel adversarial perturbations show that constrained perturbations converge faster." ], "reasoning": "To find the average number of iterations for constrained perturbations, look at the 'Constrained (ours) AVG # itr' column and calculate the average of the values.", "question": "What is the average number of iterations for constrained one-pixel adversarial perturbations?", "answer": "So, the answer is {\"answer\": \"2.406\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nConstrained one-pixel adversarial perturbations take less time on average than unconstrained perturbations.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/320.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of constrained one-pixel adversarial perturbations on 100 images from CIFAR-10 dataset. For both constrained and unconstrained perturbations, the total time in seconds, average time in seconds, and the average number of DE iterations are presented. Five runs for constrained and unconstrained one-pixel adversarial perturbations show that constrained perturbations converge faster." ], "reasoning": "The table shows that the average time for constrained perturbations is consistently lower than that for unconstrained perturbations across all runs.", "statement": "Constrained one-pixel adversarial perturbations take less time on average than unconstrained perturbations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results of constrained one-pixel adversarial perturbations on the NIPS-W dataset. For both constrained and unconstrained successful attacks, the total time in seconds, average time in seconds, and average number of DE iterations are presented. Five runs for constrained and unconstrained one-pixel adversarial perturbations show that constrained perturbations converge faster.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/321.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of constrained one-pixel adversarial perturbations on the NIPS-W dataset. For both constrained and unconstrained successful attacks, the total time in seconds, average time in seconds, and average number of DE iterations are presented. Five runs for constrained and unconstrained one-pixel adversarial perturbations show that constrained perturbations converge faster." ], "reasoning": "Constrained one-pixel adversarial perturbations on the NIPS-W dataset converge faster than unconstrained perturbations, as indicated by lower total and average times.", "description": "Table 2: Results of constrained one-pixel adversarial perturbations on the NIPS-W dataset. For both constrained and unconstrained successful attacks, the total time in seconds, average time in seconds, and average number of DE iterations are presented. Five runs for constrained and unconstrained one-pixel adversarial perturbations show that constrained perturbations converge faster." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results of constrained one-pixel adversarial perturbations on the NIPS-W dataset. For both constrained and unconstrained successful attacks, the total time in seconds, average time in seconds, and average number of DE iterations are presented. Five runs for constrained and unconstrained one-pixel adversarial perturbations show that constrained perturbations converge faster.'\nProblem: 'What is the average number of DE iterations for unconstrained perturbations?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/321.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of constrained one-pixel adversarial perturbations on the NIPS-W dataset. For both constrained and unconstrained successful attacks, the total time in seconds, average time in seconds, and average number of DE iterations are presented. Five runs for constrained and unconstrained one-pixel adversarial perturbations show that constrained perturbations converge faster." ], "reasoning": "To find the average number of DE iterations for unconstrained perturbations, look at the 'Unconstrained AVG # itr' column and calculate the average of the values.", "question": "What is the average number of DE iterations for unconstrained perturbations?", "answer": "So, the answer is {\"answer\": \"9.474\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nConstrained perturbations have a lower average time than unconstrained perturbations.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/321.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of constrained one-pixel adversarial perturbations on the NIPS-W dataset. For both constrained and unconstrained successful attacks, the total time in seconds, average time in seconds, and average number of DE iterations are presented. Five runs for constrained and unconstrained one-pixel adversarial perturbations show that constrained perturbations converge faster." ], "reasoning": "The table shows that the average time for constrained perturbations (ranging from 5.95 to 7.31 seconds) is lower than the average time for unconstrained perturbations (ranging from 7.79 to 9.32 seconds).", "statement": "Constrained perturbations have a lower average time than unconstrained perturbations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Averaged test error over 10 random training data splits for AFA, FA, MKRUM and COMED in different scenarios: clean datasets for all clients, Byzantine adversaries, label flipping attacks, and noisy clients. For each scenario we considered two settings with 10 and 100 clients with 3 and 30 bad/malicious clients respectively. Best results with statistical significance at the 5% level, according to a Wilcoxon Rank-Sum test, are highlighted in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/322.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Averaged test error over 10 random training data splits for AFA, FA, MKRUM and COMED in different scenarios: clean datasets for all clients, Byzantine adversaries, label flipping attacks, and noisy clients. For each scenario we considered two settings with 10 and 100 clients with 3 and 30 bad/malicious clients respectively. Best results with statistical significance at the 5% level, according to a Wilcoxon Rank-Sum test, are highlighted in bold." ], "reasoning": "AFA is robust across all scenarios for the 4 datasets, while FA's performance is significantly affected by bad clients, especially in byzantine and flipping scenarios. MKRUM and COMED are very sensitive to label flipping attacks, particularly in MNIST, FMNIST, and CIFAR-10 for 10 clients.", "description": "Thus, for each experiment in Table 1 we highlight those results where one or two algorithms perform significantly better than the others (according to the outcome of the statistical test). From the results we can observe that AFA is robust in all scenarios for the 4 datasets, i.e. the test error of the algorithm is not significantly degraded compared to the performance achieved in the clean scenarios. As expected, FA's performance is significantly affected under the presence of bad clients, especially in byzantine and flipping scenarios. It is also interesting to observe that both MKRUM and COMED are very sensitive to label flipping attacks, especially in MNIST, FMNIST and CIFAR-10 for the case of 10 clients. For example, in MNIST, MKRUM and COMED test errors increases up to 78.98% and 11.55% respectively in the flipping attack scenario with 10 clients, whereas AFA's error is just 2.96%, similar to its performance in the clean scenario. MKRUM also offers a worse performance in the clean scenario compared to the other algorithms, which is especially noticeable in the case of CIFAR-10, where the error rate is 53.02%, i.e. a 20% more than FA's error. This is not the case for COMED, whose performance is similar to AFA and FA when no bad clients are present, except for FMNIST, where COMED exhibits a better performance. However, COMED is not consistently robust to all the scenarios considered in the experiment.. The results of our experimental evaluation are shown in Table 1.. as shown Table 1, for CIFAR-10, AFA's performance is not affected by the presence of noisy clients, when comparing to the performance obtained for the clean scenario." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Averaged test error over 10 random training data splits for AFA, FA, MKRUM and COMED in different scenarios: clean datasets for all clients, Byzantine adversaries, label flipping attacks, and noisy clients. For each scenario we considered two settings with 10 and 100 clients with 3 and 30 bad/malicious clients respectively. Best results with statistical significance at the 5% level, according to a Wilcoxon Rank-Sum test, are highlighted in bold.'\nProblem: 'Which algorithm has the highest test error in the 10 Clients Byzantine scenario for MNIST?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/322.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Averaged test error over 10 random training data splits for AFA, FA, MKRUM and COMED in different scenarios: clean datasets for all clients, Byzantine adversaries, label flipping attacks, and noisy clients. For each scenario we considered two settings with 10 and 100 clients with 3 and 30 bad/malicious clients respectively. Best results with statistical significance at the 5% level, according to a Wilcoxon Rank-Sum test, are highlighted in bold." ], "reasoning": "From the table, in the 10 Clients Byzantine scenario for MNIST, FA has the highest test error with a value of 90.03±0.59.", "question": "Which algorithm has the highest test error in the 10 Clients Byzantine scenario for MNIST?", "answer": "So, the answer is {\"answer\": \"FA\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAFA's performance is significantly degraded in the 10 Clients Flipping scenario for MNIST.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/322.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Averaged test error over 10 random training data splits for AFA, FA, MKRUM and COMED in different scenarios: clean datasets for all clients, Byzantine adversaries, label flipping attacks, and noisy clients. For each scenario we considered two settings with 10 and 100 clients with 3 and 30 bad/malicious clients respectively. Best results with statistical significance at the 5% level, according to a Wilcoxon Rank-Sum test, are highlighted in bold." ], "reasoning": "The table shows that AFA's test error in the 10 Clients Flipping scenario for MNIST is 2.96±0.15, which is similar to its performance in the clean scenario (2.80±0.12). Therefore, AFA's performance is not significantly degraded.", "statement": "AFA's performance is significantly degraded in the 10 Clients Flipping scenario for MNIST.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Averaged test error over 10 random training data splits for AFA, FA, MKRUM and COMED in different scenarios: clean datasets for all clients, Byzantine adversaries, label flipping attacks, and noisy clients. For each scenario we considered two settings with 10 and 100 clients with 3 and 30 bad/malicious clients respectively. Best results with statistical significance at the 5% level, according to a Wilcoxon Rank-Sum test, are highlighted in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/323.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Averaged test error over 10 random training data splits for AFA, FA, MKRUM and COMED in different scenarios: clean datasets for all clients, Byzantine adversaries, label flipping attacks, and noisy clients. For each scenario we considered two settings with 10 and 100 clients with 3 and 30 bad/malicious clients respectively. Best results with statistical significance at the 5% level, according to a Wilcoxon Rank-Sum test, are highlighted in bold." ], "reasoning": "AFA is robust across all scenarios for FMNIST, while FA's performance is significantly affected by bad clients, especially in byzantine and flipping scenarios. MKRUM and COMED are very sensitive to label flipping attacks, particularly in the case of 10 clients.", "description": "Thus, for each experiment in Table 1 we highlight those results where one or two algorithms perform significantly better than the others (according to the outcome of the statistical test). From the results we can observe that AFA is robust in all scenarios for the 4 datasets, i.e. the test error of the algorithm is not significantly degraded compared to the performance achieved in the clean scenarios. As expected, FA’s performance is significantly affected under thepresence of bad clients, especially in byzantine and flipping scenarios. It is also interesting to observe that both MKRUM and COMED are very sensitive to label flipping attacks, especially in MNIST, FMNIST and CIFAR-10 for the case of 10 clients. For example, in MNIST, MKRUM and COMED test errors increases up to 78.98% and 11.55% respectively in the flipping attack scenario with 10 clients, whereas AFA’s error is just 2.96%, similar to its performance in the clean scenario. MKRUM also offers a worse performance in the clean scenario compared to the other algorithms, which is especially noticeable in the case of CIFAR-10, where the error rate is 53.02%, i.e. a 20% more than FA’s error. This is not the case for COMED, whose performance is similar toAFA and FA when no bad clients are present, except for FM-NIST, where COMED exhibits a better performance. How- ever, COMED is not consistently robust to all the scenariosconsidered in the experiment." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Averaged test error over 10 random training data splits for AFA, FA, MKRUM and COMED in different scenarios: clean datasets for all clients, Byzantine adversaries, label flipping attacks, and noisy clients. For each scenario we considered two settings with 10 and 100 clients with 3 and 30 bad/malicious clients respectively. Best results with statistical significance at the 5% level, according to a Wilcoxon Rank-Sum test, are highlighted in bold.'\nProblem: 'Which algorithm has the highest test error in the 10 Clients Byzantine scenario for FMNIST?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/323.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Averaged test error over 10 random training data splits for AFA, FA, MKRUM and COMED in different scenarios: clean datasets for all clients, Byzantine adversaries, label flipping attacks, and noisy clients. For each scenario we considered two settings with 10 and 100 clients with 3 and 30 bad/malicious clients respectively. Best results with statistical significance at the 5% level, according to a Wilcoxon Rank-Sum test, are highlighted in bold." ], "reasoning": "From the table, in the 10 Clients Byzantine scenario for FMNIST, FA has the highest test error with a value of 89.27±0.81.", "question": "Which algorithm has the highest test error in the 10 Clients Byzantine scenario for FMNIST?", "answer": "So, the answer is {\"answer\": \"FA\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAFA has the lowest test error in the 100 Clients Flipping scenario for FMNIST.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/323.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Averaged test error over 10 random training data splits for AFA, FA, MKRUM and COMED in different scenarios: clean datasets for all clients, Byzantine adversaries, label flipping attacks, and noisy clients. For each scenario we considered two settings with 10 and 100 clients with 3 and 30 bad/malicious clients respectively. Best results with statistical significance at the 5% level, according to a Wilcoxon Rank-Sum test, are highlighted in bold." ], "reasoning": "From the table, in the 100 Clients Flipping scenario for FMNIST, AFA has a test error of 15.99±2.00, which is not the lowest. COMED has the lowest test error with a value of 15.14±0.30.", "statement": "AFA has the lowest test error in the 100 Clients Flipping scenario for FMNIST.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Inception Scores and FIDs for supervised image generation. Each row corresponds to a single trained model. The models we trained were chosen by minimizing the IS within 100,000 iterations. Methods that are not class conditional are indicated by the †. The best number per column is bold faced.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/324.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Inception Scores and FIDs for supervised image generation. Each row corresponds to a single trained model. The models we trained were chosen by minimizing the IS within 100,000 iterations. Methods that are not class conditional are indicated by the †. The best number per column is bold faced." ], "reasoning": "MHingeGAN achieves superior performance on CIFAR100 with a higher IS and a lower FID compared to other methods, demonstrating its effectiveness in both supervised and semi-supervised settings.", "description": "Compared to other methods, our MHingeGAN yields a higher IS and a lower FID.. Tables 1 and 2 show state of the art performance of MHingeGAN on the CIFAR10 dataset. The class specific loss on the generator is shown to be essential for superior performance, and stores more conditional information in the discriminator/classifier and the generator. MHingeGAN is able to perform well in both fully supervised and semi-supervised settings, and learns an accurate classifier concurrently with a high quality generator." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Inception Scores and FIDs for supervised image generation. Each row corresponds to a single trained model. The models we trained were chosen by minimizing the IS within 100,000 iterations. Methods that are not class conditional are indicated by the †. The best number per column is bold faced.'\nProblem: 'What is the IS score of MHingeGAN on the CIFAR100 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/324.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Inception Scores and FIDs for supervised image generation. Each row corresponds to a single trained model. The models we trained were chosen by minimizing the IS within 100,000 iterations. Methods that are not class conditional are indicated by the †. The best number per column is bold faced." ], "reasoning": "The table shows the IS score for MHingeGAN on the CIFAR100 dataset as 14.36 (± .17).", "question": "What is the IS score of MHingeGAN on the CIFAR100 dataset?", "answer": "So, the answer is {\"answer\": \"14.36 (± .17)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMHingeGAN has the lowest FID score among all methods on the CIFAR100 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/324.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Inception Scores and FIDs for supervised image generation. Each row corresponds to a single trained model. The models we trained were chosen by minimizing the IS within 100,000 iterations. Methods that are not class conditional are indicated by the †. The best number per column is bold faced." ], "reasoning": "The table shows that the Baseline method has a lower FID score (11.53) compared to MHingeGAN (17.3), refuting the statement.", "statement": "MHingeGAN has the lowest FID score among all methods on the CIFAR100 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Inception scores on unlabelled CIFAR with and without the chain generator approach trained with WGAN+GP. d refers to the depth of the first conv layer in DCGAN architectures. See section 7.4 for details about architecture.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/325.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Inception scores on unlabelled CIFAR with and without the chain generator approach trained with WGAN+GP. d refers to the depth of the first conv layer in DCGAN architectures. See section 7.4 for details about architecture." ], "reasoning": "Models 2 and 4, which are DCGAN variants using multiple critics, outperform the base DCGAN model 6 in inception scores while using fewer parameters. Model 3 shows that using a single critic does not degrade performance, but model 5 shows that end-to-end backpropagation through the chain generator significantly degrades results.", "description": "All models compared are trained to the same number of epochs using the WGAN formulation with a gradient penalty term (WGAN+GP).. Based on inception scores, our DCGAN variant of the chain generator using multiple critics (model 2 and 4) was able to outperform the base DCGAN model (model 6) while using fewer parameters.. To ensure that our generator architecture and training scheme are responsible for the performance, we run experiments with a single critic and see that results do not degrade (see model 3 in Table 1); but with end-to-end backpropagation through the chain generator results degraded significantly (see model 5 in Table 1)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Inception scores on unlabelled CIFAR with and without the chain generator approach trained with WGAN+GP. d refers to the depth of the first conv layer in DCGAN architectures. See section 7.4 for details about architecture.'\nProblem: 'Which model has the highest Best Ei Score and how many parameters does it use?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/325.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Inception scores on unlabelled CIFAR with and without the chain generator approach trained with WGAN+GP. d refers to the depth of the first conv layer in DCGAN architectures. See section 7.4 for details about architecture." ], "reasoning": "Model 9 has the highest Best Ei Score of 6.86±0.04 and uses 1,220,000 parameters.", "question": "Which model has the highest Best Ei Score and how many parameters does it use?", "answer": "So, the answer is {\"answer\": \"1,220,000\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nModel 5 has the lowest Gb Score among all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/325.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Inception scores on unlabelled CIFAR with and without the chain generator approach trained with WGAN+GP. d refers to the depth of the first conv layer in DCGAN architectures. See section 7.4 for details about architecture." ], "reasoning": "The table shows that Model 5 has a Gb Score of 2.56±0.02, which is the lowest among all the models listed.", "statement": "Model 5 has the lowest Gb Score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison of PSNR mean and standard deviation on the 880 healthy inpainted patches.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/326.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of PSNR mean and standard deviation on the 880 healthy inpainted patches." ], "reasoning": "The semantic inpainting model achieves the highest average PSNR among the methods, although this does not fully correlate with visual results.", "description": "Table 1 provides the means and standard deviations of the peak signal-to-noise ratio (PSNR) over all 880 healthy images in the test set. The PSNR value is computed with respect to the center 64 × 64 missing patch rather than the entire patch. We note the semantic inpainting model achieves the highest average PSNR in this case; however, this quantitative assessment doesn't fully correlate with the visual results." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison of PSNR mean and standard deviation on the 880 healthy inpainted patches.'\nProblem: 'Which method has the highest mean PSNR value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/326.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of PSNR mean and standard deviation on the 880 healthy inpainted patches." ], "reasoning": "By comparing the mean PSNR values of the methods listed in the table, we see that the Semantic Inpainting method has the highest mean PSNR value of 33.85.", "question": "Which method has the highest mean PSNR value?", "answer": { "answer": "Semantic Inpainting" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nContextual Attention has a higher mean PSNR than Context Encoders.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/326.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of PSNR mean and standard deviation on the 880 healthy inpainted patches." ], "reasoning": "The table shows that Contextual Attention has a mean PSNR of 31.80, while Context Encoders has a mean PSNR of 26.31. Therefore, Contextual Attention has a higher mean PSNR than Context Encoders.", "statement": "Contextual Attention has a higher mean PSNR than Context Encoders.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Comparison of false alarm rate on abnormal videos of UCF-Crime testing dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/327.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Comparison of false alarm rate on abnormal videos of UCF-Crime testing dataset." ], "reasoning": "The proposed method with the new ranking loss function achieves the lowest false alarm rate for abnormal videos, demonstrating its effectiveness.", "description": "AUC in all, therefore in Table III, we have just shown the comparison of false alarm rate at the threshold of 50% on the basis of variation of our proposed method. Abnormal activities happen rarely in the real-world scenarios, but still detecting the abnormal activity accurately is an important task. Therefore the detector should give less false alarm rate for abnormal videos. As shown in Table III, our proposed method along with the new ranking loss function gives the lowest false alarm rate on abnormal videos. The table shows the effectiveness of using the new loss function." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Comparison of false alarm rate on abnormal videos of UCF-Crime testing dataset.'\nProblem: 'What is the false alarm rate for abnormal videos using the proposed method with the new ranking loss function?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/327.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Comparison of false alarm rate on abnormal videos of UCF-Crime testing dataset." ], "reasoning": "The table shows that the proposed method with the new ranking loss function has a false alarm rate of 0.67 for abnormal videos.", "question": "What is the false alarm rate for abnormal videos using the proposed method with the new ranking loss function?", "answer": "So, the answer is {\"answer\": \"0.67\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed method with the new ranking loss function has a higher false alarm rate for abnormal videos compared to the method without it.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/327.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Comparison of false alarm rate on abnormal videos of UCF-Crime testing dataset." ], "reasoning": "The table shows that the proposed method with the new ranking loss function has a false alarm rate of 0.67, which is lower than the 0.72 false alarm rate of the method without the new ranking loss function.", "statement": "The proposed method with the new ranking loss function has a higher false alarm rate for abnormal videos compared to the method without it.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: The error rate (%) on Fashion-MNIST dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/328.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The error rate (%) on Fashion-MNIST dataset." ], "reasoning": "The experiment on Fashion-MNIST shows that the Arc and Center methods have the lowest error rates, both at 6.04.", "description": "The experiment on Fashion-MNIST, shown in Table 5, reveals our method and center loss are best." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: The error rate (%) on Fashion-MNIST dataset.'\nProblem: 'Which methods have the lowest error rates in the Fashion-MNIST experiment?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/328.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The error rate (%) on Fashion-MNIST dataset." ], "reasoning": "By examining the error rates in the table, the Arc and Center methods both have the lowest error rates of 6.04.", "question": "Which two methods have the lowest error rates in the Fashion-MNIST experiment?", "answer": "So, the answer is {\"answer\": \"Arc and Center\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe CNN method has the highest error rate in the Fashion-MNIST experiment.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/328.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The error rate (%) on Fashion-MNIST dataset." ], "reasoning": "The table shows that the CNN method has an error rate of 7.46, which is the highest among all listed methods.", "statement": "The CNN method has the highest error rate in the Fashion-MNIST experiment.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Human observer performance for the task of choosing the real x-ray. An accuracy of 50% corresponds to chance behavior; an accuracy of 100% indicates the observer can perfectly identify the unaltered chest x-ray in a pair.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/329.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human observer performance for the task of choosing the real x-ray. An accuracy of 50% corresponds to chance behavior; an accuracy of 100% indicates the observer can perfectly identify the unaltered chest x-ray in a pair." ], "reasoning": "The contextual attention model is the most successful at fooling the expert, while the semantic inpainting model is the least successful.", "description": "Table 3 presents the results of this study. The contextual attention model is the most successful one to fool the expert, because more than half of the time the expert would select the image with an inpainted region as the real chest x-ray. On the other hand, the semantic inpainting model has a more difficult time to fool the expert, where only one third of the time its results are considered 'real'.. context encoder's results are usually also not detectable with observer accuracy close to chance levels. These findings are consistent with the results of the previous sections." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Human observer performance for the task of choosing the real x-ray. An accuracy of 50% corresponds to chance behavior; an accuracy of 100% indicates the observer can perfectly identify the unaltered chest x-ray in a pair.'\nProblem: 'Which model has the lowest observer accuracy in the 2AFC test?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/329.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human observer performance for the task of choosing the real x-ray. An accuracy of 50% corresponds to chance behavior; an accuracy of 100% indicates the observer can perfectly identify the unaltered chest x-ray in a pair." ], "reasoning": "The table shows observer accuracy percentages for different models. The contextual attention model has the lowest observer accuracy at 37.03%.", "question": "Which model has the lowest observer accuracy in the 2AFC test?", "answer": "So, the answer is {\"answer\": \"Contextual Attention\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe semantic inpainting model has the highest observer accuracy in the 2AFC test.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/329.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human observer performance for the task of choosing the real x-ray. An accuracy of 50% corresponds to chance behavior; an accuracy of 100% indicates the observer can perfectly identify the unaltered chest x-ray in a pair." ], "reasoning": "The table shows that the semantic inpainting model has an observer accuracy of 66.66%, which is the highest among the listed models.", "statement": "The semantic inpainting model has the highest observer accuracy in the 2AFC test.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: A comparison of different approaches.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/330.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: A comparison of different approaches." ], "reasoning": "The proposed approach reduces the number of encrypted model copies to 1 and the ciphertext size to 2x2, improving efficiency compared to Threshold HE and Multi-Key HE.", "description": "Suppose we have. model owners and P clients, as illustrated in Fig. 1, for every client we need to generate a joint key for this client and the group of. model owners; that is, we will need to produce and maintain P joint keys. This also means that each model owner has to provide P copies of the encrypted model to the cloud.. For our collaborative machine learning scenario, an MKHE solution will require each ciphertext to be extended to. + 1 different keys (i.e., the model owners keys plus the key of the requesting client) before any computation. The efficiency of the system is affected especially if the number of model owners is large because it proportionally increases the ciphertext size.. Also, we reduce the number of encrypted models from P to 1 and the dimension of the extended ciphertexts from (N + 1) × 2 to 2 × 2.. Table. provides a summary of our comparison.. This approach also means that each model owner must prepare P encrypted copies of their models to the cloud, one for each distinct joint key.. As an alternative, participants can encrypt their inputs under their individual keys, but extend them to additional keys using the MKHE scheme at evaluation. This way, the model owners delegate one encryption copy of their models to the cloud. When a client Ci requests an evaluation, each SWHE ciphertext c = (c0, c1) is extended to one in the form ¯c = {cM1| . . . |cMN |cCi } ∈ R2(N +1)q under the set of. + 1 keys {pkM1 , . . . , pkMN , pkCi}, resulting in linear expansion of the ciphertext size. Our proposed approach leverages both the threshold and multi-key HE techniques. There is no need to set up a joint key for each client with the model owners, and ciphertexts are extended only under two different keys instead of. + 1 keys resulting in a reduction in the ciphertext size.. reduces the number of encryption of the same model, and (iii) reduces the ciphertext size with dimension reduction from (N + 1) × 2 to 2 × 2. We presented the detail design of this approach and analyzed the complexity and security." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: A comparison of different approaches.'\nProblem: 'How does the ciphertext size of the proposed approach compare to the Multi-Key HE approach?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/330.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: A comparison of different approaches." ], "reasoning": "The table shows that the ciphertext size for the proposed approach is 2x2, while for the Multi-Key HE approach, it is (N+1)x2. This indicates that the proposed approach has a smaller ciphertext size.", "question": "What is the ciphertext size of the proposed approach?", "answer": "So, the answer is {\"answer\": \"2x2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed approach requires more encrypted model copies than the Threshold HE approach.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/330.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: A comparison of different approaches." ], "reasoning": "The table indicates that the proposed approach requires 1 encrypted model copy, while the Threshold HE approach requires P encrypted model copies. Therefore, the statement is incorrect.", "statement": "The proposed approach requires more encrypted model copies than the Threshold HE approach.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Percentage of configurations with at least one infeasible joint angle η and the mean and standard deviations of the position error εpos and orientation error εori for different penalty factors λ in DT. The number of training samples is 800 (3-DoF) or 32000 (6-DoF and 15-DoF).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/331.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Percentage of configurations with at least one infeasible joint angle η and the mean and standard deviations of the position error εpos and orientation error εori for different penalty factors λ in DT. The number of training samples is 800 (3-DoF) or 32000 (6-DoF and 15-DoF)." ], "reasoning": "Increasing the penalty factor λ reduces the percentage of infeasible predictions η, but significantly increases position and orientation errors, especially for the 6-DoF mechanism.", "description": "Results are listed in Table II. We can see that DT yields lots of infeasible predictions if we do not take joint limits into account (λ = 0). The more joints are used,. the higher the chance to violate at least one joint limit. If the penalty factor λ is increased, the percentage of infeasible predictions η decreases to about 1% and for higher values of λ potentially even further. The downside of this approach is a significant increase of the position and orientation errors. The highest increase is noticed for the 6-DoF mechanism where the errors increase approximately by a factor of four. This means that the sample efficiency of DT is worse if we consider joint limits as we need a much larger number of training samples" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Percentage of configurations with at least one infeasible joint angle η and the mean and standard deviations of the position error εpos and orientation error εori for different penalty factors λ in DT. The number of training samples is 800 (3-DoF) or 32000 (6-DoF and 15-DoF).'\nProblem: 'What is the percentage of infeasible predictions η for the 6-DoF mechanism when λ is 1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/331.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Percentage of configurations with at least one infeasible joint angle η and the mean and standard deviations of the position error εpos and orientation error εori for different penalty factors λ in DT. The number of training samples is 800 (3-DoF) or 32000 (6-DoF and 15-DoF)." ], "reasoning": "From the table, for the 6-DoF mechanism with λ = 1, the percentage of infeasible predictions η is 3.30%.", "question": "What is the percentage of infeasible predictions η for the 6-DoF mechanism when λ is 1?", "answer": "So, the answer is {\"answer\": \"3.30%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe position error εpos for the 15-DoF mechanism with λ = 5 is 1.31e-2±2.67e-2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/331.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Percentage of configurations with at least one infeasible joint angle η and the mean and standard deviations of the position error εpos and orientation error εori for different penalty factors λ in DT. The number of training samples is 800 (3-DoF) or 32000 (6-DoF and 15-DoF)." ], "reasoning": "According to the table, the position error εpos for the 15-DoF mechanism with λ = 5 is indeed 1.31e-2±2.67e-2.", "statement": "The position error εpos for the 15-DoF mechanism with λ = 5 is 1.31e-2±2.67e-2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Solve rates for error thresholds εpos<0.01m and εori<0.03rad and different test sets. Numbers in brackets below DT denote the number of training samples.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/332.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Solve rates for error thresholds εpos<0.01m and εori<0.03rad and different test sets. Numbers in brackets below DT denote the number of training samples." ], "reasoning": "The solve rates are lower for singular configurations with the 6- and 15-DoF manipulators, but DT is more robust against singularities.", "description": "Results are shown in Table III. The analytical solution is exact.. While there occur no failures for the simple planar 3-DoF mechanism, solve rates are clearly lower for singular configurations with the 6- and 15-DoF manipulators. On the dataset from which near-singular configurations were removed we also see an increase of the overall solve rate. DT seems to be more robust against singularities. While there are slight deviations in the solve rate, no clear trend can be seen so that we assume that these differences are not caused by singularities." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Solve rates for error thresholds εpos<0.01m and εori<0.03rad and different test sets. Numbers in brackets below DT denote the number of training samples.'\nProblem: 'What is the solve rate for the 6-DoF mechanism using DT (6400) on the singular test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/332.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Solve rates for error thresholds εpos<0.01m and εori<0.03rad and different test sets. Numbers in brackets below DT denote the number of training samples." ], "reasoning": "The table shows the solve rate for the 6-DoF mechanism using DT (6400) on the singular test set as 95.26%.", "question": "What is the solve rate for the 6-DoF mechanism using DT (6400) on the singular test set?", "answer": { "answer": "95.26%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe solve rate for the 15-DoF mechanism using DT (256000) on the nonsingular test set is higher than 98%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/332.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Solve rates for error thresholds εpos<0.01m and εori<0.03rad and different test sets. Numbers in brackets below DT denote the number of training samples." ], "reasoning": "The table shows the solve rate for the 15-DoF mechanism using DT (256000) on the nonsingular test set as 98.12%, which is higher than 98%.", "statement": "The solve rate for the 15-DoF mechanism using DT (256000) on the nonsingular test set is higher than 98%.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison of BOCPD, CS-BOCPD, and CBOCPD with NLL and MSE on synthetic and Gazebo robot simulation datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/333.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of BOCPD, CS-BOCPD, and CBOCPD with NLL and MSE on synthetic and Gazebo robot simulation datasets." ], "reasoning": "CBOCPD outperforms BOCPD and CS-BOCPD in terms of both NLL and MSE across all environments, demonstrating the effectiveness of the proposed likelihood ratio test.", "description": "We compare the proposed CBOCPD with BOCPD and CS (CUSUM)-BOCPD.. Table 1: Comparison of BOCPD, CS-BOCPD, and CBOCPD with NLL and MSE on synthetic and Gazebo robot simulation datasets.. The last three columns in Table 1 show that CBOCPD outperforms other methods in all environments. Interestingly, the results show that CUSUM test does not help BOCPD much when the variance decreases as in Env3 but proposed likelihood ratio test improves BOCPD in all the cases.. The first two columns in Table 1 present the quantitative results from 100 runs of each algorithm after training on the first 100 points and testing on the remaining 300 points. CBOCPD outperforms BOCPD in terms of both negative log-likelihood (NLL) and mean squared error (MSE)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison of BOCPD, CS-BOCPD, and CBOCPD with NLL and MSE on synthetic and Gazebo robot simulation datasets.'\nProblem: 'Which method has the lowest NLL in Gazebo:Env3?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/333.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of BOCPD, CS-BOCPD, and CBOCPD with NLL and MSE on synthetic and Gazebo robot simulation datasets." ], "reasoning": "By comparing the NLL values in the Gazebo:Env3 column, CBOCPD has the lowest NLL value of -0.99±0.47.", "question": "Which method has the lowest NLL in Gazebo:Env3?", "answer": "So, the answer is {\"answer\": \"CBOCPD\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCS-BOCPD has the highest MSE in LEN-CHANGE.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/333.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of BOCPD, CS-BOCPD, and CBOCPD with NLL and MSE on synthetic and Gazebo robot simulation datasets." ], "reasoning": "By comparing the MSE values in the LEN-CHANGE column, BOCPD has the highest MSE value of 0.47±0.32, not CS-BOCPD.", "statement": "CS-BOCPD has the highest MSE in LEN-CHANGE.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results of predictive performance on Nile data, Well Log Data and Snow Data. The results are provided with 95% of confidence interval and the p-value of the null hypothesis that a method is equivalent to the best performing method according to NLL, using a one sided t-test.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/334.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of predictive performance on Nile data, Well Log Data and Snow Data. The results are provided with 95% of confidence interval and the p-value of the null hypothesis that a method is equivalent to the best performing method according to NLL, using a one sided t-test." ], "reasoning": "CBOCPD outperforms other algorithms in Nile and Snow data, while showing competitive results in Well Log data.", "description": "Table 2: Results of predictive performance on Nile data, Well Log Data and Snow Data. The results are provided with 95% of confidence interval and the p-value of the null hypothesis that a method is equivalent to the best performing method according to NLL, using a one sided t-test.. Further, we compare our proposed algorithm with autoregressive GP (ARGP), autoregressive GP with BOCPD (ARGPBOCPD), GP time series with BOCPD (BOCPD) and GP time series model (GPTS).. Table 2 shows the predictive performance of our proposed algorithm compared to other GP based BOCPD methods. In Nile data, we see that combining BOCPD method with GP improves the performance. CBOCPD further improves the performance by leveraging the proposed statistical hypothesis tests and outperforms all other algorithms. In Well Log data, the nonlinear temporal correlations within each regime give a slight advantage to underlying predictive model of ARGP. However, CBOCPD still shows the competitive result. In Snowfall data, CBOCPD well detects the difference in noise levels and achieves the best performance in MSE." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results of predictive performance on Nile data, Well Log Data and Snow Data. The results are provided with 95% of confidence interval and the p-value of the null hypothesis that a method is equivalent to the best performing method according to NLL, using a one sided t-test.'\nProblem: 'Which method achieved the best performance in MSE for Snow Data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/334.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of predictive performance on Nile data, Well Log Data and Snow Data. The results are provided with 95% of confidence interval and the p-value of the null hypothesis that a method is equivalent to the best performing method according to NLL, using a one sided t-test." ], "reasoning": "The table shows the MSE values for Snow Data. CBOCPD has the lowest MSE value of 6.34±0.27, indicating the best performance.", "question": "Which method achieved the best performance in MSE for Snow Data?", "answer": "So, the answer is {\"answer\": \"CBOCPD\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCBOCPD achieved the best performance in NLL for Nile Data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/334.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of predictive performance on Nile data, Well Log Data and Snow Data. The results are provided with 95% of confidence interval and the p-value of the null hypothesis that a method is equivalent to the best performing method according to NLL, using a one sided t-test." ], "reasoning": "The table shows the NLL values for Nile Data. CBOCPD has the lowest NLL value of 0.00±0.80, indicating the best performance.", "statement": "CBOCPD achieved the best performance in NLL for Nile Data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparison of the longitudinal performances of the MLP and CNN controllers (in m/s).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/335.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of the longitudinal performances of the MLP and CNN controllers (in m/s)." ], "reasoning": "The CNN model has smoother control signals and smaller longitudinal errors compared to the MLP model.", "description": "The control signals of the CNN model remains always smooth and within a reasonable range of values. Secondly, both the longitudinal errors are smaller for the CNN than the MLP as shown in Table 2." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparison of the longitudinal performances of the MLP and CNN controllers (in m/s).'\nProblem: 'Which model has a smaller standard deviation in longitudinal errors?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/335.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of the longitudinal performances of the MLP and CNN controllers (in m/s)." ], "reasoning": "By comparing the standard deviation values in the table, the CNN model has a standard deviation of 0.46, which is smaller than the MLP model's standard deviation of 0.70.", "question": "Which model has a smaller standard deviation in longitudinal errors?", "answer": "So, the answer is {\"answer\": \"CNN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe CNN model has a higher RMS value than the MLP model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/335.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of the longitudinal performances of the MLP and CNN controllers (in m/s)." ], "reasoning": "The table shows that the RMS value for the CNN model is 0.60, which is lower than the RMS value for the MLP model, which is 0.76.", "statement": "The CNN model has a higher RMS value than the MLP model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Recognition error rate (mean±std%) on CIFAR10 without data augmentation and CIFAR10+ with data augmentation. Every result is evaluated five times.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/336.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Recognition error rate (mean±std%) on CIFAR10 without data augmentation and CIFAR10+ with data augmentation. Every result is evaluated five times." ], "reasoning": "Arc has the lowest CIFAR10+ score and ArcFace has a lower CIFAR10+ score than most other methods.", "description": "Table 6 and 7 reveal Arc is the best and ArcFace is better." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Recognition error rate (mean±std%) on CIFAR10 without data augmentation and CIFAR10+ with data augmentation. Every result is evaluated five times.'\nProblem: 'Which method has the lowest CIFAR10+ score and how does ArcFace compare to other methods?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/336.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Recognition error rate (mean±std%) on CIFAR10 without data augmentation and CIFAR10+ with data augmentation. Every result is evaluated five times." ], "reasoning": "Arc has the lowest CIFAR10+ score of 5.23±0.19, and ArcFace has a CIFAR10+ score of 5.34±0.12, which is lower than most other methods.", "question": "Which method has the lowest CIFAR10+ score and how does ArcFace compare to other methods?", "answer": "So, the answer is {\"answer\": \"Arc\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nArcFace has a lower CIFAR10+ score than Softmax.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/336.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Recognition error rate (mean±std%) on CIFAR10 without data augmentation and CIFAR10+ with data augmentation. Every result is evaluated five times." ], "reasoning": "ArcFace has a CIFAR10+ score of 5.34±0.12, while Softmax has a CIFAR10+ score of 5.73±0.22.", "statement": "ArcFace has a lower CIFAR10+ score than Softmax.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Number of Venues and Venue Movements for each city'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/337.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Number of Venues and Venue Movements for each city" ], "reasoning": "The table summarizes the number of venues and total venue movements for Chicago and New York in 2018.", "description": "The check-in information describes an aggregated count of all movements from one venue to another, separated by month and five time intervals: Morning, Midday, Afternoon, Night and Overnight. In the collected data, we focus on the two cities mentioned above for the year of 2018. The aggregated number of venues and number of total venue movements are summarised in Table I." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Number of Venues and Venue Movements for each city'\nProblem: 'Which city had more unique movements in 2018?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/337.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Number of Venues and Venue Movements for each city" ], "reasoning": "Compare the 'Unique Movements' values for Chicago and New York. Chicago has 5,396,723 unique movements, while New York has 5,296,809 unique movements. Therefore, Chicago had more unique movements.", "question": "Which city had more unique movements in 2018?", "answer": "So, the answer is {\"answer\": \"Chicago\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nNew York had more venues than Chicago in 2018.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/337.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Number of Venues and Venue Movements for each city" ], "reasoning": "Compare the 'Venues' values for Chicago and New York. Chicago has 13,904 venues, while New York has 32,971 venues. Therefore, New York had more venues.", "statement": "New York had more venues than Chicago in 2018.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Feature Correlation Analysis for the New York and Chicago'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/338.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Feature Correlation Analysis for the New York and Chicago" ], "reasoning": "Crime count is highly correlated with Crime Event History and region risk analysis features, especially overnight, while many POI densities are negatively correlated.", "description": "The correlation values between the features and crime count are illustrated in Table II for Chicago and New York City across different periods of a day. We observe that crime count is highly correlated with Crime Event History in both cities over many time intervals. According to the Near Repeat theory , crime tends to happen in the vicinity of past crime events. The features derived from region risk analysis also have good correlation with crime count for both cities, especially overnight. Such positive correlation highlights the importance of such features in crime count. Surprisingly, many POI densities are negatively correlated with crime count. However, venue diversity has good correlation with crime which verifies that mixed land use has good impact on crime." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Feature Correlation Analysis for the New York and Chicago'\nProblem: 'Which feature has the highest correlation with crime count in New York City during the morning?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/338.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Feature Correlation Analysis for the New York and Chicago" ], "reasoning": "To find the feature with the highest correlation with crime count in New York City during the morning, look at the 'New York Morning' column and identify the highest value. The highest value is 0.983, which corresponds to the 'Historical Density' feature.", "question": "Which feature has the highest correlation with crime count in New York City during the morning?", "answer": "So, the answer is {\"answer\": \"Historical Density\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTotal POI density has a positive correlation with crime count in Chicago during the night.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/338.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Feature Correlation Analysis for the New York and Chicago" ], "reasoning": "To verify this statement, look at the 'Chicago Night' column for the 'Total POI' feature. The correlation value is 0.638, which is positive.", "statement": "Total POI density has a positive correlation with crime count in Chicago during the night.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: MAE and RMSE for different times of the day for Chicago and New York after applying Linear Regression'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/339.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: MAE and RMSE for different times of the day for Chicago and New York after applying Linear Regression" ], "reasoning": "The experimental results show that the model with Region Risk based features has less error compared to the model without Region Risk based features in most time intervals, and RF shows consistent and better performance among the three regression algorithms.", "description": "The experimental results using different regression algorithms including LR, NB and RF are shown in tables III to V respectively. The experimental results show that the model with Region Risk based features has less error compare to the model without Region Risk based features in most of the time intervals. Among three different regression algorithm, RF shows the consistent and better performance result." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: MAE and RMSE for different times of the day for Chicago and New York after applying Linear Regression'\nProblem: 'Which city and time of day combination has the lowest MAE when Region Risk features are present?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/339.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: MAE and RMSE for different times of the day for Chicago and New York after applying Linear Regression" ], "reasoning": "To find the lowest MAE when Region Risk features are present, we need to look at the MAE values in the table where 'All Features Present' is indicated. The lowest MAE value is 1.068, which corresponds to Chicago 1 in the morning.", "question": "Which time of day has the lowest MAE when Region Risk features are present in Chicago 1?", "answer": { "answer": "Morning" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model with Region Risk based features has a lower RMSE in New York 1 during the night compared to when these features are omitted.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/339.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: MAE and RMSE for different times of the day for Chicago and New York after applying Linear Regression" ], "reasoning": "To verify this statement, we compare the RMSE values for New York 1 during the night. With all features present, the RMSE is 16.558, and with Region Risk features omitted, the RMSE is 17.515. Since 16.558 is lower than 17.515, the statement is supported.", "statement": "The model with Region Risk based features has a lower RMSE in New York 1 during the night compared to when these features are omitted.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparison with Baselines (%: percentage of training data)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/340.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison with Baselines (%: percentage of training data)" ], "reasoning": "DL-based approaches like CoNet and I-DARec outperform non-deep models, especially in highly sparse datasets, with I-DARec achieving the best performance among the baselines.", "description": "Table 2 reports the comparison results among the selected baselines for CDR. Note that in each domain pair, the chosen categories are as irrelevant as possible, so that we can better examine the performance of our model on learning and transfering knowledge from the rating matrix only. It is obvious that DL-based approaches (i.e., CoNet and DARec) are superior to non-deep models. This can be explained by the. ability of nonlinearity modelling from the deep neural networks. Also, MF-based mehtods perform better when the source domain dataset is relatively dense, but the datasets we consider in this work are of high sparsity (over 99.8%). For example, in the Office Products and Movies & TV dataset with 90% training data, CoNet gains 2.38% RMSE improvement over FM-CDCF and I-DARec achieves 3.45% over FMCDCF. This result shows that DL-based methods are more suitable for extreme sparse datasets, which is in consistency with the findings in [Hu, 2018]. Furthermore, we notice that CoNet performs better than U-DARec as U-DARec adopts adversarial training to balance the extraction of shared user rating patterns and the pursuit of high rating prediction accuracy. This weakness is overcome by I-DARec which minimizes the losses of domain classifier and rating predictor at the same time. Thus, as we can see, I-DARec achieves the best performance among the baselines." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparison with Baselines (%: percentage of training data)'\nProblem: 'Which model achieves the best performance in the Office Products & Movies and TV 90 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/340.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison with Baselines (%: percentage of training data)" ], "reasoning": "The table shows the RMSE values for different models across various datasets. The lowest RMSE value in the Office Products & Movies and TV 90 dataset is 0.9582, achieved by I-DARec.", "question": "Which model achieves the best performance in the Office Products & Movies and TV 90 dataset?", "answer": "So, the answer is {\"answer\": \"I-DARec\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCoNet performs better than U-DARec in the Sports and Outdoors & CDs and Vinyl 90 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/340.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison with Baselines (%: percentage of training data)" ], "reasoning": "The table shows that in the Sports and Outdoors & CDs and Vinyl 90 dataset, CoNet has an RMSE of 1.9587, while U-DARec has an RMSE of 1.9808. Therefore, CoNet performs better than U-DARec in this dataset.", "statement": "CoNet performs better than U-DARec in the Sports and Outdoors & CDs and Vinyl 90 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Resynthesis accuracies. Comparison of DDSP models to SOTA WaveRNN model provided the same conditioning information. The supervised DDSP Autoencoder and WaveRNN models use the fundamental frequency from a pretrained CREPE model, while the unsupervised DDSP autoencoder learns to infer the frequency from the audio during training.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/341.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Resynthesis accuracies. Comparison of DDSP models to SOTA WaveRNN model provided the same conditioning information. The supervised DDSP Autoencoder and WaveRNN models use the fundamental frequency from a pretrained CREPE model, while the unsupervised DDSP autoencoder learns to infer the frequency from the audio during training." ], "reasoning": "The supervised DDSP autoencoder outperforms the WaveRNN model in both loudness and F0 L1 metrics, while the unsupervised DDSP autoencoder performs better than the supervised WaveRNN model in F0 L1.", "description": "Table 1: Resynthesis accuracies. Comparison of DDSP models to SOTA WaveRNN model provided the same conditioning information. The supervised DDSP Autoencoder and WaveRNN models use the fundamental frequency from a pretrained CREPE model, while the unsupervised DDSP autoencoder learns to infer the frequency from the audio during training.. For the NSynth dataset, we quantitatively compare the quality of DDSP resynthesis with that of a state-of-the-art baseline using WaveRNN (Hantrakul et al., 2019). The models are comparable as they are trained on the same data, provided the same conditioning, and both targeted towards realtime synthesis applications. In Table 1, we compute loudness and fundamental frequency (F0) L1 metrics described in Section. of the appendix. Despite the strong performance of the baseline, the supervised DDSP autoencoder still outperforms it, especially in F0 L1. This is not unexpected, as the additive synthesizer directly uses the conditioning frequency to synthesize audio. The unsupervised DDSP autoencoder must learn to infer its own F0 conditioning signal directly from the audio. As described in Section B.4, we improve optimization by also adding a perceptual loss in the form of a pretrained CREPE network (Kim et al., 2018). While not as accurate as the supervised DDSP version, the model does a fair job at learning to generate sounds with the correct frequencies without supervision, outperforming the supervised WaveRNN model." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Resynthesis accuracies. Comparison of DDSP models to SOTA WaveRNN model provided the same conditioning information. The supervised DDSP Autoencoder and WaveRNN models use the fundamental frequency from a pretrained CREPE model, while the unsupervised DDSP autoencoder learns to infer the frequency from the audio during training.'\nProblem: 'Which model has the lowest F0 L1 metric?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/341.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Resynthesis accuracies. Comparison of DDSP models to SOTA WaveRNN model provided the same conditioning information. The supervised DDSP Autoencoder and WaveRNN models use the fundamental frequency from a pretrained CREPE model, while the unsupervised DDSP autoencoder learns to infer the frequency from the audio during training." ], "reasoning": "The table shows the F0 L1 metrics for different models. The supervised DDSP autoencoder has the lowest F0 L1 metric of 0.02.", "question": "Which model has the lowest F0 L1 metric?", "answer": { "answer": "DDSP Autoencoder (Supervised)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe supervised DDSP autoencoder has a higher loudness L1 metric than the WaveRNN model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/341.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Resynthesis accuracies. Comparison of DDSP models to SOTA WaveRNN model provided the same conditioning information. The supervised DDSP Autoencoder and WaveRNN models use the fundamental frequency from a pretrained CREPE model, while the unsupervised DDSP autoencoder learns to infer the frequency from the audio during training." ], "reasoning": "The table shows that the supervised DDSP autoencoder has a loudness L1 metric of 0.07, which is lower than the WaveRNN model's loudness L1 metric of 0.10.", "statement": "The supervised DDSP autoencoder has a higher loudness L1 metric than the WaveRNN model.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison between DAP/NMF/RPCA/KAM on numerical metrics: SDR/SIR/LSD. For SDR/SIR, higher is better and for LSD, smaller is better. The best results on the three metrics are from our method, DAP.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/342.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison between DAP/NMF/RPCA/KAM on numerical metrics: SDR/SIR/LSD. For SDR/SIR, higher is better and for LSD, smaller is better. The best results on the three metrics are from our method, DAP." ], "reasoning": "DAP outperforms NMF, RPCA, and KAM in SDR, SIR, and LSD metrics.", "description": "DAP outperforms these three BSS methods in all three numerical metrics, as shown in Table 1." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison between DAP/NMF/RPCA/KAM on numerical metrics: SDR/SIR/LSD. For SDR/SIR, higher is better and for LSD, smaller is better. The best results on the three metrics are from our method, DAP.'\nProblem: 'Which method has the highest SIR value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/342.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison between DAP/NMF/RPCA/KAM on numerical metrics: SDR/SIR/LSD. For SDR/SIR, higher is better and for LSD, smaller is better. The best results on the three metrics are from our method, DAP." ], "reasoning": "To find the method with the highest SIR value, compare the SIR values of NMF, RPCA, KAM, and Proposed DAP. The highest value is 3.859 for Proposed DAP.", "question": "Which method has the highest SIR value?", "answer": { "answer": "Proposed DAP" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe KAM method has the lowest SDR value.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/342.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison between DAP/NMF/RPCA/KAM on numerical metrics: SDR/SIR/LSD. For SDR/SIR, higher is better and for LSD, smaller is better. The best results on the three metrics are from our method, DAP." ], "reasoning": "To verify this statement, compare the SDR values of NMF, RPCA, KAM, and Proposed DAP. The lowest SDR value is -6.369 for NMF, not KAM.", "statement": "The KAM method has the lowest SDR value.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Denoising performance using the mixed test set, which includes confocal, two-photon, and wide-field microscopy images. PSNR (dB), SSIM, and denoising time (seconds) are obtained by averaging over 48 noise realizations in the mixed test set for each of 5 noise levels. Results of DnCNN and Noise2Noise are obtained by training on dataset with all noise levels. All 50 captures of each FOV (except the 19-th FOV which is reserved for test) are included in the training set, with 1 (DnCNN) or 2 (Noise2Noise) samples of which randomly selected from each FOV when forming mini-batches during training for 400 epochs. †Note that test time for deep learning models on GPU is faster in orders of magnitude, i.e. 0.62 ms for DnCNN and 0.99 ms for Noise2Noise on single GPU in our experiment.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/343.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Denoising performance using the mixed test set, which includes confocal, two-photon, and wide-field microscopy images. PSNR (dB), SSIM, and denoising time (seconds) are obtained by averaging over 48 noise realizations in the mixed test set for each of 5 noise levels. Results of DnCNN and Noise2Noise are obtained by training on dataset with all noise levels. All 50 captures of each FOV (except the 19-th FOV which is reserved for test) are included in the training set, with 1 (DnCNN) or 2 (Noise2Noise) samples of which randomly selected from each FOV when forming mini-batches during training for 400 epochs. †Note that test time for deep learning models on GPU is faster in orders of magnitude, i.e. 0.62 ms for DnCNN and 0.99 ms for Noise2Noise on single GPU in our experiment." ], "reasoning": "Deep learning models outperform traditional denoising methods in terms of PSNR and SSIM across all noise levels, despite being blind to noise levels.", "description": "The benchmark denoising results on the mixed test set is shown in Table 2, including PSNR, structural similarity index (SSIM) and denoising time. From the table, BM3D (in combination with VST) is still the most versatile traditional denoising algorithm regarding its high PSNR and relatively fast denoising speed. PURE-LET, though its PSNR is not the highest, is the fastest denoising method among all the benchmarked algorithms thanks to its specific design for Poisson-Gaussian denoising. Finally, deep learning models outperform the other 8 methods by a significant margin in all noise levels, both in terms of PSNR and SSIM, even thought they are blind to noise levels. This is different from the observation made before in [1, 24], probably because the nature of Poisson dominated noise is different from Gaussian noise while most of the denoising methods are developed for Gaussian noise model. Even if we applied the VST before Gaussian denoising, the transformed noise may still be different from a pure Gaussian one. More importantly, here the models are re-trained with our FMD dataset instead of pre-trained on other datasets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Denoising performance using the mixed test set, which includes confocal, two-photon, and wide-field microscopy images. PSNR (dB), SSIM, and denoising time (seconds) are obtained by averaging over 48 noise realizations in the mixed test set for each of 5 noise levels. Results of DnCNN and Noise2Noise are obtained by training on dataset with all noise levels. All 50 captures of each FOV (except the 19-th FOV which is reserved for test) are included in the training set, with 1 (DnCNN) or 2 (Noise2Noise) samples of which randomly selected from each FOV when forming mini-batches during training for 400 epochs. †Note that test time for deep learning models on GPU is faster in orders of magnitude, i.e. 0.62 ms for DnCNN and 0.99 ms for Noise2Noise on single GPU in our experiment.'\nProblem: 'Which denoising method has the highest PSNR for 16 raw images and what is its denoising time?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/343.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Denoising performance using the mixed test set, which includes confocal, two-photon, and wide-field microscopy images. PSNR (dB), SSIM, and denoising time (seconds) are obtained by averaging over 48 noise realizations in the mixed test set for each of 5 noise levels. Results of DnCNN and Noise2Noise are obtained by training on dataset with all noise levels. All 50 captures of each FOV (except the 19-th FOV which is reserved for test) are included in the training set, with 1 (DnCNN) or 2 (Noise2Noise) samples of which randomly selected from each FOV when forming mini-batches during training for 400 epochs. †Note that test time for deep learning models on GPU is faster in orders of magnitude, i.e. 0.62 ms for DnCNN and 0.99 ms for Noise2Noise on single GPU in our experiment." ], "reasoning": "From the table, the highest PSNR for 16 raw images is 41.57, achieved by Noise2Noise. The denoising time for Noise2Noise is 2.94 seconds.", "question": "Which denoising method has the highest PSNR for 16 raw images?", "answer": { "answer": "Noise2Noise" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPURE-LET is the fastest denoising method among all the benchmarked algorithms.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/343.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Denoising performance using the mixed test set, which includes confocal, two-photon, and wide-field microscopy images. PSNR (dB), SSIM, and denoising time (seconds) are obtained by averaging over 48 noise realizations in the mixed test set for each of 5 noise levels. Results of DnCNN and Noise2Noise are obtained by training on dataset with all noise levels. All 50 captures of each FOV (except the 19-th FOV which is reserved for test) are included in the training set, with 1 (DnCNN) or 2 (Noise2Noise) samples of which randomly selected from each FOV when forming mini-batches during training for 400 epochs. †Note that test time for deep learning models on GPU is faster in orders of magnitude, i.e. 0.62 ms for DnCNN and 0.99 ms for Noise2Noise on single GPU in our experiment." ], "reasoning": "The table shows that PURE-LET has the shortest denoising time of 2.61 seconds, making it the fastest denoising method.", "statement": "PURE-LET is the fastest denoising method among all the benchmarked algorithms.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Better sample ratio (%) comparing with NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on Universal-150. All results are larger than 50%, which shows that our DAP can achieve better results than the compared methods on a majority of testing samples.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/344.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Better sample ratio (%) comparing with NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on Universal-150. All results are larger than 50%, which shows that our DAP can achieve better results than the compared methods on a majority of testing samples." ], "reasoning": "The DAP method outperforms NMF, RPCA, and KAM in terms of SDR, SIR, and LSD on a majority of testing samples.", "description": "For example, compared to the NMF in terms of SDR, the ratio is 0.753, which shows that our method is better than the NMF on 75.3% testing samples. The comparison results are shown in Table 2. We can see that our DAP achieve better results on a majority of testing samples comparing to NMF/RPCA/KAM." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Better sample ratio (%) comparing with NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on Universal-150. All results are larger than 50%, which shows that our DAP can achieve better results than the compared methods on a majority of testing samples.'\nProblem: 'What is the SDR value when comparing DAP to RPCA?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/344.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Better sample ratio (%) comparing with NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on Universal-150. All results are larger than 50%, which shows that our DAP can achieve better results than the compared methods on a majority of testing samples." ], "reasoning": "To find the SDR value for DAP vs. RPCA, look at the intersection of the SDR row and the DAP vs. RPCA column.", "question": "What is the SDR value when comparing DAP to RPCA?", "answer": "So, the answer is {\"answer\": \"66.0\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDAP achieves a higher SIR value compared to KAM.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/344.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Better sample ratio (%) comparing with NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on Universal-150. All results are larger than 50%, which shows that our DAP can achieve better results than the compared methods on a majority of testing samples." ], "reasoning": "To verify this statement, compare the SIR values for DAP vs. KAM and DAP vs. other methods. The SIR value for DAP vs. KAM is 66.7, which is lower than the SIR value for DAP vs. NMF (76.7) but higher than DAP vs. RPCA (62.7).", "statement": "DAP achieves a higher SIR value compared to KAM.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison between DAP/NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on a standard source separation benchmark (vincent2007oracle). For SDR/SIR, higher is better and for LSD, smaller is better. The best results on the three metrics are from our method, DAP.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/345.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison between DAP/NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on a standard source separation benchmark (vincent2007oracle). For SDR/SIR, higher is better and for LSD, smaller is better. The best results on the three metrics are from our method, DAP." ], "reasoning": "The proposed DAP method outperforms NMF, RPCA, and KAM in terms of SDR, SIR, and LSD.", "description": "As shown in Table 3, We can see that our method outperforms the three compared methods in terms of SDR, SIR, and LSD." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Comparison between DAP/NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on a standard source separation benchmark (vincent2007oracle). For SDR/SIR, higher is better and for LSD, smaller is better. The best results on the three metrics are from our method, DAP.'\nProblem: 'Which method has the highest SIR value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/345.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison between DAP/NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on a standard source separation benchmark (vincent2007oracle). For SDR/SIR, higher is better and for LSD, smaller is better. The best results on the three metrics are from our method, DAP." ], "reasoning": "By comparing the SIR values of all methods, the proposed DAP method has the highest SIR value of 0.82.", "question": "Which method has the highest SIR value?", "answer": "So, the answer is {\"answer\": \"Proposed DAP\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe KAM method has the lowest SDR value.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/345.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison between DAP/NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on a standard source separation benchmark (vincent2007oracle). For SDR/SIR, higher is better and for LSD, smaller is better. The best results on the three metrics are from our method, DAP." ], "reasoning": "By comparing the SDR values of all methods, RPCA has the lowest SDR value of -4.49, not KAM.", "statement": "The KAM method has the lowest SDR value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Better sample ratio (%) comparing with NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on the source separation benchmark (vincent2007oracle). Most ratio results are larger than 50%, which shows that our DAP can achieve overall better results than the compared methods.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/346.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Better sample ratio (%) comparing with NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on the source separation benchmark (vincent2007oracle). Most ratio results are larger than 50%, which shows that our DAP can achieve overall better results than the compared methods." ], "reasoning": "The table shows that DAP achieves better overall results compared to NMF, RPCA, and KAM in terms of SDR, SIR, and LSD.", "description": "The Table 4 illustrates better ratio results. We can also find that our DAP still achieves overall better results." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Better sample ratio (%) comparing with NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on the source separation benchmark (vincent2007oracle). Most ratio results are larger than 50%, which shows that our DAP can achieve overall better results than the compared methods.'\nProblem: 'Which method has the highest LSD score when compared to DAP?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/346.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Better sample ratio (%) comparing with NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on the source separation benchmark (vincent2007oracle). Most ratio results are larger than 50%, which shows that our DAP can achieve overall better results than the compared methods." ], "reasoning": "To find the method with the highest LSD score when compared to DAP, look at the LSD row and identify the highest value among the columns. The highest LSD score is 95.0, which corresponds to DAP vs. KAM.", "question": "Which method has the highest LSD score when compared to DAP?", "answer": "So, the answer is {\"answer\": \"KAM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDAP achieves the highest SIR score when compared to NMF.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/346.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Better sample ratio (%) comparing with NMF/RPCA/KAM in terms of numerical metrics: SDR/SIR/LSD on the source separation benchmark (vincent2007oracle). Most ratio results are larger than 50%, which shows that our DAP can achieve overall better results than the compared methods." ], "reasoning": "To verify this statement, look at the SIR row and the DAP vs. NMF column. The SIR score for DAP vs. NMF is 55.0, which is the highest among the SIR scores in the table.", "statement": "DAP achieves the highest SIR score when compared to NMF.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Performance evaluation of the baseline CNN architecture [2] trained with multi-class formulated objective against both the approximated ground truth (equivalent to the last observed sample annotation) as well as the actual ground-truth for Opportunity dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/347.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Performance evaluation of the baseline CNN architecture [2] trained with multi-class formulated objective against both the approximated ground truth (equivalent to the last observed sample annotation) as well as the actual ground-truth for Opportunity dataset." ], "reasoning": "The HAR system's performance is lower when evaluated against actual ground truth labels compared to approximate ground truth labels, indicating the presence of multiple activities within the sliding window segments.", "description": "In Table I, we report performance of the resulting HAR system by comparing the generated predictions against both the approximate ground truth (obtained from the last observed sample annotation) as well as the actual multi-label ground truth for Opportunity dataset.. In Table I, the lower performance measures obtained from the evaluation against the actual ground truth labels as compared with the approximated ground truth suggest that there are sensory segments in the HAR dataset that convey measurements of multiple activities in the time span of the sliding window—see the result for MR in Table I." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Performance evaluation of the baseline CNN architecture [2] trained with multi-class formulated objective against both the approximated ground truth (equivalent to the last observed sample annotation) as well as the actual ground-truth for Opportunity dataset.'\nProblem: 'What is the Fmean score when the CNN model is evaluated against the actual labels?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/347.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Performance evaluation of the baseline CNN architecture [2] trained with multi-class formulated objective against both the approximated ground truth (equivalent to the last observed sample annotation) as well as the actual ground-truth for Opportunity dataset." ], "reasoning": "The table shows the performance of the CNN model with different evaluation ground truths. The Fmean score for the CNN model evaluated against actual labels is 0.793.", "question": "What is the Fmean score when the CNN model is evaluated against the actual labels?", "answer": "So, the answer is {\"answer\": \"0.793\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe MR score is higher when the CNN model is evaluated against the last sample's label compared to actual labels.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/347.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Performance evaluation of the baseline CNN architecture [2] trained with multi-class formulated objective against both the approximated ground truth (equivalent to the last observed sample annotation) as well as the actual ground-truth for Opportunity dataset." ], "reasoning": "The table shows that the MR score is 87.4% when evaluated against the last sample's label and 54.7% when evaluated against actual labels, indicating a higher MR score for the last sample's label.", "statement": "The MR score is higher when the CNN model is evaluated against the last sample's label compared to actual labels.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Comparison of our proposed Deep Auto-Set network against the baselines according to the obtained exact match ratio for each dataset. The best results are highlighted with boldface. Note that for the Actitracker dataset, sensor segments with cardinality of 0 (corresponding to Null segments) and 3 do not exist.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/348.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Comparison of our proposed Deep Auto-Set network against the baselines according to the obtained exact match ratio for each dataset. The best results are highlighted with boldface. Note that for the Actitracker dataset, sensor segments with cardinality of 0 (corresponding to Null segments) and 3 do not exist." ], "reasoning": "Deep Auto-Set consistently outperforms baseline models on Actitracker and Opportunity datasets in terms of F1 score and exact match ratio.", "description": "Moreover, the match ratios in Table II suggest that Deep Auto-Set is a robust activity recognition system capable of: i) distinguishing different activity classes accurately (implied from MR0 and MR1 values); ii) identifying activity transition segments (implied from MR2 values); as well as iii) recognizing short duration human activities (implied from MR3 values).. The performance results of our Deep Auto-Set network and the baseline models on the two HAR representative datasets are shown in Table II and Table III for different evaluation metrics.. From the reported results, we can see that our novel Deep Auto-Set network consistently outperforms the baselines on Actitracker and Opportunity datasets in terms of both F1score and exact match ratio performance metrics." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Comparison of our proposed Deep Auto-Set network against the baselines according to the obtained exact match ratio for each dataset. The best results are highlighted with boldface. Note that for the Actitracker dataset, sensor segments with cardinality of 0 (corresponding to Null segments) and 3 do not exist.'\nProblem: 'Which model has the highest MR2 value for the Actitracker dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/348.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Comparison of our proposed Deep Auto-Set network against the baselines according to the obtained exact match ratio for each dataset. The best results are highlighted with boldface. Note that for the Actitracker dataset, sensor segments with cardinality of 0 (corresponding to Null segments) and 3 do not exist." ], "reasoning": "By examining the MR2 values for the Actitracker dataset, we see that the highest MR2 value is 75.1%, achieved by the (Ours) Auto-Set model.", "question": "Which model has the highest MR2 value for the Actitracker dataset?", "answer": "So, the answer is {\"answer\": \"(Ours) Auto-Set\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe (Ours) Auto-Set model has the highest MR0 value for the Opportunity dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/348.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Comparison of our proposed Deep Auto-Set network against the baselines according to the obtained exact match ratio for each dataset. The best results are highlighted with boldface. Note that for the Actitracker dataset, sensor segments with cardinality of 0 (corresponding to Null segments) and 3 do not exist." ], "reasoning": "By examining the MR0 values for the Opportunity dataset, we see that the highest MR0 value is 80.2%, achieved by the (Ours) Auto-Set model.", "statement": "The (Ours) Auto-Set model has the highest MR0 value for the Opportunity dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Comparison of prediction errors (lower is better)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/349.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Comparison of prediction errors (lower is better)" ], "reasoning": "The DKM method gave the best performance with the lowest heading errors and guaranteed kinematic feasibility, while the UM-heading model showed better heading and infeasibility metrics compared to UM.", "description": "The results are presented in Table I.. the na¨ıve non-learned UKF baseline gave the largest errors, Expectedly,. Furthermore, we found that poly-n methods of varying degrees yielded very different results.. Never. The second- and third-degree polynomials (constant-acceleration and constant-jerk models, respectively) further improved position errors, although with degraded heading and feasibility metrics.. Moving forward, we see that the four unconstrained models, UM, UM-velo, UM-LSTM, and UM-heading gave strong results in terms of the center position prediction.. the results are near-identical, and both have about 30% of their trajectories being kinematically infeasible,. It is interesting to discuss UM-LSTM, which showed good performance in terms of position errors but poor in terms of heading and infeasibility metrics.. The UM-heading model, which predicts both positions and headings, has better heading and infeasibility metrics com. pared to UM. However, around 20% of the predicted are still kinematically infeasible. However, when compared to unconstrained models it is clear that the method underperformed, although it is competitive in terms of kinematic feasibility.. The DKM method gave the best performance, as it improves the heading errors by a large margin and its trajectories are guaranteed to be kinematically feasible." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Comparison of prediction errors (lower is better)'\nProblem: 'Which method had the lowest heading error at 3 seconds and was kinematically feasible?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/349.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Comparison of prediction errors (lower is better)" ], "reasoning": "From the table, the DKM method had the lowest heading error at 3 seconds (3.38 degrees) and 0% infeasibility, indicating it was kinematically feasible.", "question": "Which method had the lowest heading error at 3 seconds and was kinematically feasible?", "answer": { "answer": "DKM" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe UM-heading model had the lowest position error at 3 seconds.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/349.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Comparison of prediction errors (lower is better)" ], "reasoning": "The table shows that the UM-heading model had a position error of 1.39 meters at 3 seconds, but the UM and DKM methods both had a lower position error of 1.34 meters at 3 seconds.", "statement": "The UM-heading model had the lowest position error at 3 seconds.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Comparison of normalized reconstruction MSE losses.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/350.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Comparison of normalized reconstruction MSE losses." ], "reasoning": "The multitask learning approach results in lower reconstruction loss compared to the LSTM autoencoder, with a 33% lower error in the 'combined' category.", "description": "Table II. shows the average normalized reconstruction loss on test data for different modalities between our approach (multitask learning) and LSTM autoencoder. We can notice that, our approach results in lower reconstruction loss with 33% lower error (0.2) compared to the standalone autoencoder (0.3) in the 'combined' category." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Comparison of normalized reconstruction MSE losses.'\nProblem: 'What is the reconstruction loss for the 'Pedal Pressure' feature using the multitask learning approach?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/350.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Comparison of normalized reconstruction MSE losses." ], "reasoning": "The table shows the reconstruction loss for different features using both the LSTM autoencoder and the multitask learning approach. For the 'Pedal Pressure' feature, the multitask learning approach has a reconstruction loss of 0.0003.", "question": "What is the reconstruction loss for the 'Pedal Pressure' feature using the multitask learning approach?", "answer": "So, the answer is {\"answer\": \"0.0003\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe multitask learning approach has a higher reconstruction loss for the 'Yaw' feature compared to the LSTM autoencoder.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/350.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Comparison of normalized reconstruction MSE losses." ], "reasoning": "The table shows that the reconstruction loss for the 'Yaw' feature is 0.0003 for both the multitask learning approach and the LSTM autoencoder. Therefore, the statement is refuted.", "statement": "The multitask learning approach has a higher reconstruction loss for the 'Yaw' feature compared to the LSTM autoencoder.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Comparison of qualitative results by analyzing top 0.01% scores.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/351.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Comparison of qualitative results by analyzing top 0.01% scores." ], "reasoning": "The scaled approach classifies fewer 'Normal' cases and more 'Speed' anomalies compared to the unscaled approach and autoencoder, indicating better performance.", "description": "We have summarized our analysis results in Table III. We can notice that, while autoencoder classifies U-turns as anomalous, our approach (both scaled and unscaled) does not. We can also notice that our scaled approach classifies lesser 'Normal' and more 'Speed' anomalies. By comparing, the percentage of 'Normal' cases classified as anomalous, we can tell that. scaled approach performs better than unscaled, which in turns performs better than standalone autoencoder approach." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Comparison of qualitative results by analyzing top 0.01% scores.'\nProblem: 'Which approach classifies the highest percentage of 'Lane Change' anomalies?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/351.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Comparison of qualitative results by analyzing top 0.01% scores." ], "reasoning": "By comparing the percentages of 'Lane Change' anomalies classified by each approach, we see that 'Our Approach' has the highest percentage at 47.8%.", "question": "Which approach classifies the highest percentage of 'Lane Change' anomalies?", "answer": "So, the answer is {\"answer\": \"Our Approach\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LSTM Autoencoder classifies the highest percentage of 'Normal' cases as anomalous.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/351.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Comparison of qualitative results by analyzing top 0.01% scores." ], "reasoning": "By comparing the percentages of 'Normal' cases classified as anomalous, the LSTM Autoencoder has the highest percentage at 26.1%.", "statement": "The LSTM Autoencoder classifies the highest percentage of 'Normal' cases as anomalous.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Comparison of normalized reconstruction MSE losses (without u-turn data).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/352.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Comparison of normalized reconstruction MSE losses (without u-turn data)." ], "reasoning": "Our approach consistently achieves lower reconstruction error across all features compared to the multi-class LSTM autoencoder.", "description": "The final (after 300 epochs of training) reconstruction loss on the eval/test data for each feature is summarized in Table IV. We can notice that our approach achieves lower reconstruction error for all features, compared to multi-class approach." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Comparison of normalized reconstruction MSE losses (without u-turn data).'\nProblem: 'What is the difference in reconstruction loss for the 'Combined' feature between the multi-class LSTM autoencoder and our approach?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/352.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Comparison of normalized reconstruction MSE losses (without u-turn data)." ], "reasoning": "The reconstruction loss for the 'Combined' feature using the multi-class LSTM autoencoder is 0.4058, while our approach has a reconstruction loss of 0.2456. The difference is 0.4058 - 0.2456.", "question": "What is the difference in reconstruction loss for the 'Combined' feature between the multi-class LSTM autoencoder and our approach?", "answer": "So, the answer is {\"answer\": \"0.1602\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur approach has a lower reconstruction loss for the 'Pedal Angle' feature compared to the multi-class LSTM autoencoder.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/352.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Comparison of normalized reconstruction MSE losses (without u-turn data)." ], "reasoning": "The table shows that the reconstruction loss for the 'Pedal Angle' feature is 0.0013 for our approach and 0.0014 for the multi-class LSTM autoencoder, indicating that our approach has a lower reconstruction loss.", "statement": "Our approach has a lower reconstruction loss for the 'Pedal Angle' feature compared to the multi-class LSTM autoencoder.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: Comparison of percentage of u-turns detected (qualitative results) by analyzing top anomaly scores.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/353.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Comparison of percentage of u-turns detected (qualitative results) by analyzing top anomaly scores." ], "reasoning": "Our approach with scaled scores outperforms both our approach with actual scores and the multi-class autoencoder approach, especially in the top percentiles.", "description": "The results are shown in Table V. For our multi-task approach, we have two scenarios—actual reconstruction loss and scaled reconstruction loss. Considering the especially the top percentiles, we can notice that our approach with scaled scores performs better than our approach with normal scores which in turn performs better than multi-class approach. For example, considering the top 0.001 percentile anomaly scores for each approach—our approach with scaled scores is able to detect 7.97% i.e., 61 of a total 765 u-turn windows in test data (consisting of 228802 windows), while this number is 1.7% for our approach with actual scores and only 0.39% for multi-class autoencoder approach." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: Comparison of percentage of u-turns detected (qualitative results) by analyzing top anomaly scores.'\nProblem: 'How many u-turn windows are detected by our approach with scaled scores at the 0.1 percentile?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/353.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Comparison of percentage of u-turns detected (qualitative results) by analyzing top anomaly scores." ], "reasoning": "At the 0.1 percentile, our approach with scaled scores detects 48.63% of the u-turn windows, which is 372 out of 765.", "question": "How many u-turn windows are detected by our approach with scaled scores at the 0.1 percentile?", "answer": "So, the answer is {\"answer\": \"372\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur approach with actual scores detects more u-turn windows at the 0.5 percentile than the multi-class LSTM autoencoder approach.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/353.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Comparison of percentage of u-turns detected (qualitative results) by analyzing top anomaly scores." ], "reasoning": "At the 0.5 percentile, our approach with actual scores detects 52.68% (403/765) of the u-turn windows, while the multi-class LSTM autoencoder approach detects 73.46% (562/765). Therefore, the statement is refuted.", "statement": "Our approach with actual scores detects more u-turn windows at the 0.5 percentile than the multi-class LSTM autoencoder approach.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Sparse representation-based classification accuracy of different methods.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/354.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Sparse representation-based classification accuracy of different methods." ], "reasoning": "The proposed DSRC method consistently outperforms other SRC methods across different datasets, demonstrating significant improvements, especially on more challenging datasets like SVHN and UMDAA-01.", "description": "× We compare our method with the standard SRC method , Kernel SRC (KSRC) , SRC on features extracted from an autoencoder with similar architecture to our network (AESRC), and SRC on features extracted from the state-of-theart pre-trained networks. In our experiment with the pretrained networks, the networks are pre-trained on the Imagenet dataset . For this purpose, we use the following. four popular network architectures: VGG-19 , InceptionV3 , Resnet-50 and Densenet-169 .. The first row of Table II shows the performance of various SRC methods. As can be observed from this table, the proposed method performs significantly better than the other methods including the classical and deep learning-based methods.. The second row in Table II compares the performance of different SRC methods. This table demonstrates the advantage of our method. While the classification task is much more challenging on SVHN than MNIST, the gap between the performance of our method and the second best performance is even more. The next best performing method is VGG19-SRC which performs 14.86% behind the accuracy of our method.. The performance of various SRC methods on the UMDAA01 dataset are tabulated in the third row of Table II. As can be seen, our proposed DSRC method similar to the experiments with SVHN provides remarkable improvements as compared to the other SRC methods. This clearly shows that more. challenging datasets are better represented by our method." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Sparse representation-based classification accuracy of different methods.'\nProblem: 'Which SRC method performs the second best on the UMDAA-01 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/354.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Sparse representation-based classification accuracy of different methods." ], "reasoning": "To find the second best performing SRC method on the UMDAA-01 dataset, look at the accuracy values in the UMDAA-01 row. The highest value is for DSRC (93.39), and the second highest value is for Resnet50-SRC (91.84).", "question": "Which SRC method performs the second best on the UMDAA-01 dataset?", "answer": "So, the answer is {\"answer\": \"Resnet50-SRC\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe DSRC method performs better than all other methods on the USPS dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/354.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Sparse representation-based classification accuracy of different methods." ], "reasoning": "The table shows that the DSRC method has the highest accuracy (96.25) on the USPS dataset compared to all other methods listed.", "statement": "The DSRC method performs better than all other methods on the USPS dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: The classification accuracy corresponding to the ablation study. N/C refers to the cases where the learning process did not converge.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/355.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: The classification accuracy corresponding to the ablation study. N/C refers to the cases where the learning process did not converge." ], "reasoning": "The regularization norm on the coefficient matrix, whether (cid:96)1 or (cid:96)2, does not significantly impact classification performance, but norms smaller than 1 lead to instability and convergence issues.", "description": "Table III reveals that while the regularization norm on the coefficient matrix is selected between (cid:96)1 and (cid:96)2, it does not have much effect on the performance of the classification task.. However, in our experiments, we observed that for norms smaller than 1, the problem is not stable and often does not converges.. In addition, DSC-SRC cannot provide a. desirable performance.. it is possible that testing features shape an isolated group that does not have a strong connection to the training features. This makes it more difficult to estimate a label for the test samples." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: The classification accuracy corresponding to the ablation study. N/C refers to the cases where the learning process did not converge.'\nProblem: 'What is the classification performance of DSRC and DSRC2 on the USPS dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/355.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: The classification accuracy corresponding to the ablation study. N/C refers to the cases where the learning process did not converge." ], "reasoning": "The table shows the classification performance of different methods on the USPS dataset. DSRC has a performance of 96.25, and DSRC2 also has a performance of 96.25.", "question": "What is the classification performance of DSRC and DSRC2 on the USPS dataset?", "answer": "So, the answer is {\"answer\": \"96.25\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDSRC-SRC provides better performance than DSRC on the USPS dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/355.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: The classification accuracy corresponding to the ablation study. N/C refers to the cases where the learning process did not converge." ], "reasoning": "The table shows that DSRC-SRC has a performance of 78.25, while DSRC has a performance of 96.25 on the USPS dataset. Therefore, DSRC-SRC does not provide better performance than DSRC.", "statement": "DSRC-SRC provides better performance than DSRC on the USPS dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Average Vitality score of each annotator calculated on individual classes'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/356.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Average Vitality score of each annotator calculated on individual classes" ], "reasoning": "Annotator 1 had the most disagreement with the rest of the team, particularly on the person class, with an average Vitality Score of −0.124.", "description": "The quality metrics calculated for each of the 4 annotators in the professional team can be seen in Table 3 and Table 4.. Table 4 shows that Annotator 1 on the professional team, had the most disagreement from the rest of the team with an average Vitality Rating of −0.078. From table 4, we can see that the class that Annotator 1 had the most disagreement on was with the person class, with an average Vitality Score of −0.124 when comparing average overall K-α for the person class to the average K-α with Annotator 1 removed." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Average Vitality score of each annotator calculated on individual classes'\nProblem: 'Which annotator had the most disagreement on the person class in the Stanford Drone dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/356.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Average Vitality score of each annotator calculated on individual classes" ], "reasoning": "From the table, we see that Annotator 1 had the most disagreement on the person class in the Stanford Drone dataset with an average Vitality Score of −0.124.", "question": "Which annotator had the most disagreement on the person class in the Stanford Drone dataset?", "answer": "So, the answer is {\"answer\": \"Annotator 1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAnnotator 1 had the least disagreement on the vehicle class in the Stanford Drone dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/356.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Average Vitality score of each annotator calculated on individual classes" ], "reasoning": "From the table, Annotator 1 had a disagreement score of 0.010 on the vehicle class in the Stanford Drone dataset, which is not the least disagreement score.", "statement": "Annotator 1 had the least disagreement on the vehicle class in the Stanford Drone dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Average and median K-α rating of MTurk workers for Stanford Drone images annotated by both the professional team and MTurk workers'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/357.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Average and median K-α rating of MTurk workers for Stanford Drone images annotated by both the professional team and MTurk workers" ], "reasoning": "The K-α values for MTurk groups were lower than those observed from the professional team, despite restrictions on participation.", "description": "annotate. Each image was annotated by 10 workers from each MTurk group (expert, experienced, novice). K-α was calculated from all 10 workers in each experience group. The average and median K-α values for each group are found in Table 5.. Table 5 shows that despite the restrictions placed on MTurk workers who could participate on the annotation tasks, the K-α among MTurk groups was much lower than that observed from the professional team." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Average and median K-α rating of MTurk workers for Stanford Drone images annotated by both the professional team and MTurk workers'\nProblem: 'Which MTurk group had the highest median K-α value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/357.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Average and median K-α rating of MTurk workers for Stanford Drone images annotated by both the professional team and MTurk workers" ], "reasoning": "To find the MTurk group with the highest median K-α value, compare the median values for Expert, Experienced, and Novice groups. The Experienced group has the highest median K-α value of 0.392.", "question": "Which MTurk group had the highest median K-α value?", "answer": "So, the answer is {\"answer\": \"Experienced\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average K-α value for the Novice group was higher than that of the Expert group.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/357.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Average and median K-α rating of MTurk workers for Stanford Drone images annotated by both the professional team and MTurk workers" ], "reasoning": "The table shows that the average K-α value for the Novice group is 0.086, while for the Expert group it is 0.310. Since 0.086 is lower than 0.310, the statement is refuted.", "statement": "The average K-α value for the Novice group was higher than that of the Expert group.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: The results of detection and threading comparing with other 3D object detection methods.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/358.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The results of detection and threading comparing with other 3D object detection methods." ], "reasoning": "The table shows that the A2-Net method achieved the highest mAP and coverage, outperforming other methods by a large margin.", "description": "The quantitative amino acid detection results are reported in Table. 1.. Table. 1 shows that our method outperformed them by a large margin." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: The results of detection and threading comparing with other 3D object detection methods.'\nProblem: 'Which method achieved the highest mAP and coverage?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/358.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The results of detection and threading comparing with other 3D object detection methods." ], "reasoning": "By examining the table, we see that the A2-Net method has the highest mAP of 0.891 and the highest coverage of 0.91.", "question": "Which method achieved the highest mAP and coverage?", "answer": "So, the answer is {\"answer\": \"A2-Net\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe A2-Net method achieved the highest mAP and coverage.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/358.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The results of detection and threading comparing with other 3D object detection methods." ], "reasoning": "The table shows that the A2-Net method has the highest mAP of 0.891 and the highest coverage of 0.91, confirming the statement.", "statement": "The A2-Net method achieved the highest mAP and coverage.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt1 dataset. Precision, recall, F1-score and number of mis-classified predictions for RetinaNet trained on four different sets of data.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/359.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt1 dataset. Precision, recall, F1-score and number of mis-classified predictions for RetinaNet trained on four different sets of data." ], "reasoning": "RetinaNet models trained with different annotator datasets show varying performance, with higher precision and lower misclassification for Drop Antr 1.", "description": "Results from the same experiments but using ground truth sets gt1 and gt2 are shown in tables 7 and 8 respectively.. the performance looks very different. All versions of RetinaNet showed an increased level of performance for the inter annotator curated datasets, gt1 and gt2, than for the original dataset gt3.. Another observation from these experiments is that for models trained with data from annotators that disagree with the majority, the model produces more candidate predictions for the same algorithm configuration (compare tables 7 and 8)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt1 dataset. Precision, recall, F1-score and number of mis-classified predictions for RetinaNet trained on four different sets of data.'\nProblem: 'Which training dataset for RetinaNet had the highest precision and the lowest number of misclassifications?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/359.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt1 dataset. Precision, recall, F1-score and number of mis-classified predictions for RetinaNet trained on four different sets of data." ], "reasoning": "By comparing the precision and misclassification columns, Drop Antr 1 has the highest precision (0.65) and the lowest number of misclassifications (380).", "question": "Which training dataset for RetinaNet had the highest precision and the lowest number of misclassifications?", "answer": "So, the answer is {\"answer\": \"Drop Antr 1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDrop Antr 3 had the highest recall among the training datasets for RetinaNet.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/359.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt1 dataset. Precision, recall, F1-score and number of mis-classified predictions for RetinaNet trained on four different sets of data." ], "reasoning": "By comparing the recall values, Drop Antr 2 and Drop Antr 4 both have a recall of 0.67, which is higher than Drop Antr 3's recall of 0.64.", "statement": "Drop Antr 3 had the highest recall among the training datasets for RetinaNet.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt2 dataset. Precision, recall, F1-score and number of mis-classified predictions for RetinaNet trained on four different sets of data.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/360.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt2 dataset. Precision, recall, F1-score and number of mis-classified predictions for RetinaNet trained on four different sets of data." ], "reasoning": "RetinaNet models showed improved performance on inter-annotator curated datasets (gt1 and gt2) compared to the original dataset (gt3), and models trained with data from annotators who disagree with the majority produced more candidate predictions.", "description": "Results from the same experiments but using ground truth sets gt1 and gt2 are shown in tables 7 and 8 respectively.. the performance looks very different. All versions of RetinaNet showed an increased level of performance for the inter annotator curated datasets, gt1 and gt2, than for the original dataset gt3.. Another observation from these experiments is that for models trained with data from annotators that disagree with the majority, the model produces more candidate predictions for the same algorithm configuration (compare tables 7 and 8)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 8: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt2 dataset. Precision, recall, F1-score and number of mis-classified predictions for RetinaNet trained on four different sets of data.'\nProblem: 'Which training dataset for RetinaNet had the highest precision and how many misclassifications did it have?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/360.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt2 dataset. Precision, recall, F1-score and number of mis-classified predictions for RetinaNet trained on four different sets of data." ], "reasoning": "By examining the table, 'Drop Antr 1' had the highest precision of 0.70. The number of misclassifications for 'Drop Antr 1' is 375.", "question": "Which training dataset for RetinaNet had the highest precision and how many misclassifications did it have?", "answer": "So, the answer is {\"answer\": \"375\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe training dataset 'Drop Antr 2' had the highest recall.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/360.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt2 dataset. Precision, recall, F1-score and number of mis-classified predictions for RetinaNet trained on four different sets of data." ], "reasoning": "By examining the table, 'Drop Antr 2' had a recall of 0.66, which is not the highest. 'Drop Antr 4' had the highest recall of 0.67.", "statement": "The training dataset 'Drop Antr 2' had the highest recall.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 9: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt1 dataset. Precision, recall, F1-score and number of misclassified predictions.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/361.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt1 dataset. Precision, recall, F1-score and number of misclassified predictions." ], "reasoning": "The performance of RetinaNet varies depending on the inclusion or removal of labeled data from annotators with different degrees of disagreement.", "description": "Tables 9 and 10 show performance results of RetinaNet using ground truth from the best annotators (i.e. gt1, and gt2 sets).. through these experiments we have seen the impact in model performance of the inclusion or removal of labeled data from annotators with larger degree of disagreement (see tables 9 and 10)," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 9: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt1 dataset. Precision, recall, F1-score and number of misclassified predictions.'\nProblem: 'Which annotator's removal resulted in the highest precision for RetinaNet?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/361.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt1 dataset. Precision, recall, F1-score and number of misclassified predictions." ], "reasoning": "By comparing the precision values for each annotator's removal, we see that 'Drop Antr 1' has the highest precision value of 0.66.", "question": "Which annotator's removal resulted in the highest precision for RetinaNet?", "answer": "So, the answer is {\"answer\": \"Drop Antr 1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRemoving annotator 3 resulted in the lowest recall for RetinaNet.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/361.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: RetinaNet trained with 4 datasets (Drop Antr 1, Drop Antr 2, Drop Antr 3, and Drop Antr 4) performance measured against custom ground truth gt1 dataset. Precision, recall, F1-score and number of misclassified predictions." ], "reasoning": "By comparing the recall values for each annotator's removal, we see that 'Drop Antr 3' has the lowest recall value of 0.52.", "statement": "Removing annotator 3 resulted in the lowest recall for RetinaNet.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Evaluation results on CLIC 2019 validation dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/362.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation results on CLIC 2019 validation dataset" ], "reasoning": "The post-processing module improves the PSNR and MS-SSIM metrics for both BPG and H.266 algorithms, with EDSR's rotation trick further enhancing these metrics.", "description": "We choose BPG and H.266 as baselines. For BPG, we mixed quality parameters at 40 and 41; As for H.266, we mixed quality parameters at 35, 36 and 37 for different configure setting. As shown in Table 1, it can be seen the post-processing module improves the PSNR and MS-SSIM metrics for both algorithms. The EDSR's rotation trick elevates about 1 percent of PSNR and 0.2 percent of MS-SSIM." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Evaluation results on CLIC 2019 validation dataset'\nProblem: 'What is the PSNR value for H.266 with post-processing and rotation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/362.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation results on CLIC 2019 validation dataset" ], "reasoning": "To find the PSNR value for H.266 with post-processing and rotation, locate the row labeled 'H.266 + Post + Rotation' and check the PSNR column.", "question": "What is the PSNR value for H.266 with post-processing and rotation?", "answer": "So, the answer is {\"answer\": \"32.10\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BPP value for BPG+Post is higher than for H.266+Post.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/362.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation results on CLIC 2019 validation dataset" ], "reasoning": "To verify this statement, compare the BPP values for 'BPG+Post' and 'H.266 + Post'. The BPP for BPG+Post is 0.148, and for H.266 + Post, it is 0.147.", "statement": "The BPP value for BPG+Post is higher than for H.266+Post.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Average (Mean) test accuracy along with standard deviation (SD) over 15 trials obtained by using squared loss based linear classifier learnt on Sy-De (noise rate p) attribute noise corrupted data. Even though squared loss is theoretically shown to be Sy-De noise robust, it doesn’t show good performance at high noise rates. This could be because the result in Theorem 1 is in expectation. In particular, finite sample size starts showing its effect at high noise rates and the performance deteriorates.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/363.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average (Mean) test accuracy along with standard deviation (SD) over 15 trials obtained by using squared loss based linear classifier learnt on Sy-De (noise rate p) attribute noise corrupted data. Even though squared loss is theoretically shown to be Sy-De noise robust, it doesn’t show good performance at high noise rates. This could be because the result in Theorem 1 is in expectation. In particular, finite sample size starts showing its effect at high noise rates and the performance deteriorates." ], "reasoning": "The table shows the average test accuracy and standard deviation over 15 trials for different noise rates using a squared loss-based linear classifier on various datasets, indicating that performance decreases at higher noise rates.", "description": "In Tables 2 and 3, we present the values which we used to generate the plots in Figure 1. The results are averaged over 15 trials. We observe that at high noise rates, the theoretically proven robustness of squared loss doesn't work because of the finite samples.. Table 2: Average (Mean) test accuracy along with standard deviation (SD) over 15 trials obtained by using squared loss based linear classifier learnt on Sy-De (noise rate p) attribute noise corrupted data. Even though squared loss is theoretically shown to be Sy-De noise robust, it doesn't show good performance at high noise rates. This could be because the" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Average (Mean) test accuracy along with standard deviation (SD) over 15 trials obtained by using squared loss based linear classifier learnt on Sy-De (noise rate p) attribute noise corrupted data. Even though squared loss is theoretically shown to be Sy-De noise robust, it doesn’t show good performance at high noise rates. This could be because the result in Theorem 1 is in expectation. In particular, finite sample size starts showing its effect at high noise rates and the performance deteriorates.'\nProblem: 'What is the mean test accuracy for the Vote dataset at a noise rate of 0.3?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/363.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average (Mean) test accuracy along with standard deviation (SD) over 15 trials obtained by using squared loss based linear classifier learnt on Sy-De (noise rate p) attribute noise corrupted data. Even though squared loss is theoretically shown to be Sy-De noise robust, it doesn’t show good performance at high noise rates. This could be because the result in Theorem 1 is in expectation. In particular, finite sample size starts showing its effect at high noise rates and the performance deteriorates." ], "reasoning": "To find the mean test accuracy for the Vote dataset at a noise rate of 0.3, locate the row corresponding to p=0.3 and the column under 'Vote (232(124,108),16)' labeled 'Mean'. The value is 0.821.", "question": "What is the mean test accuracy for the Vote dataset at a noise rate of 0.3?", "answer": { "answer": "0.821" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe standard deviation for the KR-vs-KP dataset at a noise rate of 0.4 is 0.0393.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/363.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average (Mean) test accuracy along with standard deviation (SD) over 15 trials obtained by using squared loss based linear classifier learnt on Sy-De (noise rate p) attribute noise corrupted data. Even though squared loss is theoretically shown to be Sy-De noise robust, it doesn’t show good performance at high noise rates. This could be because the result in Theorem 1 is in expectation. In particular, finite sample size starts showing its effect at high noise rates and the performance deteriorates." ], "reasoning": "To verify the standard deviation for the KR-vs-KP dataset at a noise rate of 0.4, locate the row corresponding to p=0.4 and the column under 'KR-vs-KP (3196(1569,1527),35)' labeled 'SD'. The value is 0.0393.", "statement": "The standard deviation for the KR-vs-KP dataset at a noise rate of 0.4 is 0.0393.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Average (Mean) test AM value along with standard deviation (SD) over 15 trials obtained by using squared loss based linear classifier learnt on Sy-De (noise rate p) attribute noise corrupted data. Due to the imbalanced nature of SPECT dataset, AM is a more suitable evaluation metric.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/364.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average (Mean) test AM value along with standard deviation (SD) over 15 trials obtained by using squared loss based linear classifier learnt on Sy-De (noise rate p) attribute noise corrupted data. Due to the imbalanced nature of SPECT dataset, AM is a more suitable evaluation metric." ], "reasoning": "The results in Table 3 show that at high noise rates, the robustness of squared loss fails due to finite samples, and AM is used as a metric due to the imbalanced nature of the SPECT dataset.", "description": "In Tables 2 and 3, we present the values which we used to generate the plots in Figure 1. The results are averaged over 15 trials. We observe that at high noise rates, the theoretically proven robustness of squared loss doesn't work because of the finite samples.. Table 3: Average (Mean) test AM value along with standard deviation (SD) over 15 trials obtained by using squared loss based linear classifier learnt on Sy-De (noise rate p) attribute noise corrupted data. Due to the imbalanced nature of SPECT dataset, AM is a more suitable evaluation metric." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Average (Mean) test AM value along with standard deviation (SD) over 15 trials obtained by using squared loss based linear classifier learnt on Sy-De (noise rate p) attribute noise corrupted data. Due to the imbalanced nature of SPECT dataset, AM is a more suitable evaluation metric.'\nProblem: 'What is the mean test AM value for the SPECT dataset at a noise rate of 0.3?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/364.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average (Mean) test AM value along with standard deviation (SD) over 15 trials obtained by using squared loss based linear classifier learnt on Sy-De (noise rate p) attribute noise corrupted data. Due to the imbalanced nature of SPECT dataset, AM is a more suitable evaluation metric." ], "reasoning": "To find the mean test AM value for the SPECT dataset at a noise rate of 0.3, locate the row corresponding to 0.3 and the column for SPECT Mean. The value is 0.586.", "question": "What is the mean test AM value for the SPECT dataset at a noise rate of 0.3?", "answer": "So, the answer is {\"answer\": \"0.586\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe standard deviation for the KR-vs-KP dataset at a noise rate of 0.35 is 0.0261.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/364.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average (Mean) test AM value along with standard deviation (SD) over 15 trials obtained by using squared loss based linear classifier learnt on Sy-De (noise rate p) attribute noise corrupted data. Due to the imbalanced nature of SPECT dataset, AM is a more suitable evaluation metric." ], "reasoning": "To verify this statement, locate the row corresponding to 0.35 and the column for KR-vs-KP SD. The value is indeed 0.0261.", "statement": "The standard deviation for the KR-vs-KP dataset at a noise rate of 0.35 is 0.0261.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Test accuracies and their standard deviation over 5 runs on various image datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/365.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test accuracies and their standard deviation over 5 runs on various image datasets." ], "reasoning": "ANODEs outperform NODEs in terms of test accuracy across MNIST, CIFAR10, and SVHN datasets, indicating better generalization on image datasets.", "description": "As can be seen in Fig. 12 and Table 1, for MNIST, CIFAR10, and SVHN, ANODEs achieve lower test losses and higher test accuracies than NODEs, suggesting that ANODEs also generalize better on image datasets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Test accuracies and their standard deviation over 5 runs on various image datasets.'\nProblem: 'Which model achieves higher test accuracy on the CIFAR10 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/365.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test accuracies and their standard deviation over 5 runs on various image datasets." ], "reasoning": "The table shows test accuracies for both NODE and ANODE models on the CIFAR10 dataset. Comparing the two, ANODE achieves 60.6% while NODE achieves 53.7%.", "question": "Which model achieves higher test accuracy on the CIFAR10 dataset?", "answer": "So, the answer is {\"answer\": \"ANODE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nANODEs achieve higher test accuracy than NODEs on the MNIST dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/365.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test accuracies and their standard deviation over 5 runs on various image datasets." ], "reasoning": "The table shows that ANODEs have a test accuracy of 98.2% on the MNIST dataset, while NODEs have a test accuracy of 96.4%. Therefore, ANODEs achieve higher test accuracy.", "statement": "ANODEs achieve higher test accuracy than NODEs on the MNIST dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table I: Sound Quality Matrix Analysis Results: Average normalized cross-correlation value obtained from comparing the original and generated audio signals for model 1 and 2 in all sound classes'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/366.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table I: Sound Quality Matrix Analysis Results: Average normalized cross-correlation value obtained from comparing the original and generated audio signals for model 1 and 2 in all sound classes" ], "reasoning": "Model 1 generally provides higher correlation values than Model 2, except for the classes breaking, cutting, footsteps, and gunshots.", "description": "For each sound category, we calculate the average normalized cross correlation values between our generated and original audio signals to know how much similarity exists between them. We present the correlation values for models in Table I. We notice all positive cross-correlation values (above 0.5) representing an expected analogy between the ground truth and our obtained result. Apart from the four most temporal action sensitive classes (e.g. breaking, cutting, footsteps, gunshots), model 1 provides higher correlation values than method 2." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table I: Sound Quality Matrix Analysis Results: Average normalized cross-correlation value obtained from comparing the original and generated audio signals for model 1 and 2 in all sound classes'\nProblem: 'Which model has a higher average normalized correlation value for the sound class 'Rain'?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/366.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table I: Sound Quality Matrix Analysis Results: Average normalized cross-correlation value obtained from comparing the original and generated audio signals for model 1 and 2 in all sound classes" ], "reasoning": "To determine which model has a higher average normalized correlation value for the sound class 'Rain', compare the values for 'Rain' in both models. Model 1 has a value of 0.90, while Model 2 has a value of 0.81.", "question": "Which model has a higher average normalized correlation value for the sound class 'Rain'?", "answer": "So, the answer is {\"answer\": \"Model 1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nModel 2 has a higher average normalized correlation value for the sound class 'Clock' than Model 1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/366.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table I: Sound Quality Matrix Analysis Results: Average normalized cross-correlation value obtained from comparing the original and generated audio signals for model 1 and 2 in all sound classes" ], "reasoning": "To verify this statement, compare the average normalized correlation values for the sound class 'Clock' in both models. Model 1 has a value of 0.92, while Model 2 has a value of 0.73.", "statement": "Model 2 has a higher average normalized correlation value for the sound class 'Clock' than Model 1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: The results of threading by DFS-based methods and the proposed MCTS+PBNet.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/367.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The results of threading by DFS-based methods and the proposed MCTS+PBNet." ], "reasoning": "PBNet significantly enhances the performance of both DFS and MCTS threading algorithms, achieving a coverage of 0.91 and an RMSD of 2.0 for MCTS+PBNet.", "description": "It can be seen in Table. 2 shows that both threading algorithms are largely improved with PBNet. PBNet achieved 89.8% accuracy for peptide bond recognition." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: The results of threading by DFS-based methods and the proposed MCTS+PBNet.'\nProblem: 'What is the RMSD value for the DFS d method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/367.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The results of threading by DFS-based methods and the proposed MCTS+PBNet." ], "reasoning": "To find the RMSD value for the DFS d method, locate the row labeled 'DFS d' and then look at the corresponding RMSD column.", "question": "What is the RMSD value for the DFS d method?", "answer": "So, the answer is {\"answer\": \"3.1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe coverage for the MCTS method is higher than that for the DFS o method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/367.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The results of threading by DFS-based methods and the proposed MCTS+PBNet." ], "reasoning": "To verify this statement, compare the coverage values for the MCTS method and the DFS o method. The coverage for MCTS is 0.72, while the coverage for DFS o is 0.65.", "statement": "The coverage for the MCTS method is higher than that for the DFS o method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table II: Top 1 sound class prediction accuracy of existing models'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/368.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table II: Top 1 sound class prediction accuracy of existing models" ], "reasoning": "The average prediction accuracy of the classifier network using original spectrograms is 78.32%, and the proposed models show leading results above 63% for generated sounds.", "description": "The purpose of this qualitative task is to evaluate if semantic information of the sound class are present in our synthesized sound.. To evaluate our classifier performance and for better comparison, we also calculate the average prediction accuracy of the classifier network as 78.32% by using the original spectrograms from our test dataset. Since prior works have not considered the movie sound effects domain, it is difficult to compare with their works directly. We present the prediction accuracy results of the most related sound generation models and our proposed models on the same retrieval task using classifier in Table II and III respectively. Both of our average prediction accuracies for this sound retrieval qualitative experiment show leading results above 63% with our classifier for the generated sounds from AutoFoley Models." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table II: Top 1 sound class prediction accuracy of existing models'\nProblem: 'What is the average prediction accuracy of the model that uses the Flow method and the VEGAS dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/368.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table II: Top 1 sound class prediction accuracy of existing models" ], "reasoning": "The table lists the average prediction accuracies of various models. The model that uses the Flow method and the VEGAS dataset is Zhuo [zhou2017visual], which has an average prediction accuracy of 45.47%.", "question": "What is the average prediction accuracy of the model that uses the Flow method and the VEGAS dataset?", "answer": "Thus, the answer is {\"answer\": \"45.47%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe POCAN model has an average prediction accuracy higher than 40%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/368.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table II: Top 1 sound class prediction accuracy of existing models" ], "reasoning": "The table shows that the POCAN model [zhangvisually] has an average prediction accuracy of 36.32%, which is lower than 40%.", "statement": "The POCAN model has an average prediction accuracy higher than 40%.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table III: Top 1 sound class prediction accuracy of proposed models with AutoFoley Dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/369.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table III: Top 1 sound class prediction accuracy of proposed models with AutoFoley Dataset" ], "reasoning": "The AutoFoley models show leading prediction accuracy results above 63% for generated sounds, with the highest accuracy being 78.32% for real sound tracks.", "description": "The purpose of this qualitative task is to evaluate if semantic information of the sound class are present in our synthesized sound.. To evaluate our classifier performance and for better comparison, we also calculate the average prediction accuracy of the classifier network as 78.32% by using the original spectrograms from our test dataset. Since prior works have not considered the movie sound effects domain, it is difficult to compare with their works directly. We present the prediction accuracy results of the most related sound generation models and our proposed models on the same retrieval task using classifier in Table II and III respectively. Both of our average prediction accuracies for this sound retrieval qualitative experiment show leading results above 63% with our classifier for the generated sounds from AutoFoley Models." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table III: Top 1 sound class prediction accuracy of proposed models with AutoFoley Dataset'\nProblem: 'What is the average prediction accuracy of the AutoFoley (Frame-Sequence Network) model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/369.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table III: Top 1 sound class prediction accuracy of proposed models with AutoFoley Dataset" ], "reasoning": "The table lists the average prediction accuracies for different AutoFoley models. The AutoFoley (Frame-Sequence Network) model has an accuracy of 65.79%.", "question": "What is the average prediction accuracy of the AutoFoley (Frame-Sequence Network) model?", "answer": "So, the answer is {\"answer\": \"65.79%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe AutoFoley (Frame-Relation Network) model has an average prediction accuracy above 65%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/369.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table III: Top 1 sound class prediction accuracy of proposed models with AutoFoley Dataset" ], "reasoning": "The table shows that the AutoFoley (Frame-Relation Network) model has an average prediction accuracy of 63.40%, which is below 65%.", "statement": "The AutoFoley (Frame-Relation Network) model has an average prediction accuracy above 65%.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table IV: Loss and Accuracy Calculation Result'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/370.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table IV: Loss and Accuracy Calculation Result" ], "reasoning": "Model 1 has lower log loss and higher accuracy compared to Model 2 during both training and testing.", "description": "We calculate the average log loss and accuracy during training and testing of our models and display the results in Table IV. For both models, lower training losses are calculated as compared to the test case. Here, Model 1 gives a smaller log loss than Model 2 resulting in increased average accuracy throughout training and testing the network." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table IV: Loss and Accuracy Calculation Result'\nProblem: 'Which model has the highest accuracy during testing?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/370.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table IV: Loss and Accuracy Calculation Result" ], "reasoning": "By comparing the test accuracy values for both models, Model 1 has an accuracy of 0.834, which is higher than Model 2's accuracy of 0.806.", "question": "Which model has the highest accuracy during testing?", "answer": { "answer": "Model 1" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nModel 2 has a higher log loss during training compared to testing.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/370.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table IV: Loss and Accuracy Calculation Result" ], "reasoning": "The table shows that Model 2 has a training log loss of 0.035 and a testing log loss of 0.194, indicating that the training log loss is lower than the testing log loss.", "statement": "Model 2 has a higher log loss during training compared to testing.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table V: Comparison of the average accuracy (%) among the Ablation Models and our proposed method 1 (Frame Sequence Network) on AutoFoley Dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/371.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table V: Comparison of the average accuracy (%) among the Ablation Models and our proposed method 1 (Frame Sequence Network) on AutoFoley Dataset." ], "reasoning": "The proposed Frame Sequence Network outperforms all other ablation models in terms of accuracy, particularly due to its ability to capture useful color and motion features from consecutive video frames.", "description": "The Table V shows the performance comparisons among these ablation models and our proposed Frame Sequence model. There is a noticeable accuracy degradation for the models that are using direct raw images as video frame input to the CNN. This is because they often miss useful color and motion features from consecutive video frames. The use of a simple LSTM design reduces the computation time, however, it provides higher class prediction errors. Though accuracy computed from the last ablation model (including SP, VGG19, FS-LSTM) is close to the proposed Frame-Sequence Network result, our model outperforms all other cascaded models.. To understand the significance of each component of this prediction network, we build the following alternative cascaded ablation models, train them with the AutoFoley dataset, then compute the prediction accuracy in each case and compare with the obtained result from our proposed Model 1 (Frame-Sequence Network):" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table V: Comparison of the average accuracy (%) among the Ablation Models and our proposed method 1 (Frame Sequence Network) on AutoFoley Dataset.'\nProblem: 'Which model has the highest average accuracy and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/371.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table V: Comparison of the average accuracy (%) among the Ablation Models and our proposed method 1 (Frame Sequence Network) on AutoFoley Dataset." ], "reasoning": "The table shows the average accuracy of different models. By comparing the values, the Frame Sequence Network has the highest average accuracy.", "question": "Which model has the highest average accuracy and what is its value?", "answer": "So, the answer is {\"answer\": \"65.79\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAblation Model 3 has an average accuracy higher than 56%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/371.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table V: Comparison of the average accuracy (%) among the Ablation Models and our proposed method 1 (Frame Sequence Network) on AutoFoley Dataset." ], "reasoning": "The table shows that Ablation Model 3 has an average accuracy of 56.04%, which is slightly higher than 56%.", "statement": "Ablation Model 3 has an average accuracy higher than 56%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table VI: Comparison of the average accuracy (%) among the Ablation Models and our proposed method 2 (Frame Relation Network) on AutoFoley Dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/372.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table VI: Comparison of the average accuracy (%) among the Ablation Models and our proposed method 2 (Frame Relation Network) on AutoFoley Dataset." ], "reasoning": "The accuracy improves with the increment of Q, but shows a noticeable computational time increment after setting Q value to 16 compared to 8, resulting in a marginal accuracy improvement. ResNet-50 outperforms VGG19 as the base CNN.", "description": "To evaluate the effects of parameters and models used in our second proposed model (Frame-Relation Network), we train and test the following ablation models and compare their performance.. The detailed comparative results are shown in Table VI. Here, we observe lower accuracies in cases of the 4 frame relation TRN. The accuracy improves with the increment of Q. However, it shows a noticeable computational time increment after setting Q value to 16 compared to 8, resulting a marginal accuracy improvement. Hence, in our proposed network, we do not further increase the Q value above 8. We also find that,. ResNet-50 outperforms VGG19 model as the base CNN." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table VI: Comparison of the average accuracy (%) among the Ablation Models and our proposed method 2 (Frame Relation Network) on AutoFoley Dataset.'\nProblem: 'What is the average accuracy of the Frame Relation Network with TRN for 8 frame relations and ResNet-50 as the base CNN?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/372.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table VI: Comparison of the average accuracy (%) among the Ablation Models and our proposed method 2 (Frame Relation Network) on AutoFoley Dataset." ], "reasoning": "The table shows the average accuracy of different models. The Frame Relation Network with TRN for 8 frame relations and ResNet-50 as the base CNN has an average accuracy of 63.40%.", "question": "What is the average accuracy of the Frame Relation Network with TRN for 8 frame relations and ResNet-50 as the base CNN?", "answer": { "answer": "63.40%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAblation Model 4 has the highest average accuracy among the models listed.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/372.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table VI: Comparison of the average accuracy (%) among the Ablation Models and our proposed method 2 (Frame Relation Network) on AutoFoley Dataset." ], "reasoning": "The table lists the average accuracies of different models. Ablation Model 4, with an average accuracy of 64.27%, has the highest accuracy among the models listed.", "statement": "Ablation Model 4 has the highest average accuracy among the models listed.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table VIII: Human Evaluation Results: Selection percentage of each sound category for the first and second human survey questions'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/373.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table VIII: Human Evaluation Results: Selection percentage of each sound category for the first and second human survey questions" ], "reasoning": "The survey evaluates the quality of synthesized sounds by asking students to select their preferred audio sample based on realism, suitability, noise, and synchronization.", "description": "To evaluate the quality of our synthesized sounds, we conduct a survey among local college students. In the survey, the students are presented a video with two audio samples, the original sound and the synthesised sound. We then ask students to select the option they prefer using four questions: 1) Select the original (more realistic) sample. 2) Select the most suitable sample (most reasonable audio initiated from the video clip). 3) Select the sample with minimum noise. 4) Select the most synchronized sample. We assess the performance of our produced sound tracks for each of the categories.. The detailed human evaluation results (selection percentages of survey queries) for each individual class are presented in Tables VIII and IX for Models 1 and 2 separately." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table VIII: Human Evaluation Results: Selection percentage of each sound category for the first and second human survey questions'\nProblem: 'Which class had the highest selection percentage for Query 1 Method 1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/373.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table VIII: Human Evaluation Results: Selection percentage of each sound category for the first and second human survey questions" ], "reasoning": "To find the class with the highest selection percentage for Query 1 Method 1, we need to compare the percentages in the 'Query 1 Method 1' column. The highest percentage is 90.91% for the Clock class.", "question": "Which class had the highest selection percentage for Query 1 Method 1?", "answer": { "answer": "Clock" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average selection percentage for Query 2 Method 2 is above 50%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/373.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table VIII: Human Evaluation Results: Selection percentage of each sound category for the first and second human survey questions" ], "reasoning": "To verify this statement, we need to check the average selection percentage for Query 2 Method 2. The table shows that the average is 40.63%, which is below 50%.", "statement": "The average selection percentage for Query 2 Method 2 is above 50%.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table IX: Human Evaluation Results: Selection percentage of each sound category for the third and fourth human survey questions'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/374.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table IX: Human Evaluation Results: Selection percentage of each sound category for the third and fourth human survey questions" ], "reasoning": "The survey evaluates synthesized sounds based on realism, suitability, noise, and synchronization, with detailed results for each class presented in Tables VIII and IX for Models 1 and 2.", "description": "To evaluate the quality of our synthesized sounds, we conduct a survey among local college students. In the survey, the students are presented a video with two audio samples, the original sound and the synthesised sound. We then ask students to select the option they prefer using four questions: 1) Select the original (more realistic) sample. 2) Select the most suitable sample (most reasonable audio initiated from the video clip). 3) Select the sample with minimum noise. 4) Select the most synchronized sample. We assess the performance of our produced sound tracks for each of the categories.. The detailed human evaluation results (selection percentages of survey queries) for each individual class are presented in Tables VIII and IX for Models 1 and 2 separately." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table IX: Human Evaluation Results: Selection percentage of each sound category for the third and fourth human survey questions'\nProblem: 'Which class had the highest selection percentage for Query 3 using Method 1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/374.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table IX: Human Evaluation Results: Selection percentage of each sound category for the third and fourth human survey questions" ], "reasoning": "To find the class with the highest selection percentage for Query 3 using Method 1, look at the 'Query 3 Method 1' column and identify the highest value, which is 76.92% for Thunder.", "question": "Which class had the highest selection percentage for Query 3 using Method 1?", "answer": "So, the answer is {\"answer\": \"Thunder\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average selection percentage for Query 4 using Method 2 is higher than 50%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/374.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table IX: Human Evaluation Results: Selection percentage of each sound category for the third and fourth human survey questions" ], "reasoning": "The table shows the average selection percentage for Query 4 using Method 2 is 43.14%, which is lower than 50%.", "statement": "The average selection percentage for Query 4 using Method 2 is higher than 50%.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: State of the art accuracies on multi-class classification, results taken from [12].'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/375.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: State of the art accuracies on multi-class classification, results taken from [12]." ], "reasoning": "The table shows that accuracies on Amazon and Yelp reviews datasets are around 60% to 70%, while the IMDB dataset for movie genre prediction has not surpassed the 50% threshold.", "description": "However, if you look at data from Table 1, which shows state of the art results on multi-class classification dataset, it becomes clear that this is not a solved problem. Accuracies on Amazon and Yelp reviews datasets, where you have to predict the ranking of a review on 1 to 5 scale, hover around 60% to 70%, with nobody beating the 50% threshold on IMDB dataset for movie genre prediction with 15 categories." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: State of the art accuracies on multi-class classification, results taken from [12].'\nProblem: 'Which paper achieved the highest accuracy on the Yelp'15 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/375.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: State of the art accuracies on multi-class classification, results taken from [12]." ], "reasoning": "By examining the Yelp'15 column, the highest accuracy is 71.0%, which is achieved by Yang et al., 2016.", "question": "Which paper achieved the highest accuracy on the Yelp'15 dataset?", "answer": "So, the answer is {\"answer\": \"Yang et al., 2016\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTang et al., 2015 achieved the highest accuracy on the Amazon dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/375.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: State of the art accuracies on multi-class classification, results taken from [12]." ], "reasoning": "By examining the Amazon column, the highest accuracy is 63.6%, which is achieved by Yang et al., 2016, not Tang et al., 2015.", "statement": "Tang et al., 2015 achieved the highest accuracy on the Amazon dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Performance comparison of various CNN architectures on ImageNet-1K dataset. All the evaluations are based on 50,000 images of ImageNet-1K validation dataset. The input resolution is set to 224×224.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/376.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance comparison of various CNN architectures on ImageNet-1K dataset. All the evaluations are based on 50,000 images of ImageNet-1K validation dataset. The input resolution is set to 224×224." ], "reasoning": "ShrinkCNN-A and ShrinkCNN-B models significantly reduce parameters and MACs compared to other models like MobileNetV2, MNasNet-A, and EfficientNet, while maintaining similar top-1 error rates.", "description": "For example, compared to the hand-crafted MobileNetV2 model with ReLU activation, ShrinkCNN-A reduces 48% model parameters and 34% MACs. Compared to MNasNet-A which is crafted by conducting resource-aware neural architecture search, ShrinkCNN-B can further cut off 25% parameters with negligible impact on the top-1 error rate.. Our ShrinkCNN-B requires slightly more MACs mainly because MnasNet-A applies architecture motifs (e.g., squeeze-and-excitation layers) to further increase the efficiency of MACs.. Compared to EfficientNet (Tan and Le 2019) obtained by scaling up MobileNetV2 blocks using automated search, ShrinkCNN-B can save 32% parameters with the similar top-1 error rate and MACs." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Performance comparison of various CNN architectures on ImageNet-1K dataset. All the evaluations are based on 50,000 images of ImageNet-1K validation dataset. The input resolution is set to 224×224.'\nProblem: 'How much does ShrinkCNN-B reduce the number of parameters compared to MNasNet-A?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/376.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance comparison of various CNN architectures on ImageNet-1K dataset. All the evaluations are based on 50,000 images of ImageNet-1K validation dataset. The input resolution is set to 224×224." ], "reasoning": "The description states that ShrinkCNN-B can cut off 25% parameters compared to MNasNet-A. MNasNet-A has 4.8M parameters, so ShrinkCNN-B reduces this by 25%, resulting in 3.6M parameters.", "question": "How much does ShrinkCNN-B reduce the number of parameters compared to MNasNet-A?", "answer": "So, the answer is {\"answer\": \"1.2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nShrinkCNN-B has fewer MACs than MNasNet-A.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/376.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance comparison of various CNN architectures on ImageNet-1K dataset. All the evaluations are based on 50,000 images of ImageNet-1K validation dataset. The input resolution is set to 224×224." ], "reasoning": "The table shows that ShrinkCNN-B has 0.385G MACs, while MNasNet-A has 0.340G MACs. Therefore, ShrinkCNN-B does not have fewer MACs than MNasNet-A.", "statement": "ShrinkCNN-B has fewer MACs than MNasNet-A.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Comparison with ShrinkCNN topology with prior graph topology. We use N=8 nodes when crafting CNN architectures with complete-DAG-based topology, and N=15 nodes when crafting CNN architectures with random graph priors. Experiments on random graph priors are conducted 10 times to reduce randomness as much as possible.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/377.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison with ShrinkCNN topology with prior graph topology. We use N=8 nodes when crafting CNN architectures with complete-DAG-based topology, and N=15 nodes when crafting CNN architectures with random graph priors. Experiments on random graph priors are conducted 10 times to reduce randomness as much as possible." ], "reasoning": "ShrinkCNN achieves higher accuracy compared to other topologies, with up to 1.66% higher than Complete-DAG and up to 3% higher than random graph topologies.", "description": "We craft CNN architectures with the above topology respectively, train them on the full CIFAR-10 dataset with the same hyperparameter setting, and compare their test results with ShrinkCNN in Table 4. Table 4 shows that ShrinkCNN is able to achieve up to 1.66% higher accuracy than the complete-DAG-based topology, and up to 3% higher than the topology from random graphs, thanks to the topological knowledge accumulation in the AutoShrink process." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Comparison with ShrinkCNN topology with prior graph topology. We use N=8 nodes when crafting CNN architectures with complete-DAG-based topology, and N=15 nodes when crafting CNN architectures with random graph priors. Experiments on random graph priors are conducted 10 times to reduce randomness as much as possible.'\nProblem: 'Which topology has the highest accuracy and how does it compare to the Complete-DAG topology?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/377.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison with ShrinkCNN topology with prior graph topology. We use N=8 nodes when crafting CNN architectures with complete-DAG-based topology, and N=15 nodes when crafting CNN architectures with random graph priors. Experiments on random graph priors are conducted 10 times to reduce randomness as much as possible." ], "reasoning": "From the table, ShrinkCNN has the highest accuracy at 93.22%. The Complete-DAG topology has an accuracy of 91.56%. Therefore, ShrinkCNN's accuracy is higher by 1.66%.", "question": "Which topology has the highest accuracy?", "answer": "So, the answer is {\"answer\": \"ShrinkCNN.\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nShrinkCNN has fewer nodes than WS and ER topologies.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/377.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison with ShrinkCNN topology with prior graph topology. We use N=8 nodes when crafting CNN architectures with complete-DAG-based topology, and N=15 nodes when crafting CNN architectures with random graph priors. Experiments on random graph priors are conducted 10 times to reduce randomness as much as possible." ], "reasoning": "According to the table, ShrinkCNN has 8 nodes, while WS and ER topologies both have 15 nodes. Therefore, ShrinkCNN has fewer nodes than both WS and ER topologies.", "statement": "ShrinkCNN has fewer nodes than WS and ER topologies.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Demonstration of efficient RNN cell structures found by AutoShrink algorithm on full Penn Treebank dataset. We used N=6 nodes when crafting RNN architectures with complete-DAG-based topology.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/378.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Demonstration of efficient RNN cell structures found by AutoShrink algorithm on full Penn Treebank dataset. We used N=6 nodes when crafting RNN architectures with complete-DAG-based topology." ], "reasoning": "The ShrinkRNN cell structure achieves a lower perplexity of 56.5 with fewer parameters (23M) compared to the Complete-DAG structure.", "description": "Table 5 indicates that our representative RNN cell structure can achieve a significantly lower (6.6) perplexity with 30% fewer parameters." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Demonstration of efficient RNN cell structures found by AutoShrink algorithm on full Penn Treebank dataset. We used N=6 nodes when crafting RNN architectures with complete-DAG-based topology.'\nProblem: 'Which cell topology achieves a lower perplexity with fewer parameters?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/378.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Demonstration of efficient RNN cell structures found by AutoShrink algorithm on full Penn Treebank dataset. We used N=6 nodes when crafting RNN architectures with complete-DAG-based topology." ], "reasoning": "The table shows that ShrinkRNN has a perplexity of 56.5 and 23 million parameters, which are both lower than the Complete-DAG's perplexity of 63.1 and 33 million parameters.", "question": "Which cell topology achieves a lower perplexity with fewer parameters?", "answer": "So, the answer is {\"answer\": \"ShrinkRNN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Complete-DAG cell topology has fewer parameters than the ShrinkRNN cell topology.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/378.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Demonstration of efficient RNN cell structures found by AutoShrink algorithm on full Penn Treebank dataset. We used N=6 nodes when crafting RNN architectures with complete-DAG-based topology." ], "reasoning": "The table indicates that the Complete-DAG cell topology has 33 million parameters, while the ShrinkRNN cell topology has 23 million parameters, which means the statement is incorrect.", "statement": "The Complete-DAG cell topology has fewer parameters than the ShrinkRNN cell topology.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Quantitative results. Left: negative cross-entropy (mean and standard error over five-fold cross-validation) of the proposed model on SDD with only trajectory information (STCNN-T) or with additional reference images (STCNN-T+I). LSTM is a baseline architecture, see the main text. Right: top 10%-oracle error at different time points of the forecast, see main text for details.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/379.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Quantitative results. Left: negative cross-entropy (mean and standard error over five-fold cross-validation) of the proposed model on SDD with only trajectory information (STCNN-T) or with additional reference images (STCNN-T+I). LSTM is a baseline architecture, see the main text. Right: top 10%-oracle error at different time points of the forecast, see main text for details." ], "reasoning": "Table 1 shows that the 'shot gun' baseline achieves oracle scores at least as good as all other methods for SDD, even without using the reference image.", "description": "Quantitative results for SDD are provided in Table 1. For SDD, Table 1 (right) shows the top-10% oracle error at different time points in the forecasted trajectory, as in [1, 5].. Table 1 (right) shows that this \"shot gun\" baseline achieves oracle scores at least as good as all other methods, despite not even making use of the reference image." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Quantitative results. Left: negative cross-entropy (mean and standard error over five-fold cross-validation) of the proposed model on SDD with only trajectory information (STCNN-T) or with additional reference images (STCNN-T+I). LSTM is a baseline architecture, see the main text. Right: top 10%-oracle error at different time points of the forecast, see main text for details.'\nProblem: 'What is the average oracle error for the LSTM method across all splits?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/379.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Quantitative results. Left: negative cross-entropy (mean and standard error over five-fold cross-validation) of the proposed model on SDD with only trajectory information (STCNN-T) or with additional reference images (STCNN-T+I). LSTM is a baseline architecture, see the main text. Right: top 10%-oracle error at different time points of the forecast, see main text for details." ], "reasoning": "The table provides the oracle error for the LSTM method across different splits and the average value. The average oracle error for the LSTM method is 25.0 ± 0.5.", "question": "What is the average oracle error for the LSTM method across all splits?", "answer": "So, the answer is {\"answer\": \"25.0 ± 0.5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe STCNN-T+I method has the lowest average oracle error among all methods.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/379.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Quantitative results. Left: negative cross-entropy (mean and standard error over five-fold cross-validation) of the proposed model on SDD with only trajectory information (STCNN-T) or with additional reference images (STCNN-T+I). LSTM is a baseline architecture, see the main text. Right: top 10%-oracle error at different time points of the forecast, see main text for details." ], "reasoning": "The table shows the average oracle errors for STCNN-T, STCNN-T+I, and LSTM methods. The STCNN-T+I method has an average oracle error of 21.3 ± 0.8, which is the lowest among the three methods.", "statement": "The STCNN-T+I method has the lowest average oracle error among all methods.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Evaluation of simple document analysis (% GT).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/380.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Evaluation of simple document analysis (% GT)." ], "reasoning": "The table shows that roughly 90% of the relevant areas are correctly detected for both the CVS set and the test data of TRACK A.", "description": "Table. lists the results obtained on the CVS set, as well as for the test data of TRACK A, since relevant regions are provided for this track. In both cases roughly 90% of the releveant areas are correctly detected." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Evaluation of simple document analysis (% GT).'\nProblem: 'What is the difference in the 'leading' scores between the CVS set and the Test data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/380.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Evaluation of simple document analysis (% GT)." ], "reasoning": "To find the difference in the 'leading' scores, subtract the 'leading' score of the Test data from the 'leading' score of the CVS set: 81.58 - 73.85.", "question": "What is the difference in the 'leading' scores between the CVS set and the Test data?", "answer": { "answer": "7.73" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'dbl.-page' score for the Test data is higher than that for the CVS set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/380.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Evaluation of simple document analysis (% GT)." ], "reasoning": "The table shows that the 'dbl.-page' score for the Test data is 95.93, which is higher than the 'dbl.-page' score for the CVS set, which is 94.74.", "statement": "The 'dbl.-page' score for the Test data is higher than that for the CVS set.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: The left table indicates the mean squared error values for the first three joints of the robot at the time the ball is caught while the right table is the mean absolute error for the inferred ball position. A green box represents the best method and a gray box represents methods which are not statistically worse than the best method (Mann-Whitney U, p<0.05). The values 43% and 82% indicate inference is performed after 43% of the interaction is observed (corresponding to before the ball is thrown) and 82% is observed (the ball is partway through its trajectory). The ball itself is not visible for the first 43% as this is when it is occluded by the participant’s hand. The standard error for eBIP is less than ±0.01 in all cases for the joint MSE and ±0.015 for the ball MAE.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/381.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: The left table indicates the mean squared error values for the first three joints of the robot at the time the ball is caught while the right table is the mean absolute error for the inferred ball position. A green box represents the best method and a gray box represents methods which are not statistically worse than the best method (Mann-Whitney U, p<0.05). The values 43% and 82% indicate inference is performed after 43% of the interaction is observed (corresponding to before the ball is thrown) and 82% is observed (the ball is partway through its trajectory). The ball itself is not visible for the first 43% as this is when it is occluded by the participant’s hand. The standard error for eBIP is less than ±0.01 in all cases for the joint MSE and ±0.015 for the ball MAE." ], "reasoning": "The table shows that eBIP consistently achieves the lowest joint prediction MSE errors across different modalities and observation percentages, outperforming other methods.", "description": "both methods fared much better, however, eBIP significantly more so as it achieved the best result in every category (see green box).. The inference errors for the robot joints are shown in Table I, along with the errors for the estimation of the location of the ball at the time of interception.. Results in Table. show that attempting to model the demonstrations with a parametric Gaussian model yields a poor approximation and leads to an incorrect estimate of the initial uncertainty. This is supported by the fact that both BIP (Gaussian prior) and eBIP− (mixture model prior) produce predictions that are many orders of magnitude from the true state.. However, we see from Table. that BIP yields a 1.04 × 1015 joint prediction MSE error when 82% of the ball trajectory is observed while eBIP− only yields a joint prediction MSE of 10.52.. These results also show that both the PF and eBIP− produce worse inference results as the number of active modalities increases. For example, the PF predictions result in a joint MSE of 0.11 when only the trajectory of the ball was observed, but a MSE of 0.26 when all modalities were observed.. Lastly, we observe that the introduction of additional modalities does not always yield an increase in inference accuracy, although it may provide other benefits depending on the modalities. This is evident when comparing the joint MSE prediction errors of eBIP for the {Shoe, IMU} data set and that of the {Shoe, IMU, Head} data set. The introduction of the head modality actually increases the inference error when 43% of the trajectory is observed, which is when the head modality is most relevant." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: The left table indicates the mean squared error values for the first three joints of the robot at the time the ball is caught while the right table is the mean absolute error for the inferred ball position. A green box represents the best method and a gray box represents methods which are not statistically worse than the best method (Mann-Whitney U, p<0.05). The values 43% and 82% indicate inference is performed after 43% of the interaction is observed (corresponding to before the ball is thrown) and 82% is observed (the ball is partway through its trajectory). The ball itself is not visible for the first 43% as this is when it is occluded by the participant’s hand. The standard error for eBIP is less than ±0.01 in all cases for the joint MSE and ±0.015 for the ball MAE.'\nProblem: 'What is the joint prediction MSE error for eBIP when 82% of the ball trajectory is observed?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/381.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: The left table indicates the mean squared error values for the first three joints of the robot at the time the ball is caught while the right table is the mean absolute error for the inferred ball position. A green box represents the best method and a gray box represents methods which are not statistically worse than the best method (Mann-Whitney U, p<0.05). The values 43% and 82% indicate inference is performed after 43% of the interaction is observed (corresponding to before the ball is thrown) and 82% is observed (the ball is partway through its trajectory). The ball itself is not visible for the first 43% as this is when it is occluded by the participant’s hand. The standard error for eBIP is less than ±0.01 in all cases for the joint MSE and ±0.015 for the ball MAE." ], "reasoning": "From the table, locate the row where 82% of the ball trajectory is observed and find the joint prediction MSE error for eBIP in that row.", "question": "What is the joint prediction MSE error for eBIP when 82% of the ball trajectory is observed?", "answer": "So, the answer is {\"answer\": \"0.05\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBIP yields a joint prediction MSE error of 1.04 × 1015 when 82% of the ball trajectory is observed.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/381.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: The left table indicates the mean squared error values for the first three joints of the robot at the time the ball is caught while the right table is the mean absolute error for the inferred ball position. A green box represents the best method and a gray box represents methods which are not statistically worse than the best method (Mann-Whitney U, p<0.05). The values 43% and 82% indicate inference is performed after 43% of the interaction is observed (corresponding to before the ball is thrown) and 82% is observed (the ball is partway through its trajectory). The ball itself is not visible for the first 43% as this is when it is occluded by the participant’s hand. The standard error for eBIP is less than ±0.01 in all cases for the joint MSE and ±0.015 for the ball MAE." ], "reasoning": "The table shows that BIP yields a joint prediction MSE error of 1.04 × 1015 when 82% of the ball trajectory is observed.", "statement": "BIP yields a joint prediction MSE error of 1.04 × 1015 when 82% of the ball trajectory is observed.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Performance of the methods using the Jaccard index and overall accuracy, for the types Boolean, Date, Float, Integer and String.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/382.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of the methods using the Jaccard index and overall accuracy, for the types Boolean, Date, Float, Integer and String." ], "reasoning": "Our method outperforms others in most types, especially in Boolean and integer types, but is slightly worse than Trifacta for date type.", "description": "We present the performance of the methods in Table 2, which indicates that our method performs better than the others for all types, except for the date type where it is slightly worse than Trifacta. In the table ptype denotes the discriminatively trained model, and ptype-hc the version with handcrafted parameters.. Notice that the discriminative training improves the performance, specifically for Boolean and integer types.. Note that the training has a slightly negative effect on float and string types, but it still performs better than the other methods.. It is noticeable from Table 2 that dates are difficult to infer accurately.. The large performance gap for Booleans suggests that our method handles confusions with integers and strings better. Analysis shows that such confusions occur respectively in the presence of {0, 1}, and {Yes, No}. We further note that F#, messytables, and readr perform similarly, especially on floats, integers, and strings; which is most likely explained by the fact that they employ similar heuristics." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Performance of the methods using the Jaccard index and overall accuracy, for the types Boolean, Date, Float, Integer and String.'\nProblem: 'Which method performs the best for Boolean type?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/382.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of the methods using the Jaccard index and overall accuracy, for the types Boolean, Date, Float, Integer and String." ], "reasoning": "By comparing the performance of all methods for Boolean type, we see that 'Method ptype' has the highest score of 0.83.", "question": "Which method performs the best for Boolean type?", "answer": "So, the answer is {\"answer\": \"Method ptype\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMethod ptype-hc performs better than Method Trifacta for float type.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/382.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of the methods using the Jaccard index and overall accuracy, for the types Boolean, Date, Float, Integer and String." ], "reasoning": "The table shows that Method ptype-hc has a score of 0.93 for float type, while Method Trifacta has a score of 0.87.", "statement": "Method ptype-hc performs better than Method Trifacta for float type.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: The percentages of FPs, FNs, and TPs for Trifacta and ptype on type/non-type detection.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/383.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The percentages of FPs, FNs, and TPs for Trifacta and ptype on type/non-type detection." ], "reasoning": "ptype has more false positives and true positives but fewer false negatives compared to Trifacta.", "description": "Lastly, we compare Trifacta and ptype in terms of percentages of TPs, FPs, and FNs which are presented in Table 3, where the labels for ptype are generated by applying a threshold of 0.5 on the posterior distributions. Note that here we aggregate the predictions over the datasets. As per the table, ptype results in a higher number of FPs than Trifacta, but Trifacta produces a higher number of FNs and a lower number of TPs than ptype. Note that here we denote non-type and type entries as Positive and Negative respectively. This indicates that ptype is more likely to identify non-types than Trifacta, but it can also label type entries as non-types more often." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: The percentages of FPs, FNs, and TPs for Trifacta and ptype on type/non-type detection.'\nProblem: 'Which method has a higher number of true positives?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/383.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The percentages of FPs, FNs, and TPs for Trifacta and ptype on type/non-type detection." ], "reasoning": "By comparing the TPs column, ptype has 5.34 true positives while Trifacta has 1.57 true positives.", "question": "Which method has a higher number of true positives?", "answer": "So, the answer is {\"answer\": \"ptype\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTrifacta produces fewer false negatives than ptype.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/383.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The percentages of FPs, FNs, and TPs for Trifacta and ptype on type/non-type detection." ], "reasoning": "The table shows that Trifacta has 3.96 false negatives, whereas ptype has 0.20 false negatives, indicating that Trifacta produces more false negatives.", "statement": "Trifacta produces fewer false negatives than ptype.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Individual Models'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/384.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Individual Models" ], "reasoning": "The trial that achieved 99.79% test accuracy establishes a new state of the art, surpassing the previous state of the art of 99.77%.", "description": "For each of our three experiments, we ran 32 trials, each of which with weights randomly initialized prior to training and, due to the stochastic nature of the data augmentation, a different set of training images. As a result, training progressed to different points in the loss surface resulting in a range of values for the top accuracies that were achieved on the test set. See Table 2. The trial that achieved 99.79% test accuracy establishes a new state of the art for a single model where the previous state of the art was 99.77% . 4 additional trials achieved 99.78% test accuracy, also surpassing the previous state of the art." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Individual Models'\nProblem: 'Which experiment achieved the highest test accuracy and what was the value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/384.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Individual Models" ], "reasoning": "By examining the 'Max' column in the table, the highest test accuracy is 99.79%, which was achieved by the 'Not Learnable' experiment.", "question": "Which experiment achieved the highest test accuracy?", "answer": "So, the answer is {\"answer\": \"Not Learnable\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'Random Init.' experiment achieved a higher maximum test accuracy than the 'Ones Init.' experiment.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/384.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Individual Models" ], "reasoning": "By comparing the 'Max' values in the table, 'Random Init.' achieved a maximum test accuracy of 99.78%, while 'Ones Init.' achieved 99.77%. Therefore, the statement is supported.", "statement": "The 'Random Init.' experiment achieved a higher maximum test accuracy than the 'Ones Init.' experiment.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Best performing model of each type. Values closer to zero are better, best values are highlighted in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/385.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Best performing model of each type. Values closer to zero are better, best values are highlighted in bold." ], "reasoning": "The table shows that LR has the best predictive performance among the models, with GP performing the worst. All models perform better when lanes are known.", "description": "For each model type among GP, DNN, and LR, Table III summarizes the predictive quality of the best performing model as measured through mean RMSE (Eq. 11) over all. experiments.. As may be expected, all models perform better when blocked lanes are known than otherwise.. LR is the best performing model type when lanes are either known or unknown. GP performs much worse than both LR and DNN,. The poor fit of GP. The predictive performance of DNN is a little worse than that of LR,. and they demonstrate (Table III) that higher precision of incident location indeed yields better predictions across all model types." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Best performing model of each type. Values closer to zero are better, best values are highlighted in bold.'\nProblem: 'Which model has the highest mean RMSE when lanes are unknown?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/385.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Best performing model of each type. Values closer to zero are better, best values are highlighted in bold." ], "reasoning": "To find the model with the highest mean RMSE when lanes are unknown, look at the 'Mean RMSE Lanes Unknown' column and identify the highest value. GP has the highest mean RMSE of 23.676.", "question": "Which model has the lowest mean RMSE when lanes are unknown?", "answer": "So, the answer is {\"answer\": \"LR\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDNN performs better than LR when lanes are known.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/385.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Best performing model of each type. Values closer to zero are better, best values are highlighted in bold." ], "reasoning": "To verify this statement, compare the mean RMSE values for DNN and LR when lanes are known. DNN has a mean RMSE of 6.328, while LR has a mean RMSE of 6.290. Since LR has a lower RMSE, the statement is refuted.", "statement": "DNN performs better than LR when lanes are known.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Performance of Mordinary under normal vs. incident conditions'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/386.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Performance of Mordinary under normal vs. incident conditions" ], "reasoning": "Mordinary's performance deteriorates under incident conditions compared to normal conditions, regardless of access to uplink and downlink information.", "description": "we measure how worse Mordinary performs on incident conditions, for which it was not trained. The results are summarized in Table IV, and we see that Mordinary deteriorates in comparison with its performance under normal conditions. The deterioration occurs whether or not Mordinary has access to information about uplink. and downlink D." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Performance of Mordinary under normal vs. incident conditions'\nProblem: 'What is the difference in RMSE between normal conditions without uplink and downlink information and incident conditions with uplink and downlink information?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/386.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Performance of Mordinary under normal vs. incident conditions" ], "reasoning": "To find the difference in RMSE, subtract the RMSE value for normal conditions without uplink and downlink information (5.259) from the RMSE value for incident conditions with uplink and downlink information (9.513).", "question": "What is the difference in RMSE between normal conditions without uplink and downlink information and incident conditions with uplink and downlink information?", "answer": { "answer": "4.254" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMordinary's MSD is worse under incident conditions without uplink and downlink information compared to normal conditions with uplink and downlink information.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/386.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Performance of Mordinary under normal vs. incident conditions" ], "reasoning": "The table shows that the MSD for incident conditions without uplink and downlink information is 0.440, which is worse than the MSD for normal conditions with uplink and downlink information, which is -0.183.", "statement": "Mordinary's MSD is worse under incident conditions without uplink and downlink information compared to normal conditions with uplink and downlink information.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: The Kendall’s Tau (KTau) of Peephole, E2EPP and the proposed algorithms on the NASBench dataset with different proportions of training samples.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/387.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The Kendall’s Tau (KTau) of Peephole, E2EPP and the proposed algorithms on the NASBench dataset with different proportions of training samples." ], "reasoning": "The proposed method with loss function L achieves the best KTau performance with limited training data, while the proposed method with L1 loss is better with more training data.", "description": "The experimental results are shown in Tab. 2.. we focus on the second column when using only 0.1% (424 models) of the NASBench dataset as training set. Different proportions are used in the experiment for integrity. The results show that the proposed encoding method can represent an architecture better than without using it, and the KTau indicator increases about 0.14 when using MSE loss and 0.05 when using pairwise loss. When using pairwise loss instead of element-wise MSE loss, the KTau indicator increases about 0.26 when using only the type matrix as feature, and about 0.17 when using the proposed feature tensor. It means that pairwise loss is better than MSE loss at ranking regardless of input feature.. Peephole. shows better result than proposed v1 method. However, it performs worse than proposed v2 method. E2EPP used random forest as predictor, which has advantages only when the training samples are extremely rare. When using limited training data, the proposed method with loss function L (Eq. 5) achieves the best KTau performance, while the proposed. method with L1 loss (Eq. 3) is better when more training data is used. The results show that generating features with continuity has advantageous to model ranking when little training data is used, which is often the case in reality." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: The Kendall’s Tau (KTau) of Peephole, E2EPP and the proposed algorithms on the NASBench dataset with different proportions of training samples.'\nProblem: 'Which method achieves the highest KTau indicator when using 50% of the NASBench dataset as the training set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/387.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The Kendall’s Tau (KTau) of Peephole, E2EPP and the proposed algorithms on the NASBench dataset with different proportions of training samples." ], "reasoning": "To find the method with the highest KTau indicator at 50% training data, we look at the column under 50% and identify the highest value. ReNAS-4 (tensor + L1) has the highest value of 0.8957.", "question": "Which method achieves the highest KTau indicator when using 50% of the NASBench dataset as the training set?", "answer": "So, the answer is {\"answer\": \"ReNAS-4 (tensor + L1)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nReNAS-4 (tensor + L1) achieves the highest KTau indicator when using 90% of the NASBench dataset as the training set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/387.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The Kendall’s Tau (KTau) of Peephole, E2EPP and the proposed algorithms on the NASBench dataset with different proportions of training samples." ], "reasoning": "To verify this statement, we look at the column under 90% and identify the highest value. ReNAS-4 (tensor + L1) has the highest value of 0.8997.", "statement": "ReNAS-4 (tensor + L1) achieves the highest KTau indicator when using 90% of the NASBench dataset as the training set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: The classification accuracy (%) on CIFAR-10 dataset and the ranking (%) among different architectures in NASBench dataset using EA algorithm with the proposed predictor and the peer competitors. Predictors are trained with 0.1% samples randomly selected from NASBench dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/388.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The classification accuracy (%) on CIFAR-10 dataset and the ranking (%) among different architectures in NASBench dataset using EA algorithm with the proposed predictor and the peer competitors. Predictors are trained with 0.1% samples randomly selected from NASBench dataset." ], "reasoning": "The proposed method, specifically Proposed v6, achieves the highest classification accuracy on the CIFAR-10 test set and ranks in the top 0.02% among 423k models in the NASBench dataset.", "description": "The second column represents the classification accuracies of the selected models on CIFAR-10 test set, and the third column represents the true ranking of the selected models among all the 423k different models in NASBench dataset. The proposed method outperforms other competitors, and finds an network architecture with top 0.02% performance among the search space using only 0.1% dataset." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: The classification accuracy (%) on CIFAR-10 dataset and the ranking (%) among different architectures in NASBench dataset using EA algorithm with the proposed predictor and the peer competitors. Predictors are trained with 0.1% samples randomly selected from NASBench dataset.'\nProblem: 'Which method has the highest classification accuracy and what is its ranking percentage?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/388.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The classification accuracy (%) on CIFAR-10 dataset and the ranking (%) among different architectures in NASBench dataset using EA algorithm with the proposed predictor and the peer competitors. Predictors are trained with 0.1% samples randomly selected from NASBench dataset." ], "reasoning": "By examining the table, Proposed v6 has the highest classification accuracy of 93.95 ± 0.11 and a ranking percentage of 0.02%.", "question": "Which method has the highest classification accuracy and what is its ranking percentage?", "answer": "So, the answer is {\"answer\": \"Proposed v6\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nProposed v4 has a higher classification accuracy than E2EPP.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/388.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The classification accuracy (%) on CIFAR-10 dataset and the ranking (%) among different architectures in NASBench dataset using EA algorithm with the proposed predictor and the peer competitors. Predictors are trained with 0.1% samples randomly selected from NASBench dataset." ], "reasoning": "The table shows that Proposed v4 has an accuracy of 93.90 ± 0.21, while E2EPP has an accuracy of 93.47 ± 0.44. Therefore, Proposed v4 has a higher classification accuracy than E2EPP.", "statement": "Proposed v4 has a higher classification accuracy than E2EPP.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: The classification accuracy (%) on CIFAR-100 dataset among different architectures in NASBench using EA algorithm with the proposed predictor and the peer competitors. Predictors are trained with 424 samples randomly selected from NASBench.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/389.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The classification accuracy (%) on CIFAR-100 dataset among different architectures in NASBench using EA algorithm with the proposed predictor and the peer competitors. Predictors are trained with 424 samples randomly selected from NASBench." ], "reasoning": "The proposed method outperforms Peephole and E2EPP in both top-1 and top-5 accuracy on the CIFAR-100 dataset.", "description": "we randomly select 424 architectures and train them on CIFAR-100 dataset from scratch and get the ground-truth labels.. The results in Tab. 4 show the priority of the proposed method." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: The classification accuracy (%) on CIFAR-100 dataset among different architectures in NASBench using EA algorithm with the proposed predictor and the peer competitors. Predictors are trained with 424 samples randomly selected from NASBench.'\nProblem: 'Which method achieved the highest top-1 accuracy on the CIFAR-100 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/389.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The classification accuracy (%) on CIFAR-100 dataset among different architectures in NASBench using EA algorithm with the proposed predictor and the peer competitors. Predictors are trained with 424 samples randomly selected from NASBench." ], "reasoning": "By comparing the top-1 accuracy percentages for Peephole, E2EPP, and the proposed method, the proposed method has the highest top-1 accuracy at 78.56%.", "question": "Which method achieved the highest top-1 accuracy on the CIFAR-100 dataset?", "answer": "So, the answer is {\"answer\": \"Proposed\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Peephole method achieved a top-5 accuracy of 91.97%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/389.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The classification accuracy (%) on CIFAR-100 dataset among different architectures in NASBench using EA algorithm with the proposed predictor and the peer competitors. Predictors are trained with 424 samples randomly selected from NASBench." ], "reasoning": "The table shows that the top-5 accuracy for the Peephole method is indeed 91.97%.", "statement": "The Peephole method achieved a top-5 accuracy of 91.97%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Characteristics of the datasets we consider, together with the base test errors that an SVM achieves on them (with regularization parameters λ selected by validation). The input covariates for Enron and IMDB must be non-negative integers.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/390.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Characteristics of the datasets we consider, together with the base test errors that an SVM achieves on them (with regularization parameters λ selected by validation). The input covariates for Enron and IMDB must be non-negative integers." ], "reasoning": "The MNIST-1-7 dataset is easily linearly separable with a low base error, while the IMDB dataset has high dimensionality and a high base error, making it computationally challenging.", "description": "Summaries of each dataset are given in Table 1, including the number of training points n, the dimension of each point d, and the base accuracy of an SVM trained only on the clean data. Each dataset has its own characteristics and input constraints:. The MNIST-1-7 image dataset (LeCun et al., 1998) requires each input feature to lie within the interval [0, 1] (representing normalized pixels).. It is easily linearly separable: an SVM achieves 0.7% error on the clean data.. The Dogfish image dataset (Koh and Liang, 2017) has no input constraints;. paper we allow to take any value in Rd. Compared to the MNIST-1-7 dataset (where n (cid:29) d), the Dogfish dataset has n ≈ d, allowing attackers to potentially exploit overfitting. The Dogfish dataset also has a low base error of 1.3%.. The Enron spam classification text dataset (Metsis et al., 2006) requires input to be non-negative and integer valued,. As with the Dogfish dataset, the Enron dataset has n ≈ d and a relatively low base error of 3.0%.. The IMDB sentiment classification text dataset (Maas et al., 2011) similarly requires input to be non-negative and integer valued. Compared to the other datasets, the IMDB dataset has significantly larger n and d, presenting computational challenges. It also has n (cid:28) d and is not as linearly separable as the other datasets, with a high base error of 11.9%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Characteristics of the datasets we consider, together with the base test errors that an SVM achieves on them (with regularization parameters λ selected by validation). The input covariates for Enron and IMDB must be non-negative integers.'\nProblem: 'Which dataset has the highest base error and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/390.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Characteristics of the datasets we consider, together with the base test errors that an SVM achieves on them (with regularization parameters λ selected by validation). The input covariates for Enron and IMDB must be non-negative integers." ], "reasoning": "From the table, the IMDB dataset has the highest base error of 11.9%.", "question": "Which dataset has the highest base error?", "answer": "So, the answer is {\"answer\": \"IMDB\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Dogfish dataset has a higher base error than the Enron dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/390.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Characteristics of the datasets we consider, together with the base test errors that an SVM achieves on them (with regularization parameters λ selected by validation). The input covariates for Enron and IMDB must be non-negative integers." ], "reasoning": "The table shows that the Dogfish dataset has a base error of 1.3%, while the Enron dataset has a base error of 2.9%. Therefore, the statement is refuted.", "statement": "The Dogfish dataset has a higher base error than the Enron dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Evaluation on {real, ProGAN, SNGAN, CramerGAN, MMDGAN}. The best performance is highlighted in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/391.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation on {real, ProGAN, SNGAN, CramerGAN, MMDGAN}. The best performance is highlighted in bold." ], "reasoning": "The table demonstrates that a regular CNN classifier can effectively differentiate GAN-generated images from real ones and attribute them to their sources with high accuracy.", "description": "Table 1 shows that we can effectively differentiate GANgenerated images from real ones and attribute generated images to their sources, just using a regular CNN classifier." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Evaluation on {real, ProGAN, SNGAN, CramerGAN, MMDGAN}. The best performance is highlighted in bold.'\nProblem: 'What is the accuracy percentage of the 'Ours' method for the CelebA dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/391.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation on {real, ProGAN, SNGAN, CramerGAN, MMDGAN}. The best performance is highlighted in bold." ], "reasoning": "To find the accuracy percentage of the 'Ours' method for the CelebA dataset, look at the row labeled 'Ours' under the 'Accuracy (%)' column and find the corresponding value under the 'CelebA' column.", "question": "What is the accuracy percentage of the 'Ours' method for the CelebA dataset?", "answer": "So, the answer is {\"answer\": \"99.43\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe PRNU method has a higher accuracy percentage for the CelebA dataset compared to the LSUN dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/391.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation on {real, ProGAN, SNGAN, CramerGAN, MMDGAN}. The best performance is highlighted in bold." ], "reasoning": "To verify this statement, compare the accuracy percentages of the PRNU method for both datasets. The table shows PRNU has 86.61% for CelebA and 67.84% for LSUN, indicating higher accuracy for CelebA.", "statement": "The PRNU method has a higher accuracy percentage for the CelebA dataset compared to the LSUN dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Evaluation on {real, ProGAN_seed_v#i}. The best performance is highlighted in bold. “Our visNet” row indicates our fingerprint visualization network described in Section 3.3 and evaluated in Section 4.5.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/392.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation on {real, ProGAN_seed_v#i}. The best performance is highlighted in bold. “Our visNet” row indicates our fingerprint visualization network described in Section 3.3 and evaluated in Section 4.5." ], "reasoning": "The FD ratio measures in the current table have consistently decreased compared to Table 3, and the attribution performance is shown in the 'Our visNet' row.", "description": "In this setup of {real, ProGAN seed v#i} where i ∈ {1, ..., 10}, we show the performance evaluation in Table 3.. All the FD ratio measures consistently decreased compared to Table 3.. We show the attribution performance in the \"Our visNet\" row in Table 3," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Evaluation on {real, ProGAN_seed_v#i}. The best performance is highlighted in bold. “Our visNet” row indicates our fingerprint visualization network described in Section 3.3 and evaluated in Section 4.5.'\nProblem: 'What is the accuracy percentage of 'Ours' for the LSUN 10.58 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/392.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation on {real, ProGAN_seed_v#i}. The best performance is highlighted in bold. “Our visNet” row indicates our fingerprint visualization network described in Section 3.3 and evaluated in Section 4.5." ], "reasoning": "To find the accuracy percentage of 'Ours' for the LSUN 10.58 dataset, locate the 'Ours' row under the 'Accuracy (%)' section and then find the corresponding value under the LSUN 10.58 column.", "question": "What is the accuracy percentage of 'Ours' for the LSUN 10.58 dataset?", "answer": "So, the answer is {\"answer\": \"97.04\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe FD ratio for 'Our fingerprint' is higher for the CelebA 10.88 dataset compared to the LSUN 10.58 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/392.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation on {real, ProGAN_seed_v#i}. The best performance is highlighted in bold. “Our visNet” row indicates our fingerprint visualization network described in Section 3.3 and evaluated in Section 4.5." ], "reasoning": "To verify this statement, compare the FD ratio values for 'Our fingerprint' under the CelebA 10.88 and LSUN 10.58 columns. The value for CelebA 10.88 is 80.28, and for LSUN 10.58, it is 36.48, indicating that the FD ratio is indeed higher for CelebA 10.88.", "statement": "The FD ratio for 'Our fingerprint' is higher for the CelebA 10.88 dataset compared to the LSUN 10.58 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Parameters settings for the CNN.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/393.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Parameters settings for the CNN." ], "reasoning": "The CNN architecture for the MNIST task includes two convolution layers with 32 and 64 channels respectively, followed by max pooling, a fully connected layer with 512 units, and a softmax output layer.", "description": "The architecture of the CNN used for the MNIST task has two 5 × 5 convolution layers (the first one has 32 channels and the other has 64) and 2 × 2 max pooling layers. Then a fully connected layer with 512 units and ReLu activation is followed. The output layer is a softmax unit. The parameter settings for the CNN are listed in Table II." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Parameters settings for the CNN.'\nProblem: 'What is the shape of the first dense layer in the CNN architecture used for the MNIST task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/393.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Parameters settings for the CNN." ], "reasoning": "The table lists the shapes of each layer in the CNN. The first dense layer, labeled as dense_1, has a shape of 1024×512.", "question": "What is the shape of the first dense layer in the CNN architecture used for the MNIST task?", "answer": "So, the answer is {\"answer\": \"1024×512\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe second convolution layer in the CNN architecture has 64 channels.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/393.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Parameters settings for the CNN." ], "reasoning": "The table shows that the second convolution layer, labeled as conv2d_2, has 64 channels, which matches the statement.", "statement": "The second convolution layer in the CNN architecture has 64 channels.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Parameter settings for the LSTM.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/394.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Parameter settings for the LSTM." ], "reasoning": "The LSTM architecture includes two LSTM layers with specific cell sizes and time steps, followed by a fully connected layer and a softmax output layer.", "description": "The architecture of the LSTM used in this study has two 5 × 5 LSTM layers (the first one with cell size = 20 and time steps = 128 and the other with cell size = 10 and the same time steps), a fully connected layer with 128 units and the ReLu activation, and a softmax output layer. The corresponding parameters of the LSTM is given in Table III." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Parameter settings for the LSTM.'\nProblem: 'What is the shape of the fully connected layer with 128 units?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/394.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Parameter settings for the LSTM." ], "reasoning": "The description mentions a fully connected layer with 128 units and the table lists the shapes of the dense layers. The shape of the dense layer with 128 units is 256.", "question": "What is the shape of the fully connected layer with 128 units?", "answer": { "answer": "256" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LSTM architecture includes a dense layer with a shape of 256×6.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/394.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Parameter settings for the LSTM." ], "reasoning": "The table lists the shapes of the dense layers, and one of them is indeed 256×6.", "statement": "The LSTM architecture includes a dense layer with a shape of 256×6.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Experimental results on freq.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/395.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Experimental results on freq." ], "reasoning": "Lower exchange frequency reduces communication costs but can deteriorate the central model's accuracy.", "description": "a = e/2, K = 20, and m = 2. Two metrics are adopted in this work for measuring the performance of the compared algorithms. One is the best accuracy of the central model within 200 rounds and the other is to the required rounds before the central model's accuracy reaches 95.0%. Note that the same computing rounds means the same communication cost. All experiments are independently run for ten times, and their average (AVG) and standard deviation (STDEV) values are presented in Tables IV, V, and VI. In the tables, the average value is listed before the standard deviation in parenthesis. Based on the results in Tables IV, V, and VI, the following observations can be made.. • Analysis on freq: From the results presented in Table the lower the exchange IV, we can conclude that frequency is, the less communication costs will be required, which is very much expected. However, a too low f req will deteriorate the accuracy of the central model." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Experimental results on freq.'\nProblem: 'What is the accuracy of the central model when the exchange frequency is 5/15?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/395.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Experimental results on freq." ], "reasoning": "From the table, locate the row where freq is 5/15 and find the corresponding accuracy value.", "question": "What is the accuracy of the central model when the exchange frequency is 5/15?", "answer": "So, the answer is {\"answer\": \"97.08%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe central model's accuracy is highest when the exchange frequency is 7/15.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/395.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Experimental results on freq." ], "reasoning": "From the table, compare the accuracy values for all exchange frequencies. The highest accuracy is 97.12% at 7/15.", "statement": "The central model's accuracy is highest when the exchange frequency is 7/15.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: Experimental results on a.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/396.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Experimental results on a." ], "reasoning": "The results in Table V show that using e/2 as a parameter results in better accuracy for the CNN on the 1@MNIST dataset compared to using e.", "description": "a = e/2, K = 20, and m = 2. Two metrics are adopted in this work for measuring the performance of the compared algorithms. One is the best accuracy of the central model within 200 rounds and the other is to the required rounds before the central model's accuracy reaches 95.0%. Note that the same computing rounds means the same communication cost. All experiments are independently run for ten times, and their average (AVG) and standard deviation (STDEV) values are presented in Tables IV, V, and VI. In the tables, the average value is listed before the standard deviation in parenthesis. Based on the results in Tables IV, V, and VI, the following observations can be made.. The results in Table V indicate that e/2 is a better option for the CNN on the 1@MNIST dataset. When a = 1, the algorithm is reduced to AFL, meaning that the parameters uploaded in different rounds will be of equal importance in the aggregation." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: Experimental results on a.'\nProblem: 'What is the accuracy and standard deviation when a = e/2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/396.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Experimental results on a." ], "reasoning": "From the table, when a = e/2, the accuracy is 97.08% with a standard deviation of 0.0037.", "question": "What is the accuracy and standard deviation when a = e/2?", "answer": "So, the answer is {\"answer\": \"97.08% (0.0037)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe accuracy of the central model is higher when a = e compared to a = e/2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/396.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Experimental results on a." ], "reasoning": "The table shows that the accuracy when a = e is 96.92%, which is lower than the accuracy when a = e/2, which is 97.08%.", "statement": "The accuracy of the central model is higher when a = e compared to a = e/2.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VI: Experimental results on the scalability of the algorithm.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/397.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Experimental results on the scalability of the algorithm." ], "reasoning": "A larger number of involved clients leads to higher recognition accuracy, and TWAFL outperforms FedAVG when the active client fraction is 0.1.", "description": "a = e/2, K = 20, and m = 2. Two metrics are adopted in this work for measuring the performance of the compared algorithms. One is the best accuracy of the central model within 200 rounds and the other is to the required rounds before the central model's accuracy reaches 95.0%. Note that the same computing rounds means the same communication cost. All experiments are independently run for ten times, and their average (AVG) and standard deviation (STDEV) values are presented in Tables IV, V, and VI. In the tables, the average value is listed before the standard deviation in parenthesis. Based on the results in Tables IV, V, and VI, the following observations can be made.. separately assessed. Based on the results presented in Table VI, the following three conclusions can be made. First, a larger number of involved clients (a larger m) leads to a higher recognition accuracy. Second, TWAFL outperforms FedAVG when the active client fraction is. = 0.1, as indicated in the results in rows one and two in the table. Third, FedAVG is slightly better when the total number of clients is smaller and. is higher, as shown by the results listed in the last row of the table. This implies that the advantage of the proposed algorithm over the traditional federated learning will become more apparent as the number of clients increases. This is encouraging since in most realworld problems, the number of clients is typically very large." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VI: Experimental results on the scalability of the algorithm.'\nProblem: 'What is the accuracy of TWAFL when K=20 and m=2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/397.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Experimental results on the scalability of the algorithm." ], "reasoning": "From the table, locate the row where K=20 and m=2, and find the accuracy value under the TWAFL column.", "question": "What is the accuracy of TWAFL when K=20 and m=2?", "answer": "So, the answer is {\"answer\": \"97.16%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFedAVG has a higher accuracy than TWAFL when K=10 and m=1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/397.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Experimental results on the scalability of the algorithm." ], "reasoning": "From the table, locate the row where K=10 and m=1, and compare the accuracy values of FedAVG and TWAFL.", "statement": "FedAVG has a higher accuracy than TWAFL when K=10 and m=1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VII: Experiments on Performance .'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/398.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: Experiments on Performance ." ], "reasoning": "TEFL achieves the best performance on most tasks in terms of the total number of rounds and the best accuracy, while TWAFL performs slightly better than TEFL on MNIST in terms of the total communication cost.", "description": "Finally, the comparative results of the four algorithms on ten test cases generated from MNIST and HAR tasks are given in Table VII. The listed metrics include the number of rounds needed, the classification accuracy (listed in parenthesis), and total communication cost (C. Cost for short). From these results, the following observations can be made: • Both TWAFL and TEFL outperform FedAVG on most cases in terms of the total number of rounds, the best accuracy, and the total communication cost. • TEFL achieves the best performance on most tasks in terms of the total number of rounds and the best accuracy. The temporally weighted aggregation strategy accelerates the convergence of the learning and improved the learning performance. • TWAFL performs slightly better than TEFL on MNIST in terms of the total communication cost, while TEFL works better than TWAFL on the HAR datasets. The asynchronous model update strategy significantly contributes to reducing the communication cost per round. • AFL performs the worst among the four compared algorithms. When comparing the performance of the AFL only adopting the asynchronous strategy and the TWAFL using both of them, the asynchronous one always needs the help of the other strategy." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VII: Experiments on Performance .'\nProblem: 'Which algorithm achieves the best accuracy on the 2@MNIST task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/398.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: Experiments on Performance ." ], "reasoning": "From the table, TEFL achieves the best accuracy on the 2@MNIST task with 98.5%.", "question": "Which algorithm achieves the best accuracy on the 2@MNIST task?", "answer": "So, the answer is {\"answer\": \"TEFL\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTWAFL has the lowest communication cost on the 4@HAR task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/398.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: Experiments on Performance ." ], "reasoning": "From the table, TWAFL has a communication cost of 1 on the 4@HAR task, which is the lowest compared to other algorithms.", "statement": "TWAFL has the lowest communication cost on the 4@HAR task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: Classification accuracy (%) of our network w.r.t. different perturbation attacks before or after immunization on LSUN bedroom {real, ProGAN_seed_v#i}. The best performance is highlighted in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/399.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Classification accuracy (%) of our network w.r.t. different perturbation attacks before or after immunization on LSUN bedroom {real, ProGAN_seed_v#i}. The best performance is highlighted in bold." ], "reasoning": "The pre-trained classifier performances are shown in the 'Akt' columns, and the finetuned classifier performances are shown in the 'Dfs' columns for both Table 7 and Table 8.", "description": "Given perturbed images and the setup of {real, ProGAN seed v#i}, we show the pre-trained classifier performances in the \"Akt\" columns in Table 7 and Table 8.. Given perturbed images and the setup of {real, ProGAN seed v#i}, we show the finetuned classifier performance in the \"Dfs\" columns in Table 7 and Table 8." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 8: Classification accuracy (%) of our network w.r.t. different perturbation attacks before or after immunization on LSUN bedroom {real, ProGAN_seed_v#i}. The best performance is highlighted in bold.'\nProblem: 'Which method has the highest performance in the LSUN Blur category for the finetuned classifier?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/399.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Classification accuracy (%) of our network w.r.t. different perturbation attacks before or after immunization on LSUN bedroom {real, ProGAN_seed_v#i}. The best performance is highlighted in bold." ], "reasoning": "To find the highest performance in the LSUN Blur category for the finetuned classifier, look at the 'Dfs' column under 'LSUN Blur'. The highest value is 96.68, which corresponds to the 'Ours' method.", "question": "Which method has the highest performance in the LSUN Blur category for the finetuned classifier?", "answer": "So, the answer is {\"answer\": \"Ours\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe PRNU method has a higher performance than the Ours method in the LSUN Noise category for the pre-trained classifier.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/399.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Classification accuracy (%) of our network w.r.t. different perturbation attacks before or after immunization on LSUN bedroom {real, ProGAN_seed_v#i}. The best performance is highlighted in bold." ], "reasoning": "To verify this statement, compare the values in the 'Akt' column under 'LSUN Noise' for both methods. PRNU has a value of 39.59, while Ours has a value of 11.80. Since 39.59 is greater than 11.80, the statement is correct.", "statement": "The PRNU method has a higher performance than the Ours method in the LSUN Noise category for the pre-trained classifier.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: PSNR results of blind denoisers on BSD68 and Set12 datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/400.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: PSNR results of blind denoisers on BSD68 and Set12 datasets." ], "reasoning": "Our network training approach shows similar results to DnCNN-N2N and outperforms BM3D and DnCNN-SURE on the BSD68 test set by about 0.5dB and 0.15dB respectively.", "description": "The performance of our approach along with the state-of- the-art methods was tabulated in Table 1. Our network training approach demonstrates almost identical quantitative results with Noise2Noise trained DnCNN (DnCNN-N2N) in both test sets.. Moreover, quantitative analysis on BSD68 test set reveals that our eSURE is consistently better than conventional BM3D for about 0.5dB and outperforms DnCNN-SURE for about 0.15 dB in lower and higher noise cases. The performance gap between the proposed method, BM3D and DnCNN-SURE are still similar for Set12 test set. In addition, we can observe that minimizing MC-SURE with twice more dataset (DnCNN-SURE*) provides a little improvement, but not enough to reach DnCNN-eSURE and DnCNN-N2N." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: PSNR results of blind denoisers on BSD68 and Set12 datasets.'\nProblem: 'Which method shows the highest performance on the BSD-68 test set for σ=25?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/400.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: PSNR results of blind denoisers on BSD68 and Set12 datasets." ], "reasoning": "To find the highest performance on the BSD-68 test set for σ=25, compare the values in the row corresponding to σ=25. The highest value is 30.31, which corresponds to DnCNN-eSURE.", "question": "Which method shows the highest performance on the BSD-68 test set for σ=25?", "answer": "So, the answer is {\"answer\": \"DnCNN-eSURE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDnCNN-SURE* outperforms DnCNN-N2N on the BSD-68 test set for σ=50.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/400.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: PSNR results of blind denoisers on BSD68 and Set12 datasets." ], "reasoning": "To verify this statement, compare the values for DnCNN-SURE* and DnCNN-N2N on the BSD-68 test set for σ=50. DnCNN-SURE* has a value of 26.13, while DnCNN-N2N has a value of 27.07. Therefore, DnCNN-SURE* does not outperform DnCNN-N2N.", "statement": "DnCNN-SURE* outperforms DnCNN-N2N on the BSD-68 test set for σ=50.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results of denoising methods on BSD68 and Set 12 datasets (Performance in dB).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/401.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of denoising methods on BSD68 and Set 12 datasets (Performance in dB)." ], "reasoning": "At low ground-truth noise (σgt = 1), DnCNN-N2N and DnCNN-eSURE yield the best PSNR results, but as noise correlation increases (σgt = 5, 10), DnCNN-eSURE consistently produces the best results.", "description": "We simulated the case with imperfect ground truth images (or slightly noisy ground truth), by adding synthetic Gaussian noise with σgt to the noiseless clean oracle images. Consequently, noisy training images were generated by adding i.i.d. Gaussian noise on the top of the imperfect ground truth dataset.. Table 2 shows the performance of denoising methods trained for a fixed noise given a noisy groundtruth images with σgt = {1, 5, 10, 20}. The higher the σgt is, the more correlated noise is in a training set. We notice that at low level of ground-truth noise (σgt = 1), both Noise2Noise (DnCNN-N2N) and eSURE (DnCNN-eSURE) yield the best PSNR results and even comparable to the DnCNNMSE. However, as noise correlation gets severe (σgt = 5, 10), Noise2Noise fails to achieve high performance that is consistent with our theoretical derivation. In contrast, the proposed DnCNN. eSURE produces the best quantitative results in a stable manner. Although, DnCNN-SURE is not susceptible to noise correlation, it still yielded worse performance than DnCNN-eSURE." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results of denoising methods on BSD68 and Set 12 datasets (Performance in dB).'\nProblem: 'Which denoising method produces the best PSNR results for BSD-68 with σgt = 10?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/401.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of denoising methods on BSD68 and Set 12 datasets (Performance in dB)." ], "reasoning": "For BSD-68 with σgt = 10, we compare the PSNR results of all methods. DnCNN-eSURE has the highest PSNR values across all noise levels (25, 50, 68).", "question": "Which denoising method produces the best PSNR results for BSD-68 with σgt = 10?", "answer": "So, the answer is {\"answer\": \"DnCNN-eSURE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDnCNN-N2N produces the best PSNR results for BSD-68 with σgt = 1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/401.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of denoising methods on BSD68 and Set 12 datasets (Performance in dB)." ], "reasoning": "For BSD-68 with σgt = 1, DnCNN-N2N has the highest PSNR value (29.23) compared to other methods.", "statement": "DnCNN-N2N produces the best PSNR results for BSD-68 with σgt = 1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results of denoising methods on RGB color BSD68 dataset(Performance in dB).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/402.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of denoising methods on RGB color BSD68 dataset(Performance in dB)." ], "reasoning": "The table shows the performance degradation of DnCNN-N2N as the noise in the ground truth image increases.", "description": "The experimental results for RGB color image denoising case are tabulated in Table 3. In this case, we observe the same performance degradation pattern of DnCNN-N2N as the noise in ground truth image increases (more results in the supplementary material)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Results of denoising methods on RGB color BSD68 dataset(Performance in dB).'\nProblem: 'What is the performance of CDnCNN-N2N for RGB BSD-68 with a noise level of 50 and ground truth noise of 20?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/402.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of denoising methods on RGB color BSD68 dataset(Performance in dB)." ], "reasoning": "To find the performance of CDnCNN-N2N for RGB BSD-68 with a noise level of 50 and ground truth noise of 20, locate the intersection of the CDnCNN-N2N row and the RGB BSD-68 50 column under the 20 ground truth noise.", "question": "What is the performance of CDnCNN-N2N for RGB BSD-68 with a noise level of 50 and ground truth noise of 20?", "answer": "So, the answer is {\"answer\": \"25.62\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCDnCNN-eSURE performs better than CDnCNN-N2N for RGB BSD-68 with a noise level of 25 and ground truth noise of 10.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/402.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of denoising methods on RGB color BSD68 dataset(Performance in dB)." ], "reasoning": "To verify this statement, compare the values of CDnCNN-eSURE and CDnCNN-N2N for RGB BSD-68 with a noise level of 25 and ground truth noise of 10. CDnCNN-eSURE has a value of 27.94, while CDnCNN-N2N has a value of 27.87.", "statement": "CDnCNN-eSURE performs better than CDnCNN-N2N for RGB BSD-68 with a noise level of 25 and ground truth noise of 10.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: RMSE values for all models on the ML-100k and ML-1M datasets using the new training and test sets, averaged over 5 different runs, along with standard deviations. GCMC (original) results are the same as those reported in previous work [1]. The † symbol denotes experiments using disjoint edge sets in every M(i); ‡ denotes the representation where every step contains all edges from previous steps.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/403.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: RMSE values for all models on the ML-100k and ML-1M datasets using the new training and test sets, averaged over 5 different runs, along with standard deviations. GCMC (original) results are the same as those reported in previous work [1]. The † symbol denotes experiments using disjoint edge sets in every M(i); ‡ denotes the representation where every step contains all edges from previous steps." ], "reasoning": "Recurrent models perform better than the baseline in one representation, with the disjoint edge representation performing worse than its alternative.", "description": "At first glance, the most striking result is the difference in performance GCMC displays when the training and test sets were changed to the new ones. As the original training and test sets are closer to a uniform sample taken from the whole dataset, their distributions are similar. Once we view this from a temporal perspective, that is, unseen data is comprised of future ratings, it is evident that such a change in the distribution severely affects GCMC. This highlights the importance of taking into account the evolution of user-item relationships happening over time.. With this in mind, we see that the recurrent models attain better results than the baseline only in one of the representations. While both LSTM and GRU variants displayed very similar performance, the disjoint edge representation performs worse than its alternative.. As the recurrent models have the exact same configuration in both scenarios, the representation is clearly the main factor influencing the performance of the model. When using all edges from previous steps at every step, the performance gap is evident." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: RMSE values for all models on the ML-100k and ML-1M datasets using the new training and test sets, averaged over 5 different runs, along with standard deviations. GCMC (original) results are the same as those reported in previous work [1]. The † symbol denotes experiments using disjoint edge sets in every M(i); ‡ denotes the representation where every step contains all edges from previous steps.'\nProblem: 'Which model and representation combination shows the best performance on the ML-100k dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/403.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: RMSE values for all models on the ML-100k and ML-1M datasets using the new training and test sets, averaged over 5 different runs, along with standard deviations. GCMC (original) results are the same as those reported in previous work [1]. The † symbol denotes experiments using disjoint edge sets in every M(i); ‡ denotes the representation where every step contains all edges from previous steps." ], "reasoning": "By comparing the performance values in the ML-100k column, the GCMC-GRU‡ model shows the lowest value of 1.0630±0.0141, indicating the best performance.", "question": "Which model and representation combination shows the best performance on the ML-100k dataset?", "answer": "So, the answer is {\"answer\": \"GCMC-GRU‡\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe GCMC-GRU‡ model performs better than the GCMC-LSTM‡ model on the ML-1M dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/403.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: RMSE values for all models on the ML-100k and ML-1M datasets using the new training and test sets, averaged over 5 different runs, along with standard deviations. GCMC (original) results are the same as those reported in previous work [1]. The † symbol denotes experiments using disjoint edge sets in every M(i); ‡ denotes the representation where every step contains all edges from previous steps." ], "reasoning": "By comparing the performance values in the ML-1M column, the GCMC-GRU‡ model has a value of 1.0407±0.0336, which is slightly worse than the GCMC-LSTM‡ model's value of 1.0394±0.0230.", "statement": "The GCMC-GRU‡ model performs better than the GCMC-LSTM‡ model on the ML-1M dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Classification accuracy (%) of our network w.r.t. different perturbation attacks before or after immunization on CelebA {real, ProGAN_seed_v#i}. The best performance is highlighted in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/404.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Classification accuracy (%) of our network w.r.t. different perturbation attacks before or after immunization on CelebA {real, ProGAN_seed_v#i}. The best performance is highlighted in bold." ], "reasoning": "The pre-trained classifier performances are shown in the 'Akt' columns, and the finetuned classifier performances are shown in the 'Dfs' columns for both Table 7 and Table 8.", "description": "Given perturbed images and the setup of {real, ProGAN seed v#i}, we show the pre-trained classifier performances in the \"Akt\" columns in Table 7 and Table 8.. Given perturbed images and the setup of {real, ProGAN seed v#i}, we show the finetuned classifier performance in the \"Dfs\" columns in Table 7 and Table 8." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Classification accuracy (%) of our network w.r.t. different perturbation attacks before or after immunization on CelebA {real, ProGAN_seed_v#i}. The best performance is highlighted in bold.'\nProblem: 'What is the highest performance of the finetuned classifier for CelebA Relighting?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/404.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Classification accuracy (%) of our network w.r.t. different perturbation attacks before or after immunization on CelebA {real, ProGAN_seed_v#i}. The best performance is highlighted in bold." ], "reasoning": "To find the highest performance of the finetuned classifier for CelebA Relighting, look at the 'Dfs' columns under CelebA Relighting. The highest value is 98.66.", "question": "What is the highest performance of the finetuned classifier for CelebA Relighting?", "answer": { "answer": "98.66" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe pre-trained classifier performance for CelebA Noise is higher than for CelebA Compression.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/404.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Classification accuracy (%) of our network w.r.t. different perturbation attacks before or after immunization on CelebA {real, ProGAN_seed_v#i}. The best performance is highlighted in bold." ], "reasoning": "To verify this statement, compare the 'Akt' values for CelebA Noise and CelebA Compression. The 'Akt' value for CelebA Noise is 57.88, which is higher than the 'Akt' value for CelebA Compression, which is 26.15.", "statement": "The pre-trained classifier performance for CelebA Noise is higher than for CelebA Compression.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: ECE and UCE test set results in % (M=15 bins). 0 % means perfect calibration. In TS calibration with MC dropout the same value of T was used to report both ECE and UCE.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/405.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: ECE and UCE test set results in % (M=15 bins). 0 % means perfect calibration. In TS calibration with MC dropout the same value of T was used to report both ECE and UCE." ], "reasoning": "The table shows that temperature scaling (TS) calibration significantly reduces the Expected Calibration Error (ECE) and Uncertainty Calibration Error (UCE) for MC dropout, especially for larger networks.", "description": "Tab. 1 reports test set results for different networks [17, 18] and data sets used to evaluate the performance of temperature scaling for dropout variational inference. The proposed UCE metric is used to quantify calibration of uncertainty.. Uncalibrated ECE shows, that MC dropout already reduces miscalibration of model likelihood by up to 6.6 percentage points. With TS calibration, MC dropout reduces ECE by 45–66 % and UCE drops drastically (especially for larger networks)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: ECE and UCE test set results in % (M=15 bins). 0 % means perfect calibration. In TS calibration with MC dropout the same value of T was used to report both ECE and UCE.'\nProblem: 'What is the ECE for the ResNet-101 model on the CIFAR-100 dataset after TS calibration with MC dropout?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/405.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: ECE and UCE test set results in % (M=15 bins). 0 % means perfect calibration. In TS calibration with MC dropout the same value of T was used to report both ECE and UCE." ], "reasoning": "To find the ECE for the ResNet-101 model on the CIFAR-100 dataset after TS calibration with MC dropout, locate the row for CIFAR-100 and ResNet-101, then find the ECE value under the 'TS Calibrated MC Dropout' column.", "question": "What is the ECE for the ResNet-101 model on the CIFAR-100 dataset after TS calibration with MC dropout?", "answer": "So, the answer is {\"answer\": \"1.92\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe UCE for the DenseNet-169 model on the CIFAR-100 dataset is higher after TS calibration with MC dropout compared to uncalibrated MC dropout.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/405.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: ECE and UCE test set results in % (M=15 bins). 0 % means perfect calibration. In TS calibration with MC dropout the same value of T was used to report both ECE and UCE." ], "reasoning": "To verify this statement, compare the UCE values for DenseNet-169 on CIFAR-100 under 'Uncalibrated MC Dropout' and 'TS Calibrated MC Dropout'. The UCE for uncalibrated MC dropout is 29.62, and for TS calibrated MC dropout, it is 2.69.", "statement": "The UCE for the DenseNet-169 model on the CIFAR-100 dataset is higher after TS calibration with MC dropout compared to uncalibrated MC dropout.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Mean value and standard deviation for the critical blocks of first and second attempts; p-value indicating the significance of the difference between the first and second attempts'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/406.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Mean value and standard deviation for the critical blocks of first and second attempts; p-value indicating the significance of the difference between the first and second attempts" ], "reasoning": "The mean IAT score significantly decreased from the first to the second attempt, indicating a strong statistical difference.", "description": "Table III shows the mean and standard deviation of the response times, error rates, and IAT scores for both the first and second IAT attempts. The last row shows the p-value of a two-tailed dependent t-test for paired samples. These values indicate a strong statistical difference between the first and second attempts.. the average IAT score was 0.39. The mean score decreased to 0.01" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Mean value and standard deviation for the critical blocks of first and second attempts; p-value indicating the significance of the difference between the first and second attempts'\nProblem: 'What is the p-value for the change in error rate between the first and second IAT attempts?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/406.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Mean value and standard deviation for the critical blocks of first and second attempts; p-value indicating the significance of the difference between the first and second attempts" ], "reasoning": "The table shows the p-value for the error rate in the last row under the 'Error Rate' column, which is 0.0006.", "question": "What is the p-value for the change in error rate between the first and second IAT attempts?", "answer": "So, the answer is {\"answer\": \"0.0006\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe response time increased from the first to the second IAT attempt.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/406.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Mean value and standard deviation for the critical blocks of first and second attempts; p-value indicating the significance of the difference between the first and second attempts" ], "reasoning": "The table shows that the mean response time increased from 0.802 in the first attempt to 0.844 in the second attempt.", "statement": "The response time increased from the first to the second IAT attempt.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Results of various machine learning methods. Pruning the data entailed of removing deception attempts that resulted in a <1 SD score change.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/407.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Results of various machine learning methods. Pruning the data entailed of removing deception attempts that resulted in a <1 SD score change." ], "reasoning": "Multinomial logistic regression performed best on unpruned data with an F1 score of 0.75, while multilayer perceptron performed best on pruned data with an F1 score of 0.81.", "description": "For each subset, we used 7 distinct machine learning algorithms provided by Weka (using the default parameters): naive Bayes, support vector machines, multinomial logistic regression, multilayer perceptron, simple logistic regression, propositional rule learner (JRip), and random forest.. Table IV shows the weighted F1 scores across each subset and with each ML method.. On the unpruned data, multinomial logistic performed the best achieving an F1 score of 0.75 followed closely by naive bayes, SVM, and multilayer perceptron. The multilayer perceptron performed the best on the pruned data achieving an F1 score of 0.81. All models, with the exception of multinomial logistic, performed better on the pruned data. Overall, the more complex models (i.e., SVM and multilayer perceptron) performed the best across the pruned and unpruned data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Results of various machine learning methods. Pruning the data entailed of removing deception attempts that resulted in a <1 SD score change.'\nProblem: 'Which machine learning method had the highest F1 score on the pruned data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/407.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Results of various machine learning methods. Pruning the data entailed of removing deception attempts that resulted in a <1 SD score change." ], "reasoning": "By examining the table, we see that the multilayer perceptron had the highest F1 score of 0.812 on the pruned data.", "question": "Which machine learning method had the highest F1 score on the pruned data?", "answer": "So, the answer is {\"answer\": \"Multilayer Perceptron\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe SVM method performed better on the pruned data than on the unpruned data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/407.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Results of various machine learning methods. Pruning the data entailed of removing deception attempts that resulted in a <1 SD score change." ], "reasoning": "The table shows that the F1 score for SVM on the pruned data is 0.779, which is higher than the F1 score of 0.728 on the unpruned data.", "statement": "The SVM method performed better on the pruned data than on the unpruned data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: F1 scores for TensorFlow MLP and ratio-based method from [1]'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/408.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: F1 scores for TensorFlow MLP and ratio-based method from [1]" ], "reasoning": "The TensorFlow MLP model outperformed the ratio-based model in both unpruned and pruned data, achieving F1 scores of 0.78 and 0.81 respectively, compared to the ratio-based model's scores of 0.64 and 0.58.", "description": "Table V shows our top performing model (the TensorFlow MLP) compared against the ratio-based model both on the unpruned and pruned data.. The ratio-based model showed accuracies of 70% or lower as opposed to 80%" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: F1 scores for TensorFlow MLP and ratio-based method from [1]'\nProblem: 'What is the difference in F1 scores between the TensorFlow MLP and the ratio-based model on pruned data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/408.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: F1 scores for TensorFlow MLP and ratio-based method from [1]" ], "reasoning": "To find the difference in F1 scores between the TensorFlow MLP and the ratio-based model on pruned data, subtract the F1 score of the ratio-based model from the F1 score of the TensorFlow MLP model: 0.81 - 0.58.", "question": "What is the difference in F1 scores between the TensorFlow MLP and the ratio-based model on pruned data?", "answer": "So, the answer is {\"answer\": \"0.23\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe TensorFlow MLP model achieved an F1 score of 0.81 on pruned data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/408.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: F1 scores for TensorFlow MLP and ratio-based method from [1]" ], "reasoning": "The table shows that the TensorFlow MLP model achieved an F1 score of 0.81 on pruned data, which matches the statement.", "statement": "The TensorFlow MLP model achieved an F1 score of 0.81 on pruned data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Summary of the change in classification accuracy (in percent) compared to the clean/original graph. Single edge perturbation under RBA setting. Lower is better.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/409.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Summary of the change in classification accuracy (in percent) compared to the clean/original graph. Single edge perturbation under RBA setting. Lower is better." ], "reasoning": "GF-Attack outperforms other attackers on all datasets and models, demonstrating its generalization ability on Graph Convolutional Networks and sampling-based models.", "description": "Table 1 summaries the attack results of different attackers on Graph Convolutional Networks. Our GF-Attack attacker outperforms other attackers on all datasets and all models. Moreover, GFAttack performs quite well on 2 layers GCN with nonlinear activation. This implies the generalization ability of our attacker on Graph Convolutional Networks.. Table 1 also summaries the attack results of different attackers on sampling-based graph embedding models. As expected, our attacker achieves the best performance nearly on all target. models. It validates the effectiveness of our method on attacking sampling-based models.. Another interesting observation is that the attack performance on LINE is much better than that on DeepWalk. This result may due to the deterministic structure of LINE, while the random sampling procedure in DeepWalk may help raise the resistance to adversarial attack. Moreover, GF-Attack on all graph filters successfully drop the classification accuracy on both Graph Convolutional Networks and sampling-based models, which again indicates the transferability of our general model in practice." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Summary of the change in classification accuracy (in percent) compared to the clean/original graph. Single edge perturbation under RBA setting. Lower is better.'\nProblem: 'Which attacker achieved the best performance on the Citeseer LINE model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/409.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Summary of the change in classification accuracy (in percent) compared to the clean/original graph. Single edge perturbation under RBA setting. Lower is better." ], "reasoning": "The table shows the attack results for different attackers on various models. By comparing the values for the Citeseer LINE model, GF-Attack has the highest negative value (-22.11), indicating the best performance.", "question": "Which attacker achieved the best performance on the Citeseer LINE model?", "answer": { "answer": "GF-Attack" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGF-Attack achieved the best performance on the Pubmed DeepWalk model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/409.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Summary of the change in classification accuracy (in percent) compared to the clean/original graph. Single edge perturbation under RBA setting. Lower is better." ], "reasoning": "The table shows the attack results for different attackers on various models. For the Pubmed DeepWalk model, GF-Attack has the highest negative value (-7.43), indicating the best performance.", "statement": "GF-Attack achieved the best performance on the Pubmed DeepWalk model.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: NMT hyperparameters. ‘decoder’ refers to function implemented in Nematus (for BPE-to-BPE) and dl4mt-c2c (for *-to-char).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/410.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: NMT hyperparameters. ‘decoder’ refers to function implemented in Nematus (for BPE-to-BPE) and dl4mt-c2c (for *-to-char)." ], "reasoning": "The BPE-to-BPE system was trained for one week using a joint BPE vocabulary of size 89,500, while the BPE-to-char and char-to-char systems were trained for two weeks on a single Titan X GPU.", "description": "We report hyperparameters in Table 2. They correspond to those used by Lee et al. (2016) for BPE-to-char and char-tochar; for BPE-to-BPE, we also adopt some hyperparameters from Sennrich et al. (2016b), most importantly, we extract a joint BPE vocabulary of size 89 500 from the parallel corpus. We trained the BPE-to-BPE system for one week, following Sennrich et al. (2016a), and the *-to-char systems for two weeks, following Lee et al. (2016), on a single Titan X GPU." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: NMT hyperparameters. ‘decoder’ refers to function implemented in Nematus (for BPE-to-BPE) and dl4mt-c2c (for *-to-char).'\nProblem: 'What is the encoder size used for the BPE-to-BPE system and how long was it trained?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/410.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: NMT hyperparameters. ‘decoder’ refers to function implemented in Nematus (for BPE-to-BPE) and dl4mt-c2c (for *-to-char)." ], "reasoning": "The encoder size for the BPE-to-BPE system is 1024, and it was trained for approximately one week.", "question": "What is the encoder size used for the BPE-to-BPE system?", "answer": "So, the answer is {\"answer\": \"1024\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe char-to-char system uses a beam size of 12.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/410.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: NMT hyperparameters. ‘decoder’ refers to function implemented in Nematus (for BPE-to-BPE) and dl4mt-c2c (for *-to-char)." ], "reasoning": "According to the table, the char-to-char system uses a beam size of 20, not 12.", "statement": "The char-to-char system uses a beam size of 12.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Case-sensitive Bleu scores (EN-DE) on WMT newstest. We report scores with detokenized NIST Bleu (mteval-v13a.pl), and in brackets, tokenized Bleu with multi-bleu.perl.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/411.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Case-sensitive Bleu scores (EN-DE) on WMT newstest. We report scores with detokenized NIST Bleu (mteval-v13a.pl), and in brackets, tokenized Bleu with multi-bleu.perl." ], "reasoning": "The BLEU scores for the systems trained are comparable to previous works, and the performance of the three systems is relatively close.", "description": "Firstly, we report case-sensitive BLEU scores for all systems we trained for comparison to previous work.10 Results are shown in Table 3. The results confirm that our systems are comparable to previously reported results (Sennrich et al., 2016a; Chung et al., 2016), and that performance of the three systems is relatively close in terms of BLEU." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Case-sensitive Bleu scores (EN-DE) on WMT newstest. We report scores with detokenized NIST Bleu (mteval-v13a.pl), and in brackets, tokenized Bleu with multi-bleu.perl.'\nProblem: 'Which system had the highest BLEU score in 2016?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/411.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Case-sensitive Bleu scores (EN-DE) on WMT newstest. We report scores with detokenized NIST Bleu (mteval-v13a.pl), and in brackets, tokenized Bleu with multi-bleu.perl." ], "reasoning": "To find the system with the highest BLEU score in 2016, compare the BLEU scores of all systems listed under the 2016 column.", "question": "Which system had the highest BLEU score in 2016?", "answer": "So, the answer is {\"answer\": \"char-to-char\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BPE-to-BPE system had the highest BLEU score in 2015.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/411.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Case-sensitive Bleu scores (EN-DE) on WMT newstest. We report scores with detokenized NIST Bleu (mteval-v13a.pl), and in brackets, tokenized Bleu with multi-bleu.perl." ], "reasoning": "To verify this statement, compare the BLEU scores of all systems listed under the 2015 column. The BPE-to-BPE system has a BLEU score of 23.2, which is not the highest.", "statement": "The BPE-to-BPE system had the highest BLEU score in 2015.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Accuracy (in percent) of models on different categories of contrastive errors. Best single model result in bold (multiple bold results indicate that difference to best system is not statistically significant).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/412.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy (in percent) of models on different categories of contrastive errors. Best single model result in bold (multiple bold results indicate that difference to best system is not statistically significant)." ], "reasoning": "Despite similar BLEU scores, models with character decoders make fewer transliteration errors than the BPE-to-BPE model.", "description": "Our main result is the assessment via contrastive translation pairs, shown in Table 4. We find that despite obtaining similar BLEU scores, the models have learned different structures to a different degree. The models with character decoder make fewer transliteration errors than the BPE-to-BPE model. However," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Accuracy (in percent) of models on different categories of contrastive errors. Best single model result in bold (multiple bold results indicate that difference to best system is not statistically significant).'\nProblem: 'Which model has the highest score for polarity (negation) insertion?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/412.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy (in percent) of models on different categories of contrastive errors. Best single model result in bold (multiple bold results indicate that difference to best system is not statistically significant)." ], "reasoning": "By comparing the scores for polarity (negation) insertion across all models, the human model has the highest score of 99.9.", "question": "Which model has the highest score for polarity (negation) insertion?", "answer": "So, the answer is {\"answer\": \"human\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BPE-to-char model has a higher score for verb particle than the char-to-char model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/412.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy (in percent) of models on different categories of contrastive errors. Best single model result in bold (multiple bold results indicate that difference to best system is not statistically significant)." ], "reasoning": "The table shows that the BPE-to-char model has a score of 88.0 for verb particle, while the char-to-char model has a score of 86.7.", "statement": "The BPE-to-char model has a higher score for verb particle than the char-to-char model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Accuracy (in percent) of models on different categories of contrastive errors related to polarity. Best single model result in bold.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/413.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy (in percent) of models on different categories of contrastive errors related to polarity. Best single model result in bold." ], "reasoning": "Polarity errors, particularly the deletion of negation markers, are a significant issue in machine translation, as shown in Table 5.", "description": "Polarity shifts between the source and target text are a well-known translation problem, and our analysis shows that the main type of error is the deletion of negation markers, in line with with findings of previous studies (Fancellu and Webber, 2015). We consider the relatively high number of errors related to polarity an important problem in machine translation, and hope that future work will try to improve upon our results, shown in more detail in Table 5." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Accuracy (in percent) of models on different categories of contrastive errors related to polarity. Best single model result in bold.'\nProblem: 'Which system has the highest accuracy for negation deletion nicht?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/413.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy (in percent) of models on different categories of contrastive errors related to polarity. Best single model result in bold." ], "reasoning": "To find the system with the highest accuracy for negation deletion nicht, compare the values in the negation deletion nicht column. The highest value is 93.6, which corresponds to the char-to-char system.", "question": "Which system has the highest accuracy for negation deletion nicht?", "answer": "So, the answer is {\"answer\": \"char-to-char\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BPE-to-BPE system has the highest accuracy for negation insertion nicht.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/413.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy (in percent) of models on different categories of contrastive errors related to polarity. Best single model result in bold." ], "reasoning": "To verify this statement, compare the values in the negation insertion nicht column. The highest value is 97.1, which corresponds to the BPE-to-BPE system.", "statement": "The BPE-to-BPE system has the highest accuracy for negation insertion nicht.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Examples where char-to-char model prefers contrastive translation (subject-verb agreement errors). 1-best translation can make error of same type (example 1), different type (translation of taught is missing in example 2), or no error (example 3).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/414.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Examples where char-to-char model prefers contrastive translation (subject-verb agreement errors). 1-best translation can make error of same type (example 1), different type (translation of taught is missing in example 2), or no error (example 3)." ], "reasoning": "The table shows examples where the contrastive translation scores higher than the reference translation, highlighting errors in the 1-best translation and missing verbs.", "description": "Table 6 shows different examples of the where the contrastive translation is scored higher than the reference by the char-to-char model, and the corresponding 1-best translation. In the first one, our method automatically recognizes an error that also appears in the 1-best translation. In the second example, the 1-best translation is missing the verb." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Examples where char-to-char model prefers contrastive translation (subject-verb agreement errors). 1-best translation can make error of same type (example 1), different type (translation of taught is missing in example 2), or no error (example 3).'\nProblem: 'What is the cost difference between the reference and contrastive translations for the sentence about FriendsFest?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/414.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Examples where char-to-char model prefers contrastive translation (subject-verb agreement errors). 1-best translation can make error of same type (example 1), different type (translation of taught is missing in example 2), or no error (example 3)." ], "reasoning": "The cost for the reference translation of the FriendsFest sentence is 0.276, while the cost for the contrastive translation is 0.262. The difference is 0.276 - 0.262.", "question": "What is the cost difference between the reference and contrastive translations for the sentence about FriendsFest?", "answer": "So, the answer is {\"answer\": \"0.014\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe contrastive translation for the sentence about Robert Lewandowski had a lower cost than the reference translation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/414.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Examples where char-to-char model prefers contrastive translation (subject-verb agreement errors). 1-best translation can make error of same type (example 1), different type (translation of taught is missing in example 2), or no error (example 3)." ], "reasoning": "The cost for the contrastive translation of the Robert Lewandowski sentence is 0.507, which is lower than the reference translation cost of 0.551.", "statement": "The contrastive translation for the sentence about Robert Lewandowski had a lower cost than the reference translation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Phrase localization accuracy on Flickr30k Entities dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/415.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Phrase localization accuracy on Flickr30k Entities dataset." ], "reasoning": "The Query-Adaptive R-CNN method significantly outperformed both non-scalable and scalable methods, suggesting that predicting the classifier is more effective than learning a common subspace for open-vocabulary detection.", "description": "non-scalable or joint localization methods. Moreover, it significantly outperformed the scalable methods, which suggests the approach of predicting the classifier is better than learning a common subspace for the open-vocabulary detection problem." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Phrase localization accuracy on Flickr30k Entities dataset.'\nProblem: 'Which method achieved the highest overall score, and what does this suggest about the approach?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/415.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Phrase localization accuracy on Flickr30k Entities dataset." ], "reasoning": "By examining the 'All' column, the Query-Adaptive R-CNN method has the highest score of 65.21. This suggests that the approach of predicting the classifier is better than learning a common subspace for the open-vocabulary detection problem.", "question": "Which method achieved the highest overall score, and what does this suggest about the approach?", "answer": "So, the answer is {\"answer\": \"Query-Adaptive R-CNN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe QRC net method outperformed all other methods in the 'Animals' category.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/415.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Phrase localization accuracy on Flickr30k Entities dataset." ], "reasoning": "By examining the 'Animals' column, the QRC net method has the highest score of 80.50, which is higher than all other methods listed.", "statement": "The QRC net method outperformed all other methods in the 'Animals' category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparison of various bounding box regressors on Flickr30k Entities for different IoU thresholds. The number of parameters in Gr is also shown.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/416.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of various bounding box regressors on Flickr30k Entities for different IoU thresholds. The number of parameters in Gr is also shown." ], "reasoning": "The regressor significantly improved localization accuracy at high IoU thresholds without decreasing accuracy when sharing or reducing hidden layer units.", "description": "Table 2 shows the results with and without regressor. The regressor significantly improved the accuracy with high IoU thresholds, which demonstrates that the regressor improved the localization accuracy. In addition, the accuracy did not decrease as a result of sharing the hidden layer or reducing the number of units in the hidden layer." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparison of various bounding box regressors on Flickr30k Entities for different IoU thresholds. The number of parameters in Gr is also shown.'\nProblem: 'Which architecture with regression achieved the highest accuracy at IoU 0.9?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/416.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of various bounding box regressors on Flickr30k Entities for different IoU thresholds. The number of parameters in Gr is also shown." ], "reasoning": "By examining the IoU 0.9 column, the highest accuracy with regression is 10.55, achieved by the 300-64(4096) architecture.", "question": "Which architecture with regression achieved the highest accuracy at IoU 0.9?", "answer": "So, the answer is {\"answer\": \"300-64(4096)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe architecture 300-1024(4096) achieved the highest accuracy at IoU 0.8.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/416.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of various bounding box regressors on Flickr30k Entities for different IoU thresholds. The number of parameters in Gr is also shown." ], "reasoning": "By examining the IoU 0.8 column, the highest accuracy is 33.94, achieved by the 300-1024(4096) architecture.", "statement": "The architecture 300-1024(4096) achieved the highest accuracy at IoU 0.8.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Open-vocabulary object detection performance on Visual Genome and PASCAL VOC 2007 datasets. WN and VG are the strategies to remove mutually non-exclusive phrases.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/417.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Open-vocabulary object detection performance on Visual Genome and PASCAL VOC 2007 datasets. WN and VG are the strategies to remove mutually non-exclusive phrases." ], "reasoning": "NPA significantly improved performance across all metrics, and combining WN and VG further enhanced performance, showing their complementary nature.", "description": "Table 3 compares different training strategies. NPA significantly improved the performance: more than 25% relative improvement for all metrics. Removing mutually non-exclusive words also contributed the performance: WN and VG both improved performance (5.8% and 6.9% relative AP gain, respectively). Performance improved even further by combining them (11.8% relative AP gain), which shows they are complementary. AP was much improved by NPA for the PASCAL dataset as well (47% relative gain)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Open-vocabulary object detection performance on Visual Genome and PASCAL VOC 2007 datasets. WN and VG are the strategies to remove mutually non-exclusive phrases.'\nProblem: 'What is the relative AP gain when combining WN and VG compared to using only NPA?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/417.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Open-vocabulary object detection performance on Visual Genome and PASCAL VOC 2007 datasets. WN and VG are the strategies to remove mutually non-exclusive phrases." ], "reasoning": "The table shows that using NPA alone results in a VOC mAP of 36.74, while combining WN and VG with NPA results in a VOC mAP of 42.81. The relative AP gain is calculated as (42.81 - 36.74) / 36.74 * 100%.", "question": "What is the relative AP gain when combining WN and VG compared to using only NPA?", "answer": "So, the answer is {\"answer\": \"16.5%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCombining WN and VG with NPA results in a VOC mAP of 42.81.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/417.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Open-vocabulary object detection performance on Visual Genome and PASCAL VOC 2007 datasets. WN and VG are the strategies to remove mutually non-exclusive phrases." ], "reasoning": "The table shows that the combination of WN and VG with NPA results in a VOC mAP of 42.81, which supports the statement.", "statement": "Combining WN and VG with NPA results in a VOC mAP of 42.81.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Number of false alarms in top 100 results for five queries (w/o NPA → w/ NPA). The top 2 confusing categories are shown for each query.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/418.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Number of false alarms in top 100 results for five queries (w/o NPA → w/ NPA). The top 2 confusing categories are shown for each query." ], "reasoning": "The table shows the most and second most confusing categories for each query and how confusion is reduced by NPA.", "description": "Table 4 shows the most confused category and its total count in the top 100 search results for each query, which shows what concept is confusing for each query and how much the confusion is reduced by NPA.9 This shows that visually similar categories resulted in false positive without NPA, while their number was suppressed by training with NPA." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Number of false alarms in top 100 results for five queries (w/o NPA → w/ NPA). The top 2 confusing categories are shown for each query.'\nProblem: 'Which query had the highest count for the most confusing class before NPA?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/418.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Number of false alarms in top 100 results for five queries (w/o NPA → w/ NPA). The top 2 confusing categories are shown for each query." ], "reasoning": "By examining the 'Most confusing class' columns, the query 'train' had the highest count of 17 for the most confusing class 'bus' before NPA.", "question": "Which query had the highest count for the most confusing class before NPA?", "answer": "So, the answer is {\"answer\": \"train\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe query 'helmet' had 'cap' as the second most confusing class with a count of 6.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/418.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Number of false alarms in top 100 results for five queries (w/o NPA → w/ NPA). The top 2 confusing categories are shown for each query." ], "reasoning": "The table shows that for the query 'helmet', the second most confusing class was 'cap' with a count of 6.", "statement": "The query 'helmet' had 'cap' as the second most confusing class with a count of 6.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Speed/memory in large-scale experiments.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/419.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Speed/memory in large-scale experiments." ], "reasoning": "The system can retrieve objects from one million images in approximately 0.5 seconds, with a standard deviation of 165 ms.", "description": "and standard deviation of speed are computed over 20 queries in PASCAL VOC dataset. Our system could retrieve objects from one million images in around 0.5 seconds." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Speed/memory in large-scale experiments.'\nProblem: 'What is the memory usage when retrieving objects from a database of 500K images?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/419.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Speed/memory in large-scale experiments." ], "reasoning": "The table shows that the memory usage for a database size of 500K images is 9.87 GB.", "question": "What is the memory usage (GB) when retrieving objects from a database of 500K images?", "answer": "So, the answer is {\"answer\": \"9.87\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe system takes around 200 ms to retrieve objects from a database of 50K images.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/419.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Speed/memory in large-scale experiments." ], "reasoning": "The table indicates that the time to retrieve objects from a database of 50K images is 196 ms with a standard deviation of 21 ms, which supports the statement.", "statement": "The system takes around 200 ms to retrieve objects from a database of 50K images.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table II: Summary of episodes of the conducted experiments'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/420.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table II: Summary of episodes of the conducted experiments" ], "reasoning": "The table shows performance measures for shape, color, and class-label across four episodes, highlighting an increase in metrics for shape and color, and a slight decrease in accuracy and precision for classification but with a 10% increase in coverage.", "description": "Table III reports the performance measures of one-shot perception for each three symbols (shape, color, class-label) separately.. The object class results represent an interesting scenario. Even though the average accuracy and precision of the classification is high, only 82.2 % of hypotheses generated in the four episodes are annotated.. Table III presents the performance measures on the four episodes using these parameters.. Overall, in the case of shape and color we can notice a general increase in all metrics, while in the case of classification there is a slight decrease in accuracy and precision but with a 10 % increase in coverage." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table II: Summary of episodes of the conducted experiments'\nProblem: 'What is the total number of hypotheses generated across all four episodes?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/420.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table II: Summary of episodes of the conducted experiments" ], "reasoning": "To find the total number of hypotheses generated, sum the values in the '# of Hyp.' row for all episodes: 130 + 130 + 353 + 353 + 694 + 694 + 759 + 759.", "question": "What is the total number of hypotheses generated across all four episodes?", "answer": "So, the answer is {\"answer\": \"3872\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe number of objects in the batch size for Episode 3 is greater than 50.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/420.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table II: Summary of episodes of the conducted experiments" ], "reasoning": "The table shows that the number of objects in the batch size for Episode 3 is 49, which is less than 50.", "statement": "The number of objects in the batch size for Episode 3 is greater than 50.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison of DPSA and the benchmarking algorithms on difference configuration of random DCOPs.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/421.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of DPSA and the benchmarking algorithms on difference configuration of random DCOPs." ], "reasoning": "DPSA outperforms other algorithms in both sparse and dense settings, with performance differences ranging from 1.8% to 61%.", "description": "From the results in Table 1, it can be observed that DPSA produces solutions that are 21% 6.7% better than DSASDP depending on the number of agents. However, when the density is high (p = 0.6), GDBA is the closest competitor to DPSA. In dense settings, DPSA outperforms GDBA by 1.8% 1.9%. Other competing algorithms perform equal or worse than GDBA and DSA-SDP and produce even bigger performance difference with DPSA (up to 15% - 61% in sparse settings and 9.6% - 3.2% in dense settings). Also note that the optimal cost for ( = 25, p = 0.1), which we | generate using the well-known DPOP (Petcu and Faltings 2005) algorithm, is 253, while DPSA produces 268 in the same setting. − − A |" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison of DPSA and the benchmarking algorithms on difference configuration of random DCOPs.'\nProblem: 'What is the optimal cost produced by DPSA when |A| = 25 and p = 0.1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/421.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of DPSA and the benchmarking algorithms on difference configuration of random DCOPs." ], "reasoning": "From the table, locate the row for DPSA and the column where |A| = 25 and p = 0.1. The value is 268.", "question": "What is the optimal cost produced by DPSA when |A| = 25 and p = 0.1?", "answer": "So, the answer is {\"answer\": \"268\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDPSA produces the lowest cost when |A| = 75 and p = 0.6.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/421.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of DPSA and the benchmarking algorithms on difference configuration of random DCOPs." ], "reasoning": "From the table, compare the costs for all algorithms when |A| = 75 and p = 0.6. DPSA has the lowest cost of 63998.", "statement": "DPSA produces the lowest cost when |A| = 75 and p = 0.6.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: MPJE (Mean Per Joint Error, mm) metric on Human3.6m dataset under defined protocol i.e. no rigid alignment of predicted 3d pose with ground truth 3d pose. GT denotes training on ground-truth 2d pose labels. Except Martinez et al. all other state-of-the-art methods use images for training instead of 2d pose labels. Our model achieves least MPJE for majority of the actions.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/422.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: MPJE (Mean Per Joint Error, mm) metric on Human3.6m dataset under defined protocol i.e. no rigid alignment of predicted 3d pose with ground truth 3d pose. GT denotes training on ground-truth 2d pose labels. Except Martinez et al. all other state-of-the-art methods use images for training instead of 2d pose labels. Our model achieves least MPJE for majority of the actions." ], "reasoning": "Our model, trained on Human3.6m dataset under full supervision, achieves greater accuracy than state-of-the-art methods on most actions, including difficult ones like Sitting and Greeting, with an overall improvement of 6% in MPJE.", "description": "Evaluation on test datasets is done using standard 3d pose estimation metrics, MPJE (Mean Per Joint Error in mm) for Human3.6m dataset, along with PCK (Percent of Correct Keypoints) and AUC (Area Under the Curve) , , , , for MPI-INF-3DHP, which are more robust and stronger metrics in identifying the incorrect joint predictions.. projection loss). Our model captures all t Quantitative results are given in Table I.. Human3.6m: Table. shows results on Human3.6m under defined protocol in using Model I, which is trained on Human3.6m dataset under full supervision. As shown in the table, we achieve greater accuracy than the state-of-the-art methods on most of the actions including difficult actions such as Sitting, Greeting, etc in terms of MPJE (Mean Per. Joint Error, mm). On average we have an overall improvement of 6% over our baseline method , which is also trained on 2d pose ground-truth. This improvement in accuracy can be attributed to the 3d-to-2d re-projection loss minimization and geometric constraints. We also outperform state-of-theart method which was trained on the input images from both Human3.6m and MPII, using our Model. trained on Human3.6m alone." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: MPJE (Mean Per Joint Error, mm) metric on Human3.6m dataset under defined protocol i.e. no rigid alignment of predicted 3d pose with ground truth 3d pose. GT denotes training on ground-truth 2d pose labels. Except Martinez et al. all other state-of-the-art methods use images for training instead of 2d pose labels. Our model achieves least MPJE for majority of the actions.'\nProblem: 'What is the average MPJE score for the 'Ours (GT)' model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/422.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: MPJE (Mean Per Joint Error, mm) metric on Human3.6m dataset under defined protocol i.e. no rigid alignment of predicted 3d pose with ground truth 3d pose. GT denotes training on ground-truth 2d pose labels. Except Martinez et al. all other state-of-the-art methods use images for training instead of 2d pose labels. Our model achieves least MPJE for majority of the actions." ], "reasoning": "To find the average MPJE score for the 'Ours (GT)' model, look at the 'Avg.' column in the table for the 'Ours (GT)' row. The value listed is 42.84.", "question": "What is the average MPJE score for the 'Ours (GT)' model?", "answer": "So, the answer is {\"answer\": \"42.84\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'Ours (GT)' model has the lowest MPJE score for the 'Photo' action.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/422.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: MPJE (Mean Per Joint Error, mm) metric on Human3.6m dataset under defined protocol i.e. no rigid alignment of predicted 3d pose with ground truth 3d pose. GT denotes training on ground-truth 2d pose labels. Except Martinez et al. all other state-of-the-art methods use images for training instead of 2d pose labels. Our model achieves least MPJE for majority of the actions." ], "reasoning": "To verify this statement, compare the MPJE scores for the 'Photo' action across all models. The 'Ours (GT)' model has a score of 44.37, which is not the lowest. The lowest score for 'Photo' is 43.2 by Sun et al.", "statement": "The 'Ours (GT)' model has the lowest MPJE score for the 'Photo' action.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Results on MPI-INF-3DHP test-set by scene. Higher PCK(%) and AUC indicates better performance. − means values are not given in original paper. ∗ denotes re-targeting of predicted 3d pose using ground truth limb length. Our model shows best performance among state-of-the-art methods while fine tuned on MPII dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/423.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Results on MPI-INF-3DHP test-set by scene. Higher PCK(%) and AUC indicates better performance. − means values are not given in original paper. ∗ denotes re-targeting of predicted 3d pose using ground truth limb length. Our model shows best performance among state-of-the-art methods while fine tuned on MPII dataset." ], "reasoning": "The evaluation on test datasets uses MPJE for Human3.6m and PCK, AUC for MPI-INF-3DHP. The model trained on Human3.6M and MPII shows an improvement in PCK and AUC over state-of-the-art methods.", "description": "Evaluation on test datasets is done using standard 3d pose estimation metrics, MPJE (Mean Per Joint Error in mm) for Human3.6m dataset, along with PCK (Percent of Correct Keypoints) and AUC (Area Under the Curve) , , , , for MPI-INF-3DHP, which are more robust and stronger metrics in identifying the incorrect joint predictions.. MPI-INF-3DHP: For MPI-INF-3DHP dataset, quantitative aluation has been done using standard metrics PCK, AUC d MPJE as used in state-of-the-art methods , , , 0], .. (a) Cross Dataset Evaluation: Table II shows evaluation sults on MPI-INF-3DHP with our Model. (trained on uman 3.6M) and Model II (trained on Human 3.6M + PII) in terms of PCK and AUC for all three different ttings (GS, NoGS and Outdoor) for the 2935 testing images. n an average we see an improvement of 2.3% on PCK ith a threshold of 150 mm) and 6.2% on AUC over the st performing state-of-the-art method. This establishes the. Quantitative results are given in Table II and Table III." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Results on MPI-INF-3DHP test-set by scene. Higher PCK(%) and AUC indicates better performance. − means values are not given in original paper. ∗ denotes re-targeting of predicted 3d pose using ground truth limb length. Our model shows best performance among state-of-the-art methods while fine tuned on MPII dataset.'\nProblem: 'What is the PCK Outdoor score for the model trained on Human3.6M and MPII?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/423.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Results on MPI-INF-3DHP test-set by scene. Higher PCK(%) and AUC indicates better performance. − means values are not given in original paper. ∗ denotes re-targeting of predicted 3d pose using ground truth limb length. Our model shows best performance among state-of-the-art methods while fine tuned on MPII dataset." ], "reasoning": "The table shows that the PCK Outdoor score for the model trained on Human3.6M and MPII is 71.4.", "question": "What is the PCK Outdoor score for the model trained on Human3.6M and MPII?", "answer": "So, the answer is {\"answer\": \"71.4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model trained on Human3.6M and MPII has the highest PCK GS score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/423.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Results on MPI-INF-3DHP test-set by scene. Higher PCK(%) and AUC indicates better performance. − means values are not given in original paper. ∗ denotes re-targeting of predicted 3d pose using ground truth limb length. Our model shows best performance among state-of-the-art methods while fine tuned on MPII dataset." ], "reasoning": "The table shows that the model trained on Human3.6M and MPII has a PCK GS score of 74.2, which is the highest among all listed methods.", "statement": "The model trained on Human3.6M and MPII has the highest PCK GS score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Activity-wise performance on MPI-INF-3DHP test-set using standard metrics PCK (%), AUC and MPJE (mm). (MPII) means pretrained on MPII dataset. a denotes background augmentation in training data. − means values are not given in original paper. ∗ denotes the re-targeting of predicted 3d pose using ground truth limb length. Higher PCK, AUC and lower MPJE indicates better performance. We have achieved significantly better performance than the state-of-the-art methods on all the actions in terms of all the metrics.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/424.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Activity-wise performance on MPI-INF-3DHP test-set using standard metrics PCK (%), AUC and MPJE (mm). (MPII) means pretrained on MPII dataset. a denotes background augmentation in training data. − means values are not given in original paper. ∗ denotes the re-targeting of predicted 3d pose using ground truth limb length. Higher PCK, AUC and lower MPJE indicates better performance. We have achieved significantly better performance than the state-of-the-art methods on all the actions in terms of all the metrics." ], "reasoning": "The evaluation on test datasets uses MPJE for Human3.6m and PCK, AUC, and MPJE for MPI-INF-3DHP. The fine-tuned Model III shows significant improvement over state-of-the-art methods in all metrics.", "description": "Evaluation on test datasets is done using standard 3d pose estimation metrics, MPJE (Mean Per Joint Error in mm) for Human3.6m dataset, along with PCK (Percent of Correct Keypoints) and AUC (Area Under the Curve) , , , , for MPI-INF-3DHP, which are more robust and stronger metrics in identifying the incorrect joint predictions.. MPI-INF-3DHP: For MPI-INF-3DHP dataset, quantitative aluation has been done using standard metrics PCK, AUC d MPJE as used in state-of-the-art methods , , , 0], .. (b) Results after Fine-tuning: We also present a performance analysis of our Model III (Model. fine-tuned on MPI-INF-3DHP dataset) in Table III. It shows a comparative analysis of the activity-wise performance of Model III with all recent state-of-the-art methods. We have achieved significant improvement over the state-of-the-art on all the actions in terms of all the metrics. On an average we exceed the best accuracy achieved by methods fully supervised on MPI-INF3DHP by 2% on PCK, 17% on AUC and 16% on MPJE.. Quantitative results are given in Table II and Table III." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Activity-wise performance on MPI-INF-3DHP test-set using standard metrics PCK (%), AUC and MPJE (mm). (MPII) means pretrained on MPII dataset. a denotes background augmentation in training data. − means values are not given in original paper. ∗ denotes the re-targeting of predicted 3d pose using ground truth limb length. Higher PCK, AUC and lower MPJE indicates better performance. We have achieved significantly better performance than the state-of-the-art methods on all the actions in terms of all the metrics.'\nProblem: 'What is the Total MPJE achieved by the method that used H3.6M+3DHP as training data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/424.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Activity-wise performance on MPI-INF-3DHP test-set using standard metrics PCK (%), AUC and MPJE (mm). (MPII) means pretrained on MPII dataset. a denotes background augmentation in training data. − means values are not given in original paper. ∗ denotes the re-targeting of predicted 3d pose using ground truth limb length. Higher PCK, AUC and lower MPJE indicates better performance. We have achieved significantly better performance than the state-of-the-art methods on all the actions in terms of all the metrics." ], "reasoning": "From the table, the method that used H3.6M+3DHP as training data is 'Ours'. The Total MPJE for 'Ours' is 71.40.", "question": "What is the Total MPJE achieved by the method that used H3.6M+3DHP as training data?", "answer": "So, the answer is {\"answer\": \"71.40\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe method by Luo et al. achieved the highest Total PCK.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/424.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Activity-wise performance on MPI-INF-3DHP test-set using standard metrics PCK (%), AUC and MPJE (mm). (MPII) means pretrained on MPII dataset. a denotes background augmentation in training data. − means values are not given in original paper. ∗ denotes the re-targeting of predicted 3d pose using ground truth limb length. Higher PCK, AUC and lower MPJE indicates better performance. We have achieved significantly better performance than the state-of-the-art methods on all the actions in terms of all the metrics." ], "reasoning": "From the table, the method 'Ours' achieved the highest Total PCK of 85.4, which is higher than Luo et al.'s Total PCK of 83.8.", "statement": "The method by Luo et al. achieved the highest Total PCK.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Ablation Study for different losses on MPI-INF-3DHP dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/425.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Ablation Study for different losses on MPI-INF-3DHP dataset." ], "reasoning": "Adding 2d re-projection loss to the supervised 3d loss improves PCK by 3.2% and AUC by 7.2%, and further adding bone length symmetry loss results in a total improvement of 6% in PCK and 13% in AUC.", "description": "Table IV and Table V shows ablative analysis of different network design parameters and losses used during training.. Table IV shows, the addition of 2d re-projection loss with the supervised 3d loss in baseline network, increases PCK by 3.2% and AUC by 7.2% on MPI-INF-3DHP dataset, during crossdataset validation. Using bone length symmetry loss with reprojection and supervised loss advances network performance further with 6% and 13% of improvement in PCK and AUC respectively for similar test-setup." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Ablation Study for different losses on MPI-INF-3DHP dataset.'\nProblem: 'What is the improvement in AUC when bone length symmetry loss is added to the network with 2d re-projection and supervised 3d loss?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/425.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Ablation Study for different losses on MPI-INF-3DHP dataset." ], "reasoning": "The baseline network with supervised 3d loss has an AUC of 27.7. Adding 2d re-projection loss increases the AUC to 29.7, and further adding bone length symmetry loss increases the AUC to 31.2. The total improvement in AUC is 31.2 - 27.7.", "question": "What is the improvement in AUC when bone length symmetry loss is added to the network with 2d re-projection and supervised 3d loss?", "answer": "So, the answer is {\"answer\": \"3.5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAdding 2d re-projection loss to the supervised 3d loss improves PCK by 3.2%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/425.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Ablation Study for different losses on MPI-INF-3DHP dataset." ], "reasoning": "The table shows that the PCK for the baseline network with supervised 3d loss is 62.2. Adding 2d re-projection loss increases the PCK to 64.2, which is an improvement of 3.2%.", "statement": "Adding 2d re-projection loss to the supervised 3d loss improves PCK by 3.2%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: Ablation study on different network parameters for our (3d-to-2d re-projection module) in terms of re-projection error on Human3.6m. Δ defines re-projection error differences between current training setup (as mentioned in section IV-D) and the above setups.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/426.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Ablation study on different network parameters for our (3d-to-2d re-projection module) in terms of re-projection error on Human3.6m. Δ defines re-projection error differences between current training setup (as mentioned in section IV-D) and the above setups." ], "reasoning": "The re-projection error is significantly higher when the network is trained without dropout or batch normalization, indicating the importance of these parameters in reducing error.", "description": "Table IV and Table V shows ablative analysis of different network design parameters and losses used during training.. architecture as the baseline network. To understand the optimality of performance of our 3d-to-2d module we have performed an ablation study on different choice of design parameters. Table V, represents error between input ground truth 2d and re-projected 2d from the 3d-to-2d module for various design choices of the network. This error is measured in terms of Euclidean distance between joints in 2d co-ordinate space. The re-projection error is quite high when the network is trained without dropout or batch normalization between. module. ∆ defines the re-projection error differences between current training setup and different choices of training setups as mentioned in Table V." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: Ablation study on different network parameters for our (3d-to-2d re-projection module) in terms of re-projection error on Human3.6m. Δ defines re-projection error differences between current training setup (as mentioned in section IV-D) and the above setups.'\nProblem: 'What is the re-projection error when the network is trained without dropout?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/426.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Ablation study on different network parameters for our (3d-to-2d re-projection module) in terms of re-projection error on Human3.6m. Δ defines re-projection error differences between current training setup (as mentioned in section IV-D) and the above setups." ], "reasoning": "The table shows the re-projection error for different training setups. The re-projection error when the network is trained without dropout is listed as 6.49.", "question": "What is the re-projection error when the network is trained without dropout?", "answer": "So, the answer is {\"answer\": \"6.49\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe re-projection error is highest when the network is trained without dropout.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/426.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Ablation study on different network parameters for our (3d-to-2d re-projection module) in terms of re-projection error on Human3.6m. Δ defines re-projection error differences between current training setup (as mentioned in section IV-D) and the above setups." ], "reasoning": "The table shows that the re-projection error is highest when the network is trained without batch normalization (36.2), not when trained without dropout (6.49).", "statement": "The re-projection error is highest when the network is trained without dropout.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Location estimation results'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/427.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Location estimation results" ], "reasoning": "Localization accuracy was tested on 6 datasets recorded over about a mile of stop-and-go traffic, with accuracy ranging from 73% to 95% and missing observations ranging from 6% to 30%.", "description": "Localization accuracy was tested on 6 hand-annotated datasets gathered by an AV on public, multi-lane roads near Nissan Research Center in Silicon Valley. Each dataset was recorded over about a mile of stop-and-go traffic and ranged in time from 2 to 6 minutes. All road segments had between 3 and 6 lanes, corresponding to between 5 and 11 states. In these experiments no metric location information, such as GPS, was used, and topological ground truth was provided. Because of the intermittent nature of the lane line and vehicle detections, not all timesteps possess enough observations to disambiguate lane-states. Thus, in the results presented in Table. we do not consider instances in which either no observations were recorded or the observations voted for at least half of all states, such as seeing only a single lane line immediately to the left of the vehicle. These instances are labeled \"Missing Observations\". Given sequences of timesteps with little or no observations, it is possible to have multiple states tie for the same belief. Localization is considered correct if the true state is among those with maximum belief, and incorrect otherwise. Length and observation quality are shown so as to give an idea about the difficulty of the dataset. Predictions are made at 100Hz." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Location estimation results'\nProblem: 'Which dataset had the highest localization accuracy and what was the percentage of missing observations for that dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/427.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Location estimation results" ], "reasoning": "To find the dataset with the highest localization accuracy, look at the 'Accuracy' column and identify the highest value, which is 95%. Then, find the corresponding 'Missing Obs.' value for that dataset, which is 9%.", "question": "What was the percentage of missing observations for dataset 4?", "answer": "So, the answer is {\"answer\": \"9\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDataset 3 had the lowest percentage of missing observations.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/427.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Location estimation results" ], "reasoning": "To verify this statement, look at the 'Missing Obs.' column and compare the values. Dataset 3 has 6% missing observations, which is the lowest among all datasets.", "statement": "Dataset 3 had the lowest percentage of missing observations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Local topological structure estimation accuracy. Results are reported as the percent of timesteps during which the correct topological structure was estimated with highest probability (lowest entropy). PM is probability of sampling from the correct topology. PE is the probability of emitting observations. Kσ is the amount by which the variance is scaled. Each entry in the table was computed from performance over 1000 timesteps.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/428.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Local topological structure estimation accuracy. Results are reported as the percent of timesteps during which the correct topological structure was estimated with highest probability (lowest entropy). PM is probability of sampling from the correct topology. PE is the probability of emitting observations. Kσ is the amount by which the variance is scaled. Each entry in the table was computed from performance over 1000 timesteps." ], "reasoning": "Simulated data was used to test topological structure estimation due to insufficient real-world cases, with varying probabilities and noise levels to mimic real-world conditions.", "description": "Testing topological structure estimation was done using simulated data, since there were too few cases in the real world data to draw concrete conclusions. To simulate false positive data, lane line and vehicle detections are generated according to the topology given by the map with probability PM , and according to some other, randomly selected topology with probability (1 − PM ). Further, to simulate the intermittent nature of real-world data, with probability (1 − PE) no observations are emitted. To see how our approach handles increased sensor noise, we tested different levels of variance. Given a variance σ based on real-world data, simulated observations are generated with variance Kσσ, where Kσ is an experimental parameter. Locations of lane lines and vehicle detections are sampled from multivariate normal distributions with means as a function of the given topology, and variances Kσσ. The observation generation process runs independently for each lane line and vehicle detection. Table II displays the results of local topological structure estimation." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Local topological structure estimation accuracy. Results are reported as the percent of timesteps during which the correct topological structure was estimated with highest probability (lowest entropy). PM is probability of sampling from the correct topology. PE is the probability of emitting observations. Kσ is the amount by which the variance is scaled. Each entry in the table was computed from performance over 1000 timesteps.'\nProblem: 'What is the highest score achieved when PM is 0.9 and PE is 0.9?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/428.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Local topological structure estimation accuracy. Results are reported as the percent of timesteps during which the correct topological structure was estimated with highest probability (lowest entropy). PM is probability of sampling from the correct topology. PE is the probability of emitting observations. Kσ is the amount by which the variance is scaled. Each entry in the table was computed from performance over 1000 timesteps." ], "reasoning": "To find the highest score when PM is 0.9 and PE is 0.9, look at the row corresponding to PM 0.9 and the columns under PE 0.9. The highest score in these columns is 96.", "question": "What is the highest score achieved when PM is 0.9 and PE is 0.9?", "answer": "So, the answer is {\"answer\": \"96\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe lowest score achieved when PM is 0.6 and PE is 0.5 is 49.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/428.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Local topological structure estimation accuracy. Results are reported as the percent of timesteps during which the correct topological structure was estimated with highest probability (lowest entropy). PM is probability of sampling from the correct topology. PE is the probability of emitting observations. Kσ is the amount by which the variance is scaled. Each entry in the table was computed from performance over 1000 timesteps." ], "reasoning": "To verify this statement, look at the row corresponding to PM 0.6 and the columns under PE 0.5. The lowest score in these columns is indeed 49.", "statement": "The lowest score achieved when PM is 0.6 and PE is 0.5 is 49.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Results of Packet-based Anomaly Detection'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/429.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Results of Packet-based Anomaly Detection" ], "reasoning": "SVM and Random Forest achieve near perfect scores, while k-nearest Neighbour performs well in certain areas and k Means Clustering has high false positives.", "description": "The results are shown in Table III.. It can be seen that SVM and Random Forest perform very well with near perfect scores, while k-nearest Neighbour performs well in certain areas and k Means Clustering does not perform satisfactorily at all, allowing too many false positives." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Results of Packet-based Anomaly Detection'\nProblem: 'Which algorithm has the lowest performance on DS2 in terms of accuracy?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/429.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Results of Packet-based Anomaly Detection" ], "reasoning": "By comparing the accuracy (Acc.) scores for DS2, k Means Clustering has the lowest performance with a score of 0.55624.", "question": "Which algorithm has the lowest performance on DS2 in terms of accuracy?", "answer": "So, the answer is {\"answer\": \"k Means Clustering\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nk-nearest Neighbour has the highest F1 score on DS1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/429.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Results of Packet-based Anomaly Detection" ], "reasoning": "The table shows that the F1 score for k-nearest Neighbour on DS1 is 0.99853, which is lower than the F1 scores of 1.0 for both Random Forest and SVM on DS1.", "statement": "k-nearest Neighbour has the highest F1 score on DS1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Data augmentation results on ADE20k dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/430.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Data augmentation results on ADE20k dataset" ], "reasoning": "Random sampling strategy fails in ADE20k dataset due to large object categories, but size-based and hard-negative sampling improve performance, with the best mIoU achieved by the size-based sampling model (0.379). Combining negative likelihood loss with hard negative sampling further improves performance to 0.385 mIoU.", "description": "We can see that random sampling strategy, which worked well in image classification, fails here leading to drop in performance. This is because, many object categories in ADE20k dataset are large and difficult to remove like bed, sofa and mountain and random strategy suffers by picking these. Instead when we switch to size-based and hard-negative based sampling, we see that the performance improves and the the size-based sampling model achieves the best mIoU of the three models (0.379). Applying negative likelihood loss on the removed object class gets further improvement when combined with hard negative sampling. This model also improves upon the Upernet base- line (achieving 0.385 IoU vs 0.377 by Upernet)," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Data augmentation results on ADE20k dataset'\nProblem: 'Which model achieved the highest ADE20k mIoU and what was the value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/430.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Data augmentation results on ADE20k dataset" ], "reasoning": "By examining the table, the model 'DA (hard negative)' with 'Negative' removed pixels achieved the highest ADE20k mIoU with a value of 0.385.", "question": "Which model achieved the highest ADE20k mIoU?", "answer": "So, the answer is {\"answer\": \"DA (hard negative)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Upernet model achieved the highest ADE20k mIoU.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/430.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Data augmentation results on ADE20k dataset" ], "reasoning": "The table shows that the Upernet model achieved an ADE20k mIoU of 0.377, which is not the highest. The highest mIoU of 0.385 was achieved by the 'DA (hard negative)' model with 'Negative' removed pixels.", "statement": "The Upernet model achieved the highest ADE20k mIoU.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparing the performance of road and sidewalk segmentation on natural images with and without cars.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/431.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparing the performance of road and sidewalk segmentation on natural images with and without cars." ], "reasoning": "The Upernet and DataAug models perform similarly on the full set and images with cars, but DataAug outperforms Upernet on images without cars, especially in road and sidewalk segmentation.", "description": "On the full set and on the split with cars, we see that the performance of the baseline Upernet and our augmented model (DA hard negative with negative loss) is equivalent. However, when we look at only images without car, the Upernet model performs significantly worse in both road (0.68 vs 0.72 for ours) and sidewalk (0.40 vs 0.46 for ours) segmentation. This quantitatively shows that the baseline model struggles to distinguish between road and sidewalk without car in the image, whereas our data augmentation is more robust and performs well even without context (car)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparing the performance of road and sidewalk segmentation on natural images with and without cars.'\nProblem: 'How does the performance of the Upernet model compare to the DataAug model on images without cars for road segmentation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/431.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparing the performance of road and sidewalk segmentation on natural images with and without cars." ], "reasoning": "The table shows that for images without cars, the Upernet model has a road segmentation score of 0.68, while the DataAug model has a score of 0.72, indicating that DataAug performs better.", "question": "Which Upernet model has a higher performance without cars for road segmentation?", "answer": "So, the answer is {\"answer\": \"DataAug\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Upernet model performs better than the DataAug model on images without cars for sidewalk segmentation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/431.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparing the performance of road and sidewalk segmentation on natural images with and without cars." ], "reasoning": "The table shows that for images without cars, the Upernet model has a sidewalk segmentation score of 0.40, while the DataAug model has a score of 0.46, indicating that DataAug performs better.", "statement": "The Upernet model performs better than the DataAug model on images without cars for sidewalk segmentation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Experiments in three class setting on ADE20k'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/432.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Experiments in three class setting on ADE20k" ], "reasoning": "The Upernet model's performance drops significantly on the Single test split when trained on Co-occur data, but data augmentation helps recover most of this loss and even outperforms the baseline on the Co-occur split.", "description": "First we can see that when we switch from training on Full training data to Co-occur split (containing only images with atleast two objects), the performance of the Upernet greatly drops on the Single test split (from 0.67 to 0.52). This is indicates that the model overfits to the context it sees, and is not able to segment objects when it seeing them out of context. However, with data-augmentation we generate images of objects without context, and can recover most of this performance loss (0.646). Surprisingly, data-augmented model trained on smaller co-occur data also outperforms the baseline trained with Full data when tested on the co-occur split." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Experiments in three class setting on ADE20k'\nProblem: 'What is the performance of the Upernet model on the Only Single test split when trained on Co-occur data with data augmentation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/432.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Experiments in three class setting on ADE20k" ], "reasoning": "The table shows that the Upernet model trained on Co-occur data with data augmentation achieves a performance of 0.646 on the Only Single test split.", "question": "What is the performance of the Upernet model on the Only Single test split when trained on Co-occur data with data augmentation?", "answer": "So, the answer is {\"answer\": \"0.646\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Upernet model trained on Co-occur data with data augmentation outperforms the baseline trained on Full data when tested on the Co-occur split.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/432.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Experiments in three class setting on ADE20k" ], "reasoning": "The table shows that the Upernet model trained on Co-occur data with data augmentation achieves a performance of 0.86 on the Co-occur split, which is higher than the baseline performance of 0.797 when trained on Full data.", "statement": "The Upernet model trained on Co-occur data with data augmentation outperforms the baseline trained on Full data when tested on the Co-occur split.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Benchmarks of private SPN inference with CryptoSPN in a WAN network. The SPN has |X| RVs, |S| sum nodes, and |P| product nodes. Setup and online runtime as well as communication are measured for both 32- and 64-bit precision. All SPNs are RAT-SPNs with Bernoulli leaves except the ones for nips, which are regular SPNs with Poisson leaves. † indicates usage of a selection network for hiding RV assignments.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/433.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Benchmarks of private SPN inference with CryptoSPN in a WAN network. The SPN has |X| RVs, |S| sum nodes, and |P| product nodes. Setup and online runtime as well as communication are measured for both 32- and 64-bit precision. All SPNs are RAT-SPNs with Bernoulli leaves except the ones for nips, which are regular SPNs with Poisson leaves. † indicates usage of a selection network for hiding RV assignments." ], "reasoning": "The table shows that the setup phase for medium-sized SPNs requires minutes and gigabytes, while the online phase takes seconds and megabytes, with the number of sums having a larger effect on runtimes than products or leaves.", "description": "Our benchmarks are given in Table 1.. Generally, our results shown in Table 1 demonstrate that we achieve tractable setup and highly efficient online performance for medium-sized SPNs. Specifically, the setup phase requires costs in the order of minutes and gigabytes, while the online phase takes only a few seconds and megabytes.. While no single parameter appears to be decisive for the runtimes, we observe that some parameters are much more significant: 1. The number of sums has a significantly larger effect than products or leaves, which is expected given the log2 and exp2 operations. But, since the absolute amount of sums is still relatively small, the additional input weights do not affect online communication. 2. Though differences in the number of RVs, product nodes, leaves, and edges do influence the runtimes, deviations have to be very large to take an effect. For instance, when examining the SPNs. for accidents, baudio, and msweb, it takes roughly twice the amount of RVs and edges (the SPN for msweb) compared to the others to reach a significant runtime deviation. 3. When looking at the SPNs for nltcs, the first three SPNs have roughly the same density and the runtime seems to scale according to their size. The last two SPNs, however, have a noticeably higher density but comparable size and result in much higher runtimes. Thus, density (especially the amount of edges) is a much more significant parameter than plain network size. Yet, depending on the SPN, the costs of other, less important parameters can outweigh the costs of individual parameters. This is in line with our theoretical analysis in Section 3.2: the circuit's size depends on the number of children (with different costs for sums and products) as well as the number of RVs and leaves. The amount of layers has no direct effect because the round complexity of Yao's GC protocol is independent of the depth. As for the regular SPNs for nips, one can observe that the effects of hiding RV assignments are insignificant compared to the overall performance. Using 64-bit precision roughly doubles the costs of 32-bit precision, which is expected as the sub-circuits are about twice the size ." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Benchmarks of private SPN inference with CryptoSPN in a WAN network. The SPN has |X| RVs, |S| sum nodes, and |P| product nodes. Setup and online runtime as well as communication are measured for both 32- and 64-bit precision. All SPNs are RAT-SPNs with Bernoulli leaves except the ones for nips, which are regular SPNs with Poisson leaves. † indicates usage of a selection network for hiding RV assignments.'\nProblem: 'Which dataset has the highest setup time in seconds for 64-bit precision?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/433.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Benchmarks of private SPN inference with CryptoSPN in a WAN network. The SPN has |X| RVs, |S| sum nodes, and |P| product nodes. Setup and online runtime as well as communication are measured for both 32- and 64-bit precision. All SPNs are RAT-SPNs with Bernoulli leaves except the ones for nips, which are regular SPNs with Poisson leaves. † indicates usage of a selection network for hiding RV assignments." ], "reasoning": "To find the dataset with the highest setup time in seconds for 64-bit precision, look at the 'setup (s) 64b' column and identify the maximum value.", "question": "Which dataset has the highest setup time in seconds for 64-bit precision?", "answer": "So, the answer is {\"answer\": \"msweb\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe dataset 'nips' has the lowest online time in seconds for 32-bit precision.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/433.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Benchmarks of private SPN inference with CryptoSPN in a WAN network. The SPN has |X| RVs, |S| sum nodes, and |P| product nodes. Setup and online runtime as well as communication are measured for both 32- and 64-bit precision. All SPNs are RAT-SPNs with Bernoulli leaves except the ones for nips, which are regular SPNs with Poisson leaves. † indicates usage of a selection network for hiding RV assignments." ], "reasoning": "To verify this statement, check the 'online (s) 32b' column for the dataset 'nips' and compare it with other datasets to see if it has the lowest value.", "statement": "The dataset 'nips' has the lowest online time in seconds for 32-bit precision.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Performance using all features vs selected features'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/434.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance using all features vs selected features" ], "reasoning": "Models using all features generally perform better across all metrics compared to those using selected features.", "description": "Comparing the performance using all features vs selected features, Table 3 shows that models using all features (denoted with an appended -A, for instance RF-A) tend to show better results in terms of all performance metrics. However," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Performance using all features vs selected features'\nProblem: 'Which model shows the highest accuracy when using all features?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/434.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance using all features vs selected features" ], "reasoning": "By comparing the accuracy values of models using all features (denoted with -A), RF-A has the highest accuracy of 0.9987.", "question": "Which model shows the highest accuracy when using all features?", "answer": "So, the answer is {\"answer\": \"RF-A\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ANN-A model has a higher precision than the ET-S model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/434.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance using all features vs selected features" ], "reasoning": "The precision of ANN-A is 0.8929, while the precision of ET-S is 0.9950. Therefore, the statement is refuted.", "statement": "The ANN-A model has a higher precision than the ET-S model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Performance using domain features vs constructed features'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/435.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance using domain features vs constructed features" ], "reasoning": "RF-A performs better than all other algorithms in terms of accuracy, precision, recall, F-score, and AUC.", "description": "So we further compare the performance of models using the two best settings all features (i.e., baseline) vs domain features. We find that, among all models, RF using all features (denoted with an appended -A, for instance RF-A) performs better than all other algorithms (see Table 5)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Performance using domain features vs constructed features'\nProblem: 'Which algorithm has the highest F-score using all features?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/435.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance using domain features vs constructed features" ], "reasoning": "By comparing the F-scores of all algorithms using all features, RF-D1 has the highest F-score of 0.9932.", "question": "Which algorithm has the highest F-score using all features?", "answer": "So, the answer is {\"answer\": \"RF-D1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRF-D1 has the highest AUC among all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/435.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance using domain features vs constructed features" ], "reasoning": "The table shows that RF-D1 has an AUC of 0.9993, which is the highest among all models listed.", "statement": "RF-D1 has the highest AUC among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Performance of unseen attack detection using RF'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/436.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performance of unseen attack detection using RF" ], "reasoning": "The performances of all, selected, and domain feature settings are similar, except for the constructed feature settings.", "description": "As shown in Table 6, we see that except for the constructed feature settings (denoted by Cons.), the performances of other feature settings (all, selected, and domain) are similar." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Performance of unseen attack detection using RF'\nProblem: 'Which attack has the lowest performance in the constructed feature settings?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/436.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performance of unseen attack detection using RF" ], "reasoning": "By examining the 'Cons.(%)' column, we see that the 'Web Attack-Sql' has the lowest performance with 0.00%.", "question": "Which attack has the lowest performance in the constructed feature settings?", "answer": { "answer": "Web Attack-Sql" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe constructed feature settings have the lowest performance for the Web Attack-Sql.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/436.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performance of unseen attack detection using RF" ], "reasoning": "The table shows that the 'Cons.(%)' for 'Web Attack-Sql' is 0.00%, which is the lowest performance.", "statement": "The constructed feature settings have the lowest performance for the Web Attack-Sql.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison of direct and hierarchical planning. Values are averaged over 5 runs. Planning horizon: 20 steps. Belief start: [5,5]T. actual start: [3.5,2.0]T. Termination condition: Maximum likelihood estimate of belief converged within a ball of 0.2 unit radius around the goal ([0,0]T) with max covariance of 1 unit.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/437.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of direct and hierarchical planning. Values are averaged over 5 runs. Planning horizon: 20 steps. Belief start: [5,5]T. actual start: [3.5,2.0]T. Termination condition: Maximum likelihood estimate of belief converged within a ball of 0.2 unit radius around the goal ([0,0]T) with max covariance of 1 unit." ], "reasoning": "The hierarchical planner achieves comparable final error and belief uncertainty to the direct planner but does so approximately 5 times faster.", "description": "Additional statistical analysis to compare the two approaches in terms of total planning time, final error and final belief uncertainty are presented in Table 1. It can be seen from Table 1 that, for. comparable final error and final belief uncertainty, the hierarchical planner is able to find a solution approximately 5 times faster than the direct planning approach." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison of direct and hierarchical planning. Values are averaged over 5 runs. Planning horizon: 20 steps. Belief start: [5,5]T. actual start: [3.5,2.0]T. Termination condition: Maximum likelihood estimate of belief converged within a ball of 0.2 unit radius around the goal ([0,0]T) with max covariance of 1 unit.'\nProblem: 'How much faster is the hierarchical planner compared to the direct planner in terms of average total time?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/437.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of direct and hierarchical planning. Values are averaged over 5 runs. Planning horizon: 20 steps. Belief start: [5,5]T. actual start: [3.5,2.0]T. Termination condition: Maximum likelihood estimate of belief converged within a ball of 0.2 unit radius around the goal ([0,0]T) with max covariance of 1 unit." ], "reasoning": "The average total time for the direct planner is 51.908 seconds, while for the hierarchical planner it is 10.695 seconds. Dividing 51.908 by 10.695 gives approximately 4.85, which is close to 5 times faster.", "question": "How much faster (times) is the hierarchical planner compared to the direct planner in terms of average total time?", "answer": "So, the answer is {\"answer\": \"5 times\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe hierarchical planner has a lower average final maximum belief uncertainty than the direct planner.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/437.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of direct and hierarchical planning. Values are averaged over 5 runs. Planning horizon: 20 steps. Belief start: [5,5]T. actual start: [3.5,2.0]T. Termination condition: Maximum likelihood estimate of belief converged within a ball of 0.2 unit radius around the goal ([0,0]T) with max covariance of 1 unit." ], "reasoning": "The table shows that the average final maximum belief uncertainty for the hierarchical planner is 0.625, while for the direct planner it is 0.696. Since 0.625 is less than 0.696, the statement is supported.", "statement": "The hierarchical planner has a lower average final maximum belief uncertainty than the direct planner.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Triplet prediction accuracy on the FEC test set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/438.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Triplet prediction accuracy on the FEC test set." ], "reasoning": "The combination of AFFNet-CL-P and cosine distance achieves the best accuracy among AFFNet and FACSNet embeddings with 53.3%, but FECNet16d significantly outperforms it with an accuracy of 81.8%.", "description": "Table 2 shows the triplet prediction accuracy of various embeddings on the FEC test set using different distance functions. Among all the AFFNet and FACSNet embeddings, the combination of AFFNet-CL-P and cosine distance gives the best accuracy,. It is worth noting that the proposed FECNet16d (81.8%) performs significantly better than the best competing approach (AFFNet-CL-P + Cosine distance; 53.3%)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Triplet prediction accuracy on the FEC test set.'\nProblem: 'Which embedding and distance combination achieves the highest accuracy on the FEC test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/438.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Triplet prediction accuracy on the FEC test set." ], "reasoning": "By comparing the accuracies in the table, FECNet-16d with a distance function achieves the highest accuracy of 81.8%, which is higher than any other combination.", "question": "Which embedding and distance combination achieves the highest accuracy on the FEC test set?", "answer": "So, the answer is {\"answer\": \"FECNet-16d\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAFFNet-CL-P with cosine distance achieves the highest accuracy among all embeddings.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/438.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Triplet prediction accuracy on the FEC test set." ], "reasoning": "The table shows that AFFNet-CL-P with cosine distance achieves an accuracy of 53.3%, but FECNet-16d achieves a higher accuracy of 81.8%, refuting the statement.", "statement": "AFFNet-CL-P with cosine distance achieves the highest accuracy among all embeddings.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Datasets with user distributions'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/439.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Datasets with user distributions" ], "reasoning": "The dataset was collected from Twitter using three hashtags and consists of 19,492 original tweets from 13,270 unique users. Three reviewers annotated 1,434 user profiles as female, male, or organization, and the distribution of each user type for ILLAE is shown in the table.", "description": "We collected the dataset from Twitter using streaming API and based on three hashtags #ILookLikeAnEngineer, #LookLikeAnEngineer and #LookLikeEngineer as we had found instances of all of them being used in conjunction. The time frame for the data ranges from August 3rd, 2015, the day the hashtag was first used, until October 15th, 2015, which is about 2 months after the first initial surge of the campaign. The dataset consists of 19,492 original tweets from 13,270 unique users (individuals and organizations). Three reviewers from our research team individually annotated 1,434 user profiles as female, male or organization. The annotated user profiles were reviewed by the team and in case of a disagreement, the user type was decided by mutual discussion. The distribution of each user type for ILLAE is shown in Table 1." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Datasets with user distributions'\nProblem: 'What percentage of the ILLAE dataset is made up of organizations?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/439.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Datasets with user distributions" ], "reasoning": "The table shows that 43.93% of the ILLAE dataset is made up of organizations.", "question": "What percentage of the ILLAE dataset is made up of organizations?", "answer": { "answer": "43.93%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ILLAE dataset has more female profiles than male profiles.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/439.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Datasets with user distributions" ], "reasoning": "The table shows that the ILLAE dataset has 451 female profiles and 353 male profiles, indicating that there are more female profiles than male profiles.", "statement": "The ILLAE dataset has more female profiles than male profiles.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Classifiers results in different feature sets for CrowdFlower dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/440.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Classifiers results in different feature sets for CrowdFlower dataset" ], "reasoning": "The image classifier outperforms the organization/individual classifier with an F1 score of 72.94% and 75.71% for the organization class, and the proposed framework with SVM classifier achieves the highest overall accuracy of 78.61%.", "description": "In Table 3, you will see the result of CrowdFlower dataset. But in this dataset, organization/individual classifier with 68.74% F1 score for organization class is outperformed by two classifiers beside the combined one: text (70.90% and 70.53% F1 score-org) and image (72.94% and 75.71% F1 score-org). Image and text features alone are enough to classify organizations and there is no need to gather extra information about the historical tweets or behavioral features in the user profile. Again, the name database has higher F1 score-female (70.11%) than the other classifiers except the combined one. Proposed framework with SVM classifier outperforms the rest of classifiers with the average overall accuracy of 78.61%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Classifiers results in different feature sets for CrowdFlower dataset'\nProblem: 'Which classifier has the highest F1 score for the female class?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/440.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Classifiers results in different feature sets for CrowdFlower dataset" ], "reasoning": "By examining the table, the proposed framework with SVM classifier has the highest F1 score for the female class at 81.27%.", "question": "Which classifier has the highest F1 score for the female class?", "answer": "So, the answer is {\"answer\": \"81.27%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe text classifier with SVM has a higher F1 score for the organization class than the image classifier with RF.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/440.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Classifiers results in different feature sets for CrowdFlower dataset" ], "reasoning": "The table shows that the text classifier with SVM has an F1 score of 70.90% for the organization class, while the image classifier with RF has an F1 score of 75.71% for the organization class.", "statement": "The text classifier with SVM has a higher F1 score for the organization class than the image classifier with RF.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Classifiers results in different feature sets for ILLAE dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/441.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Classifiers results in different feature sets for ILLAE dataset" ], "reasoning": "The proposed classifier with RF achieves the highest overall accuracy of 85.99%, outperforming all other classifiers, while the image feature set shows superior F1 scores for both organization and individual classifications.", "description": "In Table 2, the performance of each baseline is calculated based on overall accuracy and F1 score for each class. As you can see, organization/individual classifier does the best job among baselines with 84.85% F1 score for organization class as it serves its main purpose. But the classifier with image feature set outperforms that classifier with 87.77% and 91.65% F1 score in both classifiers. It means image is a more discriminant feature than historical tweets. On the other hand, name database is effective in gender classification of individuals and it outperforms all of the other classifiers except the proposed framework. At the end, the proposed classifier with RF outperforms all of the other classifiers with the average overall accuracy of 85.99%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Classifiers results in different feature sets for ILLAE dataset'\nProblem: 'Which classifier has the highest F1 score for the organization class and what is the score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/441.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Classifiers results in different feature sets for ILLAE dataset" ], "reasoning": "The table shows that the classifier with the highest F1 score for the organization class is the Image RF classifier with an F1 score of 91.65%.", "question": "Which classifier has the highest F1 score for the organization class?", "answer": { "answer": "Image RF%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe name database classifier has the highest F1 score for the female class.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/441.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Classifiers results in different feature sets for ILLAE dataset" ], "reasoning": "The table shows that the name database classifier has an F1 score of 71.41% for the female class, but the proposed RF classifier has a higher F1 score of 83.13% for the female class.", "statement": "The name database classifier has the highest F1 score for the female class.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: User type distribution in entire ILLAE dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/442.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: User type distribution in entire ILLAE dataset" ], "reasoning": "The ILLAE dataset shows that female users are the most dominant user type with 47.95%, followed by organizations with 35.31%.", "description": "The user distribution of the ILLAE dataset, Table 4, shows that female users are the most dominant user types in this online social campaign with 47.95% which is followed by organizations with 35.31%. Organiza" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: User type distribution in entire ILLAE dataset'\nProblem: 'What is the total number of users in the ILLAE dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/442.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: User type distribution in entire ILLAE dataset" ], "reasoning": "The table shows the total number of users in the 'Total' column, which is 13,270.", "question": "What is the total number of users in the ILLAE dataset?", "answer": "So, the answer is {\"answer\": \"13,270\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMale users make up the smallest percentage of the ILLAE dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/442.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: User type distribution in entire ILLAE dataset" ], "reasoning": "The table shows that male users make up 16.74%, which is less than both female users (47.95%) and organizations (35.31%).", "statement": "Male users make up the smallest percentage of the ILLAE dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Tweet, retweet and favorite distribution in entire ILLAE dataset'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/443.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Tweet, retweet and favorite distribution in entire ILLAE dataset" ], "reasoning": "Female users dominate in tweets, retweets, and favorites, highlighting the campaign's importance among women. Organizations also play a significant role, contributing notably to the campaign's sustainability.", "description": "On the same note, we would like to see the tweet, retweet and favorite distribution for each user types in the entire ILLAE dataset. In Table 5, female is the most dominant user type in all of the categories as it shows the importance of this campaign among women. Organizations' impact on this campaign is noteworthy in a sense that one of the main factors of sustainability in an online social campaign is the high level of organization engagement to involve more people in the cause. This is also confirmed by the social network analysis in the previous section that in the top 25 users with highest in-degree, 76% of them are of organization type." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Tweet, retweet and favorite distribution in entire ILLAE dataset'\nProblem: 'What percentage of retweets were made by female users?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/443.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Tweet, retweet and favorite distribution in entire ILLAE dataset" ], "reasoning": "To find the percentage of retweets made by female users, look at the 'Retweet' row and the 'Female' column. The table shows that 53.28% of retweets were made by female users.", "question": "What percentage of retweets were made by female users?", "answer": { "answer": "53.28%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFemale users made the majority of tweets in the ILLAE dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/443.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Tweet, retweet and favorite distribution in entire ILLAE dataset" ], "reasoning": "The table shows that female users made 8,993 tweets, which is the highest number compared to male users (2,995) and organizations (7,504). Therefore, the statement is supported.", "statement": "Female users made the majority of tweets in the ILLAE dataset.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Triplet prediction accuracy for different types of triplets in the FEC test set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/444.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Triplet prediction accuracy for different types of triplets in the FEC test set." ], "reasoning": "The table shows that the best performance is for two-class triplets with 85.1% accuracy, while the lowest performance is for one-class triplets with 77.1% accuracy.", "description": "Table 3 shows the triplet prediction accuracy of median rater, FECNet-16d and AFFNet-CL-P for each triplet type in the FEC test set.. the performance is best (85.1%) for two-class triplets, and is lowest (77.1%) for one-class triplets," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Triplet prediction accuracy for different types of triplets in the FEC test set.'\nProblem: 'What is the accuracy of FECNet-16d for three-class triplets?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/444.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Triplet prediction accuracy for different types of triplets in the FEC test set." ], "reasoning": "To find the accuracy of FECNet-16d for three-class triplets, look at the row labeled 'Three-class' and the column labeled 'FECNet-16d'. The value is 82.6%.", "question": "What is the accuracy of FECNet-16d for three-class triplets?", "answer": "So, the answer is {\"answer\": \"82.6%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe median rater's accuracy for one-class triplets is higher than for three-class triplets.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/444.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Triplet prediction accuracy for different types of triplets in the FEC test set." ], "reasoning": "The table shows that the median rater's accuracy for one-class triplets is 85.3%, while for three-class triplets it is 87.2%. Therefore, the statement is refuted.", "statement": "The median rater's accuracy for one-class triplets is higher than for three-class triplets.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Similarity of attention estimates. KL-D denotes Kullback-Leibler divergence and CC denotes Correlation Coefficient.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/445.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Similarity of attention estimates. KL-D denotes Kullback-Leibler divergence and CC denotes Correlation Coefficient." ], "reasoning": "The predicted attention weights have significantly lower KL divergence and higher CC compared to both distance related and self-attention weights.", "description": "As shown in Table. IVA, the KL divergence for the predicted attention weights is 50.5% smaller than for the distance related weights and 46.7% smaller than for the self-attention weights. Similarly, the CC for the predicted attention weights is 21.3% larger than for the distance related weights and 17.5 % larger than for the self-attention weights." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Similarity of attention estimates. KL-D denotes Kullback-Leibler divergence and CC denotes Correlation Coefficient.'\nProblem: 'What is the KL divergence for the predicted attention weights and how does it compare to the distance related weights?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/445.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Similarity of attention estimates. KL-D denotes Kullback-Leibler divergence and CC denotes Correlation Coefficient." ], "reasoning": "The KL divergence for the predicted attention weights is 0.49, which is 50.5% smaller than the KL divergence for the distance related weights, which is 0.99.", "question": "What is the KL divergence for the predicted attention weights and how does it compare to the distance related weights?", "answer": "So, the answer is {\"answer\": \"0.49\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe CC for the predicted attention weights is 0.74.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/445.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Similarity of attention estimates. KL-D denotes Kullback-Leibler divergence and CC denotes Correlation Coefficient." ], "reasoning": "The table shows that the CC for the predicted attention weights is indeed 0.74.", "statement": "The CC for the predicted attention weights is 0.74.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Comparison to the state-of-the-art, SARL.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/446.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Comparison to the state-of-the-art, SARL." ], "reasoning": "SA-GCNRL outperforms SARL with a 7.9% higher success rate and a 6.0% shorter navigation time, while G-GCNRL surpasses SA-GCNRL with a 9.7% higher success rate and an 11.1% shorter navigation time.", "description": "As shown in Table II, SA-GCNRL achieves on average a 7.9% higher success rate and a 6.0% shorter navigation time than SARL.. As shown in Table II, G-GCNRL achieves a 9.7% higher success rate and an 11.1% shorter navigation time than SA-GCNRL." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Comparison to the state-of-the-art, SARL.'\nProblem: 'What is the average success rate difference between SARL and G-GCNRL?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/446.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Comparison to the state-of-the-art, SARL." ], "reasoning": "To find the average success rate difference between SARL and G-GCNRL, subtract the average success rate of SARL (0.621) from the average success rate of G-GCNRL (0.735).", "question": "What is the average success rate difference between SARL and G-GCNRL?", "answer": { "answer": "0.114" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nG-GCNRL has the shortest average navigation time among the three methods.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/446.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Comparison to the state-of-the-art, SARL." ], "reasoning": "The table shows that G-GCNRL has an average navigation time of 11.2, which is shorter than both SA-GCNRL (12.6) and SARL (13.4).", "statement": "G-GCNRL has the shortest average navigation time among the three methods.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Additional ablation study to show the advantage of the attention weights trained based on human gaze data.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/447.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Additional ablation study to show the advantage of the attention weights trained based on human gaze data." ], "reasoning": "G-GCNRL has a success rate that is 10.5% higher than D-GCNRL and 5.0% higher than U-GCNRL.", "description": "As shown in Table III, the success rate of G-GCNRL was 10.5% higher than that of D-GCNRL and 5.0% higher than that of U-GCNRL." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Additional ablation study to show the advantage of the attention weights trained based on human gaze data.'\nProblem: 'What is the success rate of D-GCNRL and U-GCNRL?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/447.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Additional ablation study to show the advantage of the attention weights trained based on human gaze data." ], "reasoning": "The success rate of D-GCNRL is 0.665 and the success rate of U-GCNRL is 0.700.", "question": "What is the average success rate of D-GCNRL?", "answer": { "answer": "0.665" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nG-GCNRL has the highest success rate among all methods.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/447.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Additional ablation study to show the advantage of the attention weights trained based on human gaze data." ], "reasoning": "The success rate of G-GCNRL is 0.735, which is higher than SA-GCNRL (0.670), D-GCNRL (0.665), and U-GCNRL (0.700).", "statement": "G-GCNRL has the highest success rate among all methods.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Additional ablation study to show the advantage of the graph structure.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/448.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Additional ablation study to show the advantage of the graph structure." ], "reasoning": "U-GCNRL has the highest success rate and shortest navigation time among the methods.", "description": "As shown in Table IV, U-GCNRL achieves 23.7% higher success rate and 23.2% shorter navigation time.. As shown in Table IV, SA-GCNRL achieves 7.9% higher success rate and 6.0% shorter navigation time." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Additional ablation study to show the advantage of the graph structure.'\nProblem: 'Which method achieves the highest average success rate and the shortest average navigation time?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/448.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Additional ablation study to show the advantage of the graph structure." ], "reasoning": "By comparing the average success rates and average navigation times of all methods, U-GCNRL has the highest average success rate of 0.700 and the shortest average navigation time of 10.6.", "question": "Which method achieves the highest average success rate and the shortest average navigation time?", "answer": { "answer": "U-GCNRL" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSA-GCNRL achieves a higher success rate than SARL.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/448.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Additional ablation study to show the advantage of the graph structure." ], "reasoning": "By comparing the average success rates, SA-GCNRL has an average success rate of 0.681, which is higher than SARL's average success rate of 0.621.", "statement": "SA-GCNRL achieves a higher success rate than SARL.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Number of votes received by the summaries generated by the FECNet-16d and AFFNet-CL-P embeddings.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/449.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Number of votes received by the summaries generated by the FECNet-16d and AFFNet-CL-P embeddings." ], "reasoning": "Humans preferred the FECNet-16d embedding for eight out of ten albums, as shown by the higher vote counts in the table.", "description": "Table 4 shows the number of votes received by both the embeddings for all the albums. Humans prefer the summaries generated by the proposed FECNet-16d embedding for eight out of ten albums." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Number of votes received by the summaries generated by the FECNet-16d and AFFNet-CL-P embeddings.'\nProblem: 'How many albums did the FECNet-16d embedding receive more votes than the AFFNet-CL-P embedding?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/449.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Number of votes received by the summaries generated by the FECNet-16d and AFFNet-CL-P embeddings." ], "reasoning": "By comparing the vote counts for each album, FECNet-16d received more votes than AFFNet-CL-P in eight out of ten albums.", "question": "How many albums did the FECNet-16d embedding receive more votes than the AFFNet-CL-P embedding?", "answer": "So, the answer is {\"answer\": \"8\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFECNet-16d embedding received more votes than AFFNet-CL-P embedding for the album 'KM'.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/449.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Number of votes received by the summaries generated by the FECNet-16d and AFFNet-CL-P embeddings." ], "reasoning": "The table shows that for the album 'KM', FECNet-16d received 10 votes while AFFNet-CL-P received 0 votes.", "statement": "FECNet-16d embedding received more votes than AFFNet-CL-P embedding for the album 'KM'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Statistics of VisualGenome and MSCOCO datasets for controllable image captioning with ASGs.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/450.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Statistics of VisualGenome and MSCOCO datasets for controllable image captioning with ASGs." ], "reasoning": "The ASGs in MSCOCO are more complex than those in VisualGenome because they have more relationships and longer captions.", "description": "As shown in Table 1, the ASGs in MSCOCO are more complex than those in VisualGenome dataset since they contain more relationships and the captions are longer." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Statistics of VisualGenome and MSCOCO datasets for controllable image captioning with ASGs.'\nProblem: 'Which dataset has more objects per sentence and longer captions?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/450.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Statistics of VisualGenome and MSCOCO datasets for controllable image captioning with ASGs." ], "reasoning": "By comparing the '#objs per sent' and '#words per sent' columns for both datasets, MSCOCO has 2.93 objects per sentence and 10.28 words per sentence, while VisualGenome has 2.09 objects per sentence and 5.30 words per sentence.", "question": "Which dataset has more objects per sentence and longer captions?", "answer": "So, the answer is {\"answer\": \"MSCOCO\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMSCOCO has more relationships per sentence than VisualGenome.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/450.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Statistics of VisualGenome and MSCOCO datasets for controllable image captioning with ASGs." ], "reasoning": "The '#rels per sent' column shows that MSCOCO has 1.56 relationships per sentence, while VisualGenome has 0.95 relationships per sentence.", "statement": "MSCOCO has more relationships per sentence than VisualGenome.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparison with carefully designed baselines for controllable image caption generation conditioning on ASGs.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/451.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison with carefully designed baselines for controllable image caption generation conditioning on ASGs." ], "reasoning": "Controllable baselines outperform non-controllable baselines due to the awareness of control signal ASG, and baseline models struggle to generate designated attributes compared to objects and relationships.", "description": "Table 2 presents the comparison result. It is worth noting that controllable baselines outperform non-controllable baselines due to the awareness of control signal ASG. We can also see that baseline models are struggling to generate designated attributes compared to objects and relationships according to detailed graph structure metrics." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparison with carefully designed baselines for controllable image caption generation conditioning on ASGs.'\nProblem: 'Which method has the highest score in VisualGenome C?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/451.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison with carefully designed baselines for controllable image caption generation conditioning on ASGs." ], "reasoning": "To find the method with the highest score in VisualGenome C, compare the values in the VisualGenome C column. The highest value is 202.4, which corresponds to the 'Ours' method.", "question": "Which method has the highest score in VisualGenome C?", "answer": "So, the answer is {\"answer\": \"Ours\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe C-ST method has a higher score in MSCOCO R than in VisualGenome R.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/451.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison with carefully designed baselines for controllable image caption generation conditioning on ASGs." ], "reasoning": "To verify this statement, compare the scores of the C-ST method in MSCOCO R and VisualGenome R. The score in MSCOCO R is 41.4, and in VisualGenome R it is 37.6. Since 41.4 is greater than 37.6, the statement is supported.", "statement": "The C-ST method has a higher score in MSCOCO R than in VisualGenome R.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Ablation study to demonstrate contributions from different proposed components. (role: role-aware node embedding; rgcn: MR-GCN; ctn: graph content attention; flow: graph flow attention; gupdt: graph updating; bs: beam search)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/452.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation study to demonstrate contributions from different proposed components. (role: role-aware node embedding; rgcn: MR-GCN; ctn: graph content attention; flow: graph flow attention; gupdt: graph updating; bs: beam search)" ], "reasoning": "Incorporating role-aware node embedding, MR-GCN, graph flow attention, and graph updating in the model progressively improves performance, with the best results achieved by combining all components and applying beam search.", "description": "In order to demonstrate contributions from different components in our model, we provide an extensive ablation study in Table 3.. in Row 3, we add the role-aware node embedding in the encoder and the performance is largely improved, which indicates that it is important to distinguish different intention roles in the graph. Comparing Row 4 against Row 3 where the MR-GCN is employed for contextual graph encoding, we see that graph context is beneficial for the graph node encoding. Row 5 and 6 enhance the decoder with graph flow attention and graph updating respectively. The graph flow attention shows complementarity with the graph content attention via capturing the structure information in the graph, and outperforms Row 4 on two datasets. However, the graph updating mechanism is more effective on MSCOCO dataset where the number of graph nodes are larger than on VisualGenome dataset. Since the graph updating module explicitly records the status of graph nodes, the effectiveness might be more apparent when generating longer sentences for larger graphs. In Row 7, we incorporate all the proposed components which obtains further gains. Finally, we apply beam search on the proposed model and achieves the best performance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Ablation study to demonstrate contributions from different proposed components. (role: role-aware node embedding; rgcn: MR-GCN; ctn: graph content attention; flow: graph flow attention; gupdt: graph updating; bs: beam search)'\nProblem: 'Which row shows the highest performance on the MSCOCO B4 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/452.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation study to demonstrate contributions from different proposed components. (role: role-aware node embedding; rgcn: MR-GCN; ctn: graph content attention; flow: graph flow attention; gupdt: graph updating; bs: beam search)" ], "reasoning": "By examining the MSCOCO B4 column, the highest value is 23.0, which corresponds to Row 8.", "question": "Which row shows the highest performance on the MSCOCO B4 dataset?", "answer": { "answer": "8" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model with graph flow attention outperforms the model with MR-GCN on the VisualGenome B4 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/452.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation study to demonstrate contributions from different proposed components. (role: role-aware node embedding; rgcn: MR-GCN; ctn: graph content attention; flow: graph flow attention; gupdt: graph updating; bs: beam search)" ], "reasoning": "Comparing Row 5 (graph flow attention) and Row 4 (MR-GCN) on the VisualGenome B4 dataset, Row 5 has a higher score (15.9 vs. 15.7).", "statement": "The model with graph flow attention outperforms the model with MR-GCN on the VisualGenome B4 dataset.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Comparison with state-of-the-art approaches for diverse image caption generation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/453.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison with state-of-the-art approaches for diverse image caption generation." ], "reasoning": "The table shows that the 'Ours' method for both Visual Genome and MS COCO datasets has higher SelfCIDER scores, indicating more diverse and semantically similar captions compared to other methods.", "description": "As shown in Table 4, the generated captions of our approach are more diverse than compared methods especially on the SelfCider score which focuses on semantic similarity." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Comparison with state-of-the-art approaches for diverse image caption generation.'\nProblem: 'Which method has the highest SelfCIDER score for the MS COCO dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/453.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison with state-of-the-art approaches for diverse image caption generation." ], "reasoning": "By examining the SelfCIDER scores for the MS COCO dataset, the 'Ours' method has the highest score of 0.76.", "question": "Which method has the highest SelfCIDER score for the MS COCO dataset?", "answer": "So, the answer is {\"answer\": \"Ours\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'Region' method for the Visual Genome dataset has a higher SelfCIDER score than the 'Ours' method for the MS COCO dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/453.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison with state-of-the-art approaches for diverse image caption generation." ], "reasoning": "The table shows that the 'Region' method for the Visual Genome dataset has a SelfCIDER score of 0.47, while the 'Ours' method for the MS COCO dataset has a higher SelfCIDER score of 0.76.", "statement": "The 'Region' method for the Visual Genome dataset has a higher SelfCIDER score than the 'Ours' method for the MS COCO dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Arousal Discrimination Results'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/454.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Arousal Discrimination Results" ], "reasoning": "Pretraining the reservoir with synaptic plasticity rules and using intrinsic plasticity rule achieves the best results for EEG signal classification.", "description": "problems using DEAP dataset. For arousal classification, using ESN with offline training which is the linear regression yields better results than the online training using the delta rule. An exception is made for the case of the reservoir pretrained with the Oja's rule using the online training of output weights as depicted in Table 4. The comparison is handled with the work in since it belongs to the same context as the current work.. We highlight that using feature as input achieves higher the best accuracy results while using BCM rule. But,. achieved is 69.23% with ESN pretrained with IP using the hybrid mode inputted directly with EEG channel signal.. trained them first with linear regression. As a result, the performance increased up to 14% over the state of the art method .. But, for the other problems the offline outperforms the online mode.. Pretraining the reservoir with Synaptic plasticity rules achieves higher results using the feature vector as rules achieves higher results input than the raw EEG signal.. In all cases, pretraining ESN using intrinsic plasticity rule has achieved the best results either with using feature as input or raw EEG signal. As a consequence, we recommend the use of IP rule and the hybrid mode for classification of EEG signals." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Arousal Discrimination Results'\nProblem: 'Which system and input type combination achieved the highest accuracy in the hybrid mode?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/454.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Arousal Discrimination Results" ], "reasoning": "By examining the table, the highest accuracy in the hybrid mode is 69.23%, achieved by the ESN-IP rule with the signal input type.", "question": "Which system with signal achieved the highest accuracy in the hybrid mode?", "answer": "So, the answer is {\"answer\": \"ESN-IP rule\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ESN-BCM rule with feature input type achieved higher accuracy in the hybrid mode than the ESN-Oja rule with signal input type.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/454.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Arousal Discrimination Results" ], "reasoning": "The table shows that the ESN-BCM rule with feature input type achieved 62.17% accuracy in the hybrid mode, while the ESN-Oja rule with signal input type achieved 60.29%. Therefore, the statement is supported.", "statement": "The ESN-BCM rule with feature input type achieved higher accuracy in the hybrid mode than the ESN-Oja rule with signal input type.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: Valence Discrimination Results'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/455.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Valence Discrimination Results" ], "reasoning": "The reservoir pretrained with IP using the hybrid training achieves the highest result of 71.25%, outperforming existing work using HMM with signal as input by up to 13%.", "description": "For valence classification, the reservoir pretrained with IP using the hybrid training reaches the highest result up to 71.25%. Our system, again, outperforms the existing work using HMM with signal as input up to 13% as shown in Table 5. Furthermore, Oja's rule achieves higher result with signal as input 62.13% than the feature 60.81%. While, the BCM rule achieves higher performance 59.31% with signal than the feature input 58.26%.. other problems the offline outperforms the online mode.. In all cases, pretraining ESN using intrinsic plasticity rule has achieved the best results either with using feature as input or raw EEG signal. As a consequence, we recommend the use of IP rule and the hybrid mode for classification of EEG signals." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: Valence Discrimination Results'\nProblem: 'Which system achieves the highest performance using the hybrid mode, and what is the percentage?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/455.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Valence Discrimination Results" ], "reasoning": "The table shows that the ESN-IP rule with signal input achieves the highest performance in the hybrid mode with a percentage of 71.25%.", "question": "Which system with signal input achieves the highest performance using the hybrid mode?", "answer": { "answer": "ESN-IP rule" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ESN-BCM rule with signal input achieves higher performance in the hybrid mode than the ESN-Oja rule with feature input.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/455.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Valence Discrimination Results" ], "reasoning": "The table shows that the ESN-BCM rule with signal input achieves 59.31% in the hybrid mode, while the ESN-Oja rule with feature input achieves 60.81%. Therefore, the statement is refuted.", "statement": "The ESN-BCM rule with signal input achieves higher performance in the hybrid mode than the ESN-Oja rule with feature input.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VI: Emotional states Discrimination Results'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/456.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Emotional states Discrimination Results" ], "reasoning": "The hybrid mode achieves the best result of 69.95% for ESN-IP rule with signal input, outperforming the SVM classifier with FD features.", "description": "discrimination of 8 emotional states. ur work since the comThe online mode is more. efficient here than the offline mode for both signal and feature input. The hybridization allows us to achieve the best result which is 69.95%. In the literature, most of the existing works classify arousal and valence levels. There is only one work which classifies 8 emotional states . Our ESN trained with linear regression followed with the delta rule outperforms the SVM classifier with FD features.. According to Table 6, the ESN is more robust when using. According to Table 6, t signal instead of feature.. Overall, the online mode is better than the offline mode for the classification of the emotional states.. We can conclude that the delta rule is more efficient when the complexity of the architecture is high than the linear regression." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VI: Emotional states Discrimination Results'\nProblem: 'Which system and input type combination achieves the highest accuracy in the hybrid mode?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/456.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Emotional states Discrimination Results" ], "reasoning": "By examining the table, the highest accuracy in the hybrid mode is 69.95%, achieved by the ESN-IP rule with signal input.", "question": "Which system with signal input achieves the highest accuracy in the hybrid mode?", "answer": "So, the answer is {\"answer\": \"ESN-IP rule\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ESN-BCM rule with signal input achieves the highest accuracy in the hybrid mode.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/456.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Emotional states Discrimination Results" ], "reasoning": "The table shows that the ESN-BCM rule with signal input achieves 60.23% accuracy in the hybrid mode, which is not the highest. The highest is 69.95% by the ESN-IP rule with signal input.", "statement": "The ESN-BCM rule with signal input achieves the highest accuracy in the hybrid mode.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VII: Stress/Calm Discrimination Results'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/457.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: Stress/Calm Discrimination Results" ], "reasoning": "Combining linear regression and delta rule training improved accuracy from 61.82% to 67.27%, while ESN with IP achieved 76.15% accuracy with bandpower features.", "description": "When discriminating stress and. calm, we remark that ESN with synaptic plasticity achieves the same accuracy with signal as well feature input which is 65.45%. Combining linear regression and delta rule training enhanced the performance from 61.82% to 67.27%. While using ESN with IP, we can rise the accuracy to 76.15% with bandpower features. When inputting signal to ESN with IP, we obtained an accuracy of 41.82% and 49.09% with the offline mode and online mode, respectively. The hybrid mode shows its effectiveness in this case to reach 61.27% as recognition rate. We believe that existing work combining SVM classifier with entropy features achieves the best result up to 81.31%, but we highlight that we proved that feeding ESN with EEG signal directly can achieve better result than bandpower features which is the aim of our work as depicted in Table 7.. sing the feature vector as The only case the synaptic plasticity reaches good accuracy rate with signal is the classification of stress / calm states using Oja's rule." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VII: Stress/Calm Discrimination Results'\nProblem: 'What is the highest accuracy achieved using ESN with IP rule and feature input?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/457.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: Stress/Calm Discrimination Results" ], "reasoning": "The table shows that the highest accuracy achieved using ESN with IP rule and feature input is 76.15% in the hybrid mode.", "question": "What is the highest accuracy achieved using ESN with IP rule and feature input?", "answer": "So, the answer is {\"answer\": \"76.15%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nESN with IP rule and signal input achieves higher accuracy in online mode than offline mode.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/457.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: Stress/Calm Discrimination Results" ], "reasoning": "The table shows that ESN with IP rule and signal input achieves 49.09% accuracy in online mode and 41.82% accuracy in offline mode, which supports the statement.", "statement": "ESN with IP rule and signal input achieves higher accuracy in online mode than offline mode.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table III: Accuracy (A), Precision(P) and Recall(R) for shape, color and class annotations of object hypotheses with and without the amortization effects.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/458.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table III: Accuracy (A), Precision(P) and Recall(R) for shape, color and class annotations of object hypotheses with and without the amortization effects." ], "reasoning": "The table shows performance measures for shape, color, and class-label across four episodes, highlighting an increase in metrics for shape and color, while classification shows a slight decrease in accuracy and precision but a 10% increase in coverage.", "description": "Table III reports the performance measures of one-shot perception for each three symbols (shape, color, class-label) separately. The object class results represent an interesting scenario. Even though the average accuracy and precision of the classification is high, only 82.2 % of hypotheses generated in the four episodes are annotated. Table III presents the performance measures on the four episodes using these parameters. Overall, in the case of shape and color we can notice a general increase in all metrics, while in the case of classification there is a slight decrease in accuracy and precision but with a 10 % increase in coverage." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table III: Accuracy (A), Precision(P) and Recall(R) for shape, color and class annotations of object hypotheses with and without the amortization effects.'\nProblem: 'What is the average precision for color in the one-shot perception method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/458.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table III: Accuracy (A), Precision(P) and Recall(R) for shape, color and class annotations of object hypotheses with and without the amortization effects." ], "reasoning": "To find the average precision for color in the one-shot perception method, look at the 'Color' row under 'One-shot' and find the value in the 'Average P' column.", "question": "What is the average precision for color in the one-shot perception method?", "answer": "So, the answer is {\"answer\": \"0.94\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average recall for shape in the amortized method is higher than in the one-shot method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/458.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table III: Accuracy (A), Precision(P) and Recall(R) for shape, color and class annotations of object hypotheses with and without the amortization effects." ], "reasoning": "To verify this statement, compare the 'Average R' values for shape in the 'Amortized' and 'One-shot' rows. The average recall for shape in the amortized method is 0.71, which is higher than the 0.66 in the one-shot method.", "statement": "The average recall for shape in the amortized method is higher than in the one-shot method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: The area distortion mean(|darea|) of the global conformal parameterizations produced by the boundary first flattening (BFF) method [56], the proposed PGCP method, and the proposed PGCP method with an additional step of composing with a Möbius transformation ((41) for free-boundary conformal parameterization, (42) for disk-boundary conformal parameterization, and (41) together with the stereographic projection for spherical conformal parameterization).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/459.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: The area distortion mean(|darea|) of the global conformal parameterizations produced by the boundary first flattening (BFF) method [56], the proposed PGCP method, and the proposed PGCP method with an additional step of composing with a Möbius transformation ((41) for free-boundary conformal parameterization, (42) for disk-boundary conformal parameterization, and (41) together with the stereographic projection for spherical conformal parameterization)." ], "reasoning": "The PGCP method achieves lower area distortion for spherical conformal parameterization and similar area distortions for disk conformal parameterization compared to the BFF method. The BFF method has lower area distortion for free-boundary conformal parameterization.", "description": "Table 7 shows the area distortion of the BFF method and the proposed PGCP method for various types of global conformal parameterizations. For spherical conformal parameterization, our method achieves a lower area distortion. For disk conformal parameterization, the two methods achieve similar area distortions. For free-boundary conformal parameterization, the BFF method possesses a lower area distortion.. The final results are recorded in the rightmost column of Table 7. It can be observed that the area distortion is effectively reduced with the aid of M¨obius transformations in many cases, especially for free-boundary conformal parameterizations and spherical conformal parameterizations. The improvement for disk conformal parameterizations is relatively less significant, possibly due to the smaller number of free parameters. To achieve an even lower area distortion may require the composition with some other conformal transformations, which we plan to explore in the future." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: The area distortion mean(|darea|) of the global conformal parameterizations produced by the boundary first flattening (BFF) method [56], the proposed PGCP method, and the proposed PGCP method with an additional step of composing with a Möbius transformation ((41) for free-boundary conformal parameterization, (42) for disk-boundary conformal parameterization, and (41) together with the stereographic projection for spherical conformal parameterization).'\nProblem: 'Which method achieves the lowest area distortion for the 'Horse' surface under spherical conformal parameterization?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/459.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: The area distortion mean(|darea|) of the global conformal parameterizations produced by the boundary first flattening (BFF) method [56], the proposed PGCP method, and the proposed PGCP method with an additional step of composing with a Möbius transformation ((41) for free-boundary conformal parameterization, (42) for disk-boundary conformal parameterization, and (41) together with the stereographic projection for spherical conformal parameterization)." ], "reasoning": "The 'Horse' surface under spherical conformal parameterization has area distortions of 27.03 for BFF, 8.90 for PGCP, and 6.54 for PGCP with an additional Möbius transformation.", "question": "Which method achieves the highest area distortion for the 'Horse' surface under spherical conformal parameterization?", "answer": "So, the answer is {\"answer\": \"BFF\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BFF method achieves the lowest area distortion for the 'Buddha' surface under free-boundary conformal parameterization.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/459.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: The area distortion mean(|darea|) of the global conformal parameterizations produced by the boundary first flattening (BFF) method [56], the proposed PGCP method, and the proposed PGCP method with an additional step of composing with a Möbius transformation ((41) for free-boundary conformal parameterization, (42) for disk-boundary conformal parameterization, and (41) together with the stereographic projection for spherical conformal parameterization)." ], "reasoning": "For the 'Buddha' surface under free-boundary conformal parameterization, the area distortions are 0.78 for BFF, 1.20 for PGCP, and 1.16 for PGCP with an additional Möbius transformation. The lowest value is 0.78 for BFF.", "statement": "The BFF method achieves the lowest area distortion for the 'Buddha' surface under free-boundary conformal parameterization.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: The performance of the conformal energy minimization (CEM) [26] method, CEM combined with the Combinatorial Multigrid (CMG) [58], and our proposed PGCP method for disk conformal parameterization of simply-connected open surfaces.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/460.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: The performance of the conformal energy minimization (CEM) [26] method, CEM combined with the Combinatorial Multigrid (CMG) [58], and our proposed PGCP method for disk conformal parameterization of simply-connected open surfaces." ], "reasoning": "CEM combined with CMG improves efficiency for large problems compared to CEM, but our proposed method is more efficient and conformal.", "description": "Table 8 shows the performance of CEM, CEM combined with CMG, and our proposed method. It can be observed that while CEM combined with CMG demonstrates an improvement in efficiency for large problems when compared to the original CEM, our proposed method is still more advantageous in terms of both the efficiency and conformality." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 8: The performance of the conformal energy minimization (CEM) [26] method, CEM combined with the Combinatorial Multigrid (CMG) [58], and our proposed PGCP method for disk conformal parameterization of simply-connected open surfaces.'\nProblem: 'Which method shows the highest efficiency improvement for the Nefertiti surface?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/460.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: The performance of the conformal energy minimization (CEM) [26] method, CEM combined with the Combinatorial Multigrid (CMG) [58], and our proposed PGCP method for disk conformal parameterization of simply-connected open surfaces." ], "reasoning": "The Nefertiti surface has the highest number of vertices (1M). Comparing the times, CEM takes 83.2s, CEM with CMG takes 74.4s, and our proposed method (PGCP) takes 52.7s. PGCP shows the highest efficiency improvement.", "question": "Which method shows the highest efficiency improvement for the Nefertiti surface?", "answer": "So, the answer is {\"answer\": \"PGCP\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCEM with CMG is more efficient than CEM for the Buddha surface.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/460.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: The performance of the conformal energy minimization (CEM) [26] method, CEM combined with the Combinatorial Multigrid (CMG) [58], and our proposed PGCP method for disk conformal parameterization of simply-connected open surfaces." ], "reasoning": "For the Buddha surface, CEM takes 25.1s, while CEM with CMG takes 18.5s, indicating that CEM with CMG is more efficient.", "statement": "CEM with CMG is more efficient than CEM for the Buddha surface.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Job size distributions. The jobs are binned by their data sizes in our FB and CMU workloads'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/461.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Job size distributions. The jobs are binned by their data sizes in our FB and CMU workloads" ], "reasoning": "Table 3 shows the distribution of jobs by count, resources consumed, I/O generated, and task execution time, with a heavy-tailed distribution of input sizes.", "description": "Table 3 shows the distribution of jobs by count, cluster resources they consume, amount of I/O they generate, and aggregate task execution time. The jobs in both workloads exhibit a heavy-tailed distribution of input sizes," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Job size distributions. The jobs are binned by their data sizes in our FB and CMU workloads'\nProblem: 'Which data size bin consumes the highest percentage of resources in the CMU workload?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/461.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Job size distributions. The jobs are binned by their data sizes in our FB and CMU workloads" ], "reasoning": "To find the data size bin that consumes the highest percentage of resources in the CMU workload, look at the '% of Resources CMU' column and identify the highest value.", "question": "Which data size bin consumes the highest percentage of resources in the CMU workload?", "answer": "So, the answer is {\"answer\": \"A\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe data size bin B generates the highest percentage of I/O in the CMU workload.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/461.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Job size distributions. The jobs are binned by their data sizes in our FB and CMU workloads" ], "reasoning": "To verify this statement, look at the '% of I/O CMU' column and compare the values for each bin. Bin B has the highest value of 30.5%.", "statement": "The data size bin B generates the highest percentage of I/O in the CMU workload.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Performance results of the P-Gadget3 tests in three different code versions, indicated in the first column. The table reports the time to solution and speedup with respect to the original version for the whole application (second and third column, respectively) and for the subfind_density function (fourth and fifth column.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/462.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Performance results of the P-Gadget3 tests in three different code versions, indicated in the first column. The table reports the time to solution and speedup with respect to the original version for the whole application (second and third column, respectively) and for the subfind_density function (fourth and fifth column." ], "reasoning": "The tuned version of P-GADGET3 improves the time to solution by a factor of 1.2 to 1.3, while the optimized version adds no further speedup to the total timing.", "description": "The performance results of our tests on these three code versions for P-GADGET3 are summarized in Table III. In the tuned version the time to solution is improved by a factor of 1.2 − 1.3. The effect of the optimization (third line in Table III) adds no further speedup to the total timing of the code," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Performance results of the P-Gadget3 tests in three different code versions, indicated in the first column. The table reports the time to solution and speedup with respect to the original version for the whole application (second and third column, respectively) and for the subfind_density function (fourth and fifth column.'\nProblem: 'What is the total time improvement factor for the tuned version compared to the original version?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/462.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Performance results of the P-Gadget3 tests in three different code versions, indicated in the first column. The table reports the time to solution and speedup with respect to the original version for the whole application (second and third column, respectively) and for the subfind_density function (fourth and fifth column." ], "reasoning": "The table shows that the total time for the original version is 167.4 seconds, and the total time for the tuned version is 142.1 seconds. The speedup factor for the tuned version is 1.2x.", "question": "What is the total time improvement factor for the tuned version compared to the original version?", "answer": { "answer": "1.2x" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe optimized version of P-GADGET3 has a total speedup factor of 1.3x.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/462.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Performance results of the P-Gadget3 tests in three different code versions, indicated in the first column. The table reports the time to solution and speedup with respect to the original version for the whole application (second and third column, respectively) and for the subfind_density function (fourth and fifth column." ], "reasoning": "The table shows that the optimized version has a total speedup factor of 1.2x, not 1.3x.", "statement": "The optimized version of P-GADGET3 has a total speedup factor of 1.3x.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Execution testbed. All nodes equipped with Ubuntu Linux 18.04.2 LTS.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/463.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Execution testbed. All nodes equipped with Ubuntu Linux 18.04.2 LTS." ], "reasoning": "Table 1 shows the specifications of virtual machines used in Greece, Singapore, and the US, including vCPU cores, GHz, RAM, and provider.", "description": "As shown in Table 1 the PEU are running on virtual machines executed in different parts of the globe.. Tables 1–3 describe the simulation setup and the related network parameters.. In particular, as shown in Table 1, we used three hosts widely distributed around the globe, i.e. Greece, Singapore, and the US. The hosts have a different number of available vCPUs and RAM, as reported in the table.. the specifications of the virtual machines that have been used (Table 1)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Execution testbed. All nodes equipped with Ubuntu Linux 18.04.2 LTS.'\nProblem: 'Which virtual machine has the highest GHz and how much RAM does it have?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/463.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Execution testbed. All nodes equipped with Ubuntu Linux 18.04.2 LTS." ], "reasoning": "From the table, the virtual machine in Singapore (linode-SG) has the highest GHz of 2.8. It has 2 GB of RAM.", "question": "Which virtual machine has the highest GHz and how much RAM (GB)does it have?", "answer": "So, the answer is {\"answer\": \"2 GB\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe virtual machine in Greece has more vCPU cores than the one in California.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/463.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Execution testbed. All nodes equipped with Ubuntu Linux 18.04.2 LTS." ], "reasoning": "According to the table, the virtual machine in Greece (okeanos) has 4 vCPU cores, while the one in California (linode-US) has 1 vCPU core.", "statement": "The virtual machine in Greece has more vCPU cores than the one in California.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Transport sustainability responses for the two points of interest.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/464.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Transport sustainability responses for the two points of interest." ], "reasoning": "Test users predominantly chose sustainable transport means like walking and tram to visit Zurich Hauptbahnhof and ETH Zurich Hauptgebaude, with mean sustainability scores of 3.8 and 4.17 respectively.", "description": "Table II shows the choices of transport means made by each test user at each point of interest. Overall, none of the more unsustainable transport means, i.e. car, bus and train, are chosen by test users to visit the points of interest. Walking and tram are the most popular means given that ETH Zurich and the main train station are very well connected with tram and are in close proximity. The mean sustainability of 4.17 for ETH Zurich Hauptgeba¨ude is slightly higher than the one of 3.8 at Zurich Hauptbahnhof." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Transport sustainability responses for the two points of interest.'\nProblem: 'What is the most popular transport means chosen by test users to visit Zurich Hauptbahnhof?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/464.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Transport sustainability responses for the two points of interest." ], "reasoning": "By examining the table, we see that 'Tram' is chosen 4 times and 'Walking' is chosen 2 times for Zurich Hauptbahnhof. Therefore, 'Tram' is the most popular transport means.", "question": "What is the most popular transport means chosen by test users to visit Zurich Hauptbahnhof?", "answer": "So, the answer is {\"answer\": \"Tram\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTest users chose car as a transport means to visit Zurich Hauptbahnhof.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/464.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Transport sustainability responses for the two points of interest." ], "reasoning": "The table shows that the transport means chosen by test users to visit Zurich Hauptbahnhof are 'Walking', 'Tram', and 'Bike'. 'Car' is not listed as a chosen transport means.", "statement": "Test users chose car as a transport means to visit Zurich Hauptbahnhof.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Perceived cycling risk acquired via the Smart Agora app vs. the actual cycling risk calculated via an empirical model of real-world data [48] in the four urban spots of Figure 9. Users’ responses are in the range [1,5] with 1 for very safe and 5 for very dangerous.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/465.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Perceived cycling risk acquired via the Smart Agora app vs. the actual cycling risk calculated via an empirical model of real-world data [48] in the four urban spots of Figure 9. Users’ responses are in the range [1,5] with 1 for very safe and 5 for very dangerous." ], "reasoning": "The table compares perceived cycling risk values from 11 test users to actual baseline cycling risk values, showing high Pearson and Spearman correlations for both mean and median values.", "description": "Table III compares the perceived cycling risk values from 11 test users to the actual baseline cycling risk values. All test users cycled over the route on 12.12.2018 around 15:00 with the same provided bike to minimize biases originated from weather, light condition and the condition of different bikes. Correlation values are calculated using the mean and median value of the perceived cycling risk for each urban spot across all users. The Pearson correlation is 0.94 and 0.85 for the mean and median respectively, while the Spearman correlation is 1.0 for both mean and median." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Perceived cycling risk acquired via the Smart Agora app vs. the actual cycling risk calculated via an empirical model of real-world data [48] in the four urban spots of Figure 9. Users’ responses are in the range [1,5] with 1 for very safe and 5 for very dangerous.'\nProblem: 'What is the actual cycling risk value for Spot C?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/465.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Perceived cycling risk acquired via the Smart Agora app vs. the actual cycling risk calculated via an empirical model of real-world data [48] in the four urban spots of Figure 9. Users’ responses are in the range [1,5] with 1 for very safe and 5 for very dangerous." ], "reasoning": "To find the actual cycling risk value for Spot C, locate the row for Spot C and find the value in the 'Actual cycling risk' column.", "question": "What is the actual cycling risk value for Spot C?", "answer": "So, the answer is {\"answer\": \"6.21\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe perceived cycling risk mean value for Spot D is higher than the actual cycling risk value.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/465.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Perceived cycling risk acquired via the Smart Agora app vs. the actual cycling risk calculated via an empirical model of real-world data [48] in the four urban spots of Figure 9. Users’ responses are in the range [1,5] with 1 for very safe and 5 for very dangerous." ], "reasoning": "The table shows that the mean perceived cycling risk value for Spot D is 3.09, while the actual cycling risk value is 8.31. Therefore, the statement is refuted.", "statement": "The perceived cycling risk mean value for Spot D is higher than the actual cycling risk value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Numerical results'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/466.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Numerical results" ], "reasoning": "The table shows that using specialized encodings for specific environments can significantly reduce solution times compared to general implementations.", "description": "We report results for three different implementations in Table I.. The first implementation uses the encodings proposed in this paper. The second implementation is a special encoding that can only be used for 4-connected grid environments. That is, robots move in a two dimensional gridded environment only horizontally or vertically. In this implementation, the number of Boolean variables needed to denote the state of the robot on a x × y gridded environment is x + y as opposed to xy for a general implementation. A smaller number of decision variables decreases the solution times significantly. We also implement the continuous-state extension proposed in Section VI-A.. As can be seen in Table I, solution times can be reduced significantly if the encodings that are most appropriate for the problem at hand are used." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Numerical results'\nProblem: 'What is the solution time for the cLTL+ (grid) implementation when N is 10?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/466.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Numerical results" ], "reasoning": "To find the solution time for the cLTL+ (grid) implementation when N is 10, locate the row where N is 10 and then find the corresponding value under the cLTL+ (grid) column.", "question": "What is the solution time for the cLTL+ (grid) implementation when N is 10?", "answer": { "answer": "265.38" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe cLTL+ (regular) implementation has a lower solution time than the cLTL+ (continuous) implementation for N=8.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/466.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Numerical results" ], "reasoning": "For N=8, the solution time for cLTL+ (regular) is 1974.7, while for cLTL+ (continuous) it is 2286. Since 1974.7 is less than 2286, the statement is supported.", "statement": "The cLTL+ (regular) implementation has a lower solution time than the cLTL+ (continuous) implementation for N=8.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Numerical results'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/467.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Numerical results" ], "reasoning": "cLTL encodings scale better with the number of robots compared to cLTL+ encodings, which fail to find solutions for large numbers of robots within the timeout threshold.", "description": "Additionally, we examine the solution times for different encodings when specifications are given in cLTL and the. robots have identical dynamics.. The results in Table II are obtained by varying either the number of robots. = 10 or the time horizon h = 20 while keeping all the other parameters intact. Solution times in the first and second column are obtained by alternative cLTL encodings proposed in Section IV-F and regular cLTL+ encodings, respectively. Regular cLTL+ encodings could not find solutions for. = 500 within the timeout threshold of 60 minutes. On the other hand, cLTL encodings scale much better with the number of robots and easily handle hundreds of robots in a matter of seconds. In fact, solution times are almost unaffected by the number of robots." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Numerical results'\nProblem: 'What is the solution time for cLTL+ encoding when the number of robots is 50?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/467.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Numerical results" ], "reasoning": "The table shows the solution times for different encodings. For cLTL+ encoding with 50 robots, the solution time is listed in the second column.", "question": "What is the solution time for cLTL+ encoding when the number of robots is 50?", "answer": "So, the answer is {\"answer\": \"56.13\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\ncLTL+ encodings could not find solutions for 500 robots within the timeout threshold.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/467.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Numerical results" ], "reasoning": "The table indicates that for 500 robots, the cLTL+ encoding has a 'TO' (timeout) value, confirming that it could not find a solution within the threshold.", "statement": "cLTL+ encodings could not find solutions for 500 robots within the timeout threshold.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table II: Comparison of the Wall-Clock Times (WCTs) for solving the Quadratic Programs with and without the robust CBF formulation. The last entry is the duration during which the constraint was violated for each experiment.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/468.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table II: Comparison of the Wall-Clock Times (WCTs) for solving the Quadratic Programs with and without the robust CBF formulation. The last entry is the duration during which the constraint was violated for each experiment." ], "reasoning": "The robust CBF QP has a slightly higher wall-clock time and lower frequency compared to the non-robust CBF QP, but it encounters no collisions.", "description": "Focusing on the run time, solving the robust CBF QP averaged a wall-clock time of 4.5 ms, translating to a frequency of approximately 220 Hz, resulting in only a 25 Hz decrease compared to solving the non-robust CBF QP. The reason the frequency decreases slightly is that the only computation added is a min operation over p values, which is linear with respect to p (O(p)), where p is the number of points forming the convex hull of the disturbance. The comparison of the wall-clock times of both experiments is shown in Table II.. The robust CBF formulation (left) encounters 0 collisions whereas the non-robust CBF formulation (right) periodically encounters collisions as shown in Table II." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table II: Comparison of the Wall-Clock Times (WCTs) for solving the Quadratic Programs with and without the robust CBF formulation. The last entry is the duration during which the constraint was violated for each experiment.'\nProblem: 'What is the difference in average frequency between the robust and non-robust CBF formulations?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/468.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table II: Comparison of the Wall-Clock Times (WCTs) for solving the Quadratic Programs with and without the robust CBF formulation. The last entry is the duration during which the constraint was violated for each experiment." ], "reasoning": "The table shows that the average frequency for the robust CBF is 222 Hz and for the non-robust CBF is 247 Hz. The difference is 247 Hz - 222 Hz.", "question": "What is the difference in average frequency between the robust and non-robust CBF formulations?", "answer": "So, the answer is {\"answer\": \"25\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe robust CBF formulation has a higher average frequency than the non-robust CBF formulation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/468.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table II: Comparison of the Wall-Clock Times (WCTs) for solving the Quadratic Programs with and without the robust CBF formulation. The last entry is the duration during which the constraint was violated for each experiment." ], "reasoning": "The table shows that the average frequency for the robust CBF is 222 Hz, while for the non-robust CBF it is 247 Hz. Therefore, the statement is incorrect.", "statement": "The robust CBF formulation has a higher average frequency than the non-robust CBF formulation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Occurrence Matrix occurrence matrix of the finches on the Galapagos islands.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/469.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Occurrence Matrix occurrence matrix of the finches on the Galapagos islands." ], "reasoning": "The binary matrix represents the presence or absence of 13 species of finches across 17 islands, showing patterns of species co-occurrence and disjoint distributions.", "description": "much attention in numerical ecology. In ecological studies, the binary matrix is called occurrence matrix. Rows usually corresponds to species, the columns, to locations. For example, the binary matrix shown on Table 1 is known as \"Darwin's Finch\" dataset, which comes from Darwin's studies of the finches on the Galapagos islands (an archipelago in the East Pacific). The matrix represents the presence/absence of 13 species of finches in 17 islands. A \"1\" or \"0\" in entry (i, j) indicates the presence or absence of species i at island j. It is clear from Table 1 that some pairs of species tend to occur together (for example, species 9 and 10) while some other pairs tend to be disjoint. Therefore, it is of our interest to investigate whether the cooperation/competition influences the distribution of species on islands, or the patterns found are just by chance.. before each successful swap using 'Darwin's Finch' dataset. Assuming the randomly chosen row is mostly filled, such as row 1 in Table 1, the two random chosen entries in this row would most likely be [1, 1] but the row of a 'checkerboard unit' has to be either [1, 0] or [0, 1]. Therefore swapping rarely happens when the chosen row/column is mostly filled (or equivalently, mostly unfilled)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Occurrence Matrix occurrence matrix of the finches on the Galapagos islands.'\nProblem: 'Which species is present on the most number of islands?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/469.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Occurrence Matrix occurrence matrix of the finches on the Galapagos islands." ], "reasoning": "To determine which species is present on the most number of islands, count the number of '1's in each row. The species with the highest count is the one present on the most islands. Species 1 has the most '1's, appearing on 14 islands.", "question": "Which Finch is present on the most number of islands?", "answer": "So, the answer is {\"answer\": \"1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSpecies 9 and Species 10 tend to occur together on the islands.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/469.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Occurrence Matrix occurrence matrix of the finches on the Galapagos islands." ], "reasoning": "By examining the rows for Species 9 and Species 10, we see that they both have '1's in the same columns for Islands A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, and Q, indicating they tend to occur together.", "statement": "Species 9 and Species 10 tend to occur together on the islands.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: The comparison between swap algorithm and Rectangle Loop algorithm. Each algorithm is implemented 10000 iterations on 100×100 matrices with different filled portions. The third column records the number of successful swaps among the 10000 iterations, the last column records the average time per swap, respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/470.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The comparison between swap algorithm and Rectangle Loop algorithm. Each algorithm is implemented 10000 iterations on 100×100 matrices with different filled portions. The third column records the number of successful swaps among the 10000 iterations, the last column records the average time per swap, respectively." ], "reasoning": "The Rectangle Loop algorithm is significantly more efficient than the swap algorithm, especially for small filled portions, and maintains an advantage even when considering time per swap.", "description": "When the filled portion p is small, the Rectangle Loop algorithm is extremely efficient, producing more than 73 times more swaps than the swap algorithm. For large p, the advantage of Rectangle Loop algorithm is reduced, but still very significant. For p = 0.5, the Rectangle Loop still produces 4 times more swaps than the swap algorithm. Noteworthy, the zeros and ones play the symmetric rule in a binary matrix, therefore it is not necessary to generate the random matrix for p > 0.5. We also record the time per swap for both algorithms, as shown in the last column of Table 4. It turns out that the Rectangle Loop algorithm still has a significant advantage than the swap algorithm after the running time issue is taken into account. For p = 0.01, the Rectangle Loop is about 31 times more efficient than the swap algorithm. Even for p = 0.5, Rectangle Loop algorithm is still about 4 times more efficient than the swap algorithm." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: The comparison between swap algorithm and Rectangle Loop algorithm. Each algorithm is implemented 10000 iterations on 100×100 matrices with different filled portions. The third column records the number of successful swaps among the 10000 iterations, the last column records the average time per swap, respectively.'\nProblem: 'For a filled portion of 10%, how many more swaps does the Rectangle Loop algorithm produce compared to the swap algorithm?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/470.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The comparison between swap algorithm and Rectangle Loop algorithm. Each algorithm is implemented 10000 iterations on 100×100 matrices with different filled portions. The third column records the number of successful swaps among the 10000 iterations, the last column records the average time per swap, respectively." ], "reasoning": "For a filled portion of 10%, the Rectangle Loop algorithm produces 1838 swaps, while the swap algorithm produces 156 swaps. The difference is 1838 - 156 = 1682 swaps.", "question": "For a filled portion of 10%, how many more swaps does the Rectangle Loop algorithm produce compared to the swap algorithm?", "answer": "So, the answer is {\"answer\": \"1682\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFor a filled portion of 5%, the Rectangle Loop algorithm is more efficient than the swap algorithm when considering the time per swap.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/470.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The comparison between swap algorithm and Rectangle Loop algorithm. Each algorithm is implemented 10000 iterations on 100×100 matrices with different filled portions. The third column records the number of successful swaps among the 10000 iterations, the last column records the average time per swap, respectively." ], "reasoning": "For a filled portion of 5%, the Rectangle Loop algorithm has a time per swap of 5.30×10^-6 seconds, while the swap algorithm has a time per swap of 3.52×10^-5 seconds. The Rectangle Loop algorithm is more efficient.", "statement": "For a filled portion of 5%, the Rectangle Loop algorithm is more efficient than the swap algorithm when considering the time per swap.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Running time of our algorithm when vector length m and relative distance δ meets the Hamming bound and GV bound'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/471.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Running time of our algorithm when vector length m and relative distance δ meets the Hamming bound and GV bound" ], "reasoning": "The running time of the algorithm is subquadratic when δ is small, with specific exponents γ' listed for different δ values under the GV bound.", "description": "The running time of our algorithm depends on — in addition to the number of vectors n — both dimension m and δ := dmin/m. To illustrate its performance we choose two typical vector lengths m, namely those corresponding to the Hamming bound7 and the Gilbert-Varshamov (GV) bound8, and list the exponents γ(cid:48) in the running time of the GV-code version of our algorithm as a function of dmin (in fact δ) in Table 1. Here, we write the running of the algorithm as ˜O(nγ(cid:48) ), where ˜O suppresses any polylogarithmic factor of n. One can see that our algorithm runs in subquadratic time when δ is small, or equivalently when the Hamming distance between the closest pair is small. For instance, when δ = 0.05, and the length m = 1.4013 log n, then the running time is O(n1.3313) if we use GV bound." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Running time of our algorithm when vector length m and relative distance δ meets the Hamming bound and GV bound'\nProblem: 'What is the running time exponent γ' for δ = 0.075 under the GV bound?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/471.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Running time of our algorithm when vector length m and relative distance δ meets the Hamming bound and GV bound" ], "reasoning": "From the table, for δ = 0.075, the GV bound exponent γ' is listed as 1.5024.", "question": "What is the running time exponent γ' for δ = 0.075 under the GV bound?", "answer": "So, the answer is {\"answer\": \"1.5024\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe running time exponent γ' for δ = 0.125 under the GV bound is 1.9170.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/471.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Running time of our algorithm when vector length m and relative distance δ meets the Hamming bound and GV bound" ], "reasoning": "From the table, for δ = 0.125, the GV bound exponent γ' is listed as 1.9170.", "statement": "The running time exponent γ' for δ = 0.125 under the GV bound is 1.9170.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: All publications from WOS (2000-2017) and Scopus (2000-2017) that have an equivalent record in Unpaywall database (joined by DOIs)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/472.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: All publications from WOS (2000-2017) and Scopus (2000-2017) that have an equivalent record in Unpaywall database (joined by DOIs)" ], "reasoning": "Unpaywall has over 50% coverage for both WOS and Scopus articles and reviews, with WOS having slightly higher coverage.", "description": "Table 1 shows the number of articles and review papers from WOS and Scopus with an equivalent record in Unpaywall database. It presents also the total number of articles and review papers in WOS/Scopus to provide a baseline for comparison. Unpaywall has higher than 50% coverage in both cases while coverage of WOS is slightly higher (can be due to different indexing philosophy or DOIs completeness). In the following tables (in Unpaywall results), publications are limited to only articles and review papers published in 2000-2017." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: All publications from WOS (2000-2017) and Scopus (2000-2017) that have an equivalent record in Unpaywall database (joined by DOIs)'\nProblem: 'What is the total number of articles and review papers from Scopus that matched with Unpaywall?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/472.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: All publications from WOS (2000-2017) and Scopus (2000-2017) that have an equivalent record in Unpaywall database (joined by DOIs)" ], "reasoning": "The table shows that the number of Scopus articles and review papers that matched with Unpaywall is 14,188,983.", "question": "What is the total number of articles and review papers from Scopus that matched with Unpaywall?", "answer": "So, the answer is {\"answer\": \"14,188,983\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUnpaywall has a higher coverage percentage for WOS than for Scopus.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/472.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: All publications from WOS (2000-2017) and Scopus (2000-2017) that have an equivalent record in Unpaywall database (joined by DOIs)" ], "reasoning": "The table shows that the coverage percentage for WOS is 57.5%, while for Scopus it is 53.48%, indicating that Unpaywall has a higher coverage percentage for WOS.", "statement": "Unpaywall has a higher coverage percentage for WOS than for Scopus.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Number of licences per DOI found in Crossref'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/473.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Number of licences per DOI found in Crossref" ], "reasoning": "Approximately 50% of publications from WOS or Scopus with a matching DOI indexed in Crossref do not have a licence URL, and some DOIs have multiple licence records.", "description": "Table 2 presents a descriptive view on whether publications have licence information recorded in Crossref. It shows that about 50% of publications from WOS or Scopus with a matching DOI indexed in Crossref do not have a licence URL. Some of the publications had more than one licence information in Crossref (as an example, the number of DOIs that each have 6 licence records on Crossref are 7)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Number of licences per DOI found in Crossref'\nProblem: 'What percentage of DOIs have exactly one licence recorded in Crossref?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/473.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Number of licences per DOI found in Crossref" ], "reasoning": "The table shows the number of licences per DOI and their corresponding frequency and percentage. For DOIs with exactly one licence, the percentage is 44.28%.", "question": "What percentage of DOIs have exactly one licence recorded in Crossref?", "answer": "So, the answer is {\"answer\": \"44.28%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMore than 50% of DOIs have no licence information recorded in Crossref.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/473.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Number of licences per DOI found in Crossref" ], "reasoning": "The table shows that 51.41% of DOIs have no licence information recorded in Crossref, which is more than 50%.", "statement": "More than 50% of DOIs have no licence information recorded in Crossref.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: OA status comparison between Unpaywall and Crossref in WOS publications'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/474.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: OA status comparison between Unpaywall and Crossref in WOS publications" ], "reasoning": "The overall contradictory cases between Unpaywall and Crossref are around 27.95% in WOS and 27.57% in Scopus, partly due to Unpaywall's inclusion of green OA publications.", "description": "Tables 3 and 4 present the OA status comparison between Unpaywall and Crossref in WOS and Scopus publications, respectively.. Overall contradictory cases amount to 27.95% in WOS and 27.57% in Scopus which might partly be explained by the wider scope of Unpaywall including also green OA publications that might not be identified via license information only.. (22.98% in WOS and 22.91% in Scopus, these percentages are quite close to contradictions observed in the overall sample presented in Tables 3 and 4)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: OA status comparison between Unpaywall and Crossref in WOS publications'\nProblem: 'What is the percentage of publications that are Open Access in Unpaywall but Closed Access in Crossref?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/474.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: OA status comparison between Unpaywall and Crossref in WOS publications" ], "reasoning": "From the table, the frequency of publications that are Open Access in Unpaywall but Closed Access in Crossref is 1,363,525, which corresponds to 11.69%.", "question": "What is the percentage of publications that are Open Access in Unpaywall but Closed Access in Crossref?", "answer": "So, the answer is {\"answer\": \"11.69%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe percentage of publications that are Closed Access in both Unpaywall and Crossref is higher than 30%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/474.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: OA status comparison between Unpaywall and Crossref in WOS publications" ], "reasoning": "The table shows that the percentage of publications that are Closed Access in both Unpaywall and Crossref is 38.18%, which is higher than 30%.", "statement": "The percentage of publications that are Closed Access in both Unpaywall and Crossref is higher than 30%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Random sample OA status check on publications from WOS'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/475.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Random sample OA status check on publications from WOS" ], "reasoning": "The research found discrepancies between the accessibility of PDFs from publishers' websites and the OA status from Crossref and Unpaywall, with some Non-OA publications having accessible PDFs and some OA publications being inaccessible.", "description": "Tables 5 and 6 present the result of our research assistant's manual check for accessibility to article's PDF file from publishers websites compared to the respecitve licence in Crossref and the OA status we manually assignded to those URLs in contrast to OA status from Unpaywall. It is interesting to see there are publications defined as Non-OA while their PDF is accessible from the publisher (14.42% in WOS and 14.98% in Scopus) or vice versa, OA publications (based on either Unpaywall, Crossref or both) that are not accessible online. (17.57% in WOS and 16.74% in Scopus). Note also the contradictory cases between Crossref and Unpaywall, where metadata from one shows OA and the other Closed, which requires further probes" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Random sample OA status check on publications from WOS'\nProblem: 'What percentage of publications are defined as Non-OA but have accessible PDFs?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/475.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Random sample OA status check on publications from WOS" ], "reasoning": "To find the percentage of Non-OA publications with accessible PDFs, look for rows where 'PDF Manually accessible?' is 'PDF Accessible' and 'Licence status' is 'Closed Access'. Sum the percentages for these rows.", "question": "What percentage of publications are defined as Non-OA but have accessible PDFs?", "answer": "So, the answer is {\"answer\": \"6.31\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThere are more publications with accessible PDFs that are defined as Non-OA than those that are defined as OA.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/475.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Random sample OA status check on publications from WOS" ], "reasoning": "To verify this statement, compare the frequency of 'PDF Accessible' publications with 'Closed Access' (Non-OA) to those with 'Open Access' (OA). The frequencies are 14 for Non-OA and 104 for OA.", "statement": "There are more publications with accessible PDFs that are defined as Non-OA than those that are defined as OA.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: xPic experiment setup in the Cluster-Booster architecture evaluation measurements.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/476.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: xPic experiment setup in the Cluster-Booster architecture evaluation measurements." ], "reasoning": "The experimental setup involves running the particle solver on one Cluster node with 4096 cells per node and 2048 particles per cell, followed by using the same node for the field solver.", "description": "The experimental setup is summarized in table II.. running only on the Cluster means executing the particle solver on one Cluster node first and, once finished, using the same node for the field solver." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: xPic experiment setup in the Cluster-Booster architecture evaluation measurements.'\nProblem: 'What are the compilation flags used for the Cluster and Booster nodes?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/476.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: xPic experiment setup in the Cluster-Booster architecture evaluation measurements." ], "reasoning": "The table lists the compilation flags separately for the Cluster and Booster nodes. For the Cluster node, the flags are '-openmp' and '-mavx'. For the Booster node, the flag is '-xMIC-AVX512'.", "question": "What are the compilation flags used for the Cluster nodes?", "answer": "So, the answer is {\"answer\": \"-openmp, -mavx\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe number of particles per cell is 2048.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/476.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: xPic experiment setup in the Cluster-Booster architecture evaluation measurements." ], "reasoning": "The table explicitly states that the number of particles per cell is 2048.", "statement": "The number of particles per cell is 2048.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table I: Results on IOTA, with 60, 120, 240 buses.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/477.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table I: Results on IOTA, with 60, 120, 240 buses." ], "reasoning": "The 'Adaptive RTT' scheme has the lowest error rates and average latencies compared to 'Fixed Random' and 'Dynamic Random' schemes, making it the most efficient approach for supporting smart services in transportation systems.", "description": "The amount of errors is quite high, as well as the measured latencies. Thus, these tests seem to conclude that, at the time of writing, the IOTA DLT is not fully structured to support smart services for transportation systems. On the other hand, the good news is that if we carefully select the full node to issue a transaction, the performances definitely improve. In fact, our third scheme \"Adaptive RTT\" has a low amount of errors, on average around 0.8%. Measured latencies are lower than other approaches. Still, the average latency amounts to 23 seconds, which is far from a real-time update of the DLT.. charts further confirm the better performance obtained by the \"Adaptive RTT\" scheme. These. Results confirm that \"Adaptive RTT\" provides better results. Average latencies are definitely lower than other schemes.. In all cases, average latencies increase with the number of buses.. To better emphasize the outcomes, Table. reports some summarized statistics (shown in the box plots) and the error rates. Actually, the main difference on the performance of the approaches is on the amount of errors. While the average error for \"Adaptive RTT\" is ∼ 1%, for the other two schemes we have errors well above 15%. These error rates are clearly unacceptable, meaning that these approaches are unusable." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table I: Results on IOTA, with 60, 120, 240 buses.'\nProblem: 'What is the average latency for the 'Adaptive RTT' heuristic with 120 buses?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/477.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table I: Results on IOTA, with 60, 120, 240 buses." ], "reasoning": "To find the average latency for the 'Adaptive RTT' heuristic with 120 buses, locate the row with 120 buses and 'Adaptive RTT' heuristic. The average latency is listed in the 'Avg Latency' column.", "question": "What is the average latency for the 'Adaptive RTT' heuristic with 120 buses?", "answer": "So, the answer is {\"answer\": \"27.35 sec\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'Fixed Random' heuristic has the highest error rate for 240 buses.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/477.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table I: Results on IOTA, with 60, 120, 240 buses." ], "reasoning": "To verify this statement, compare the error rates for 240 buses across all heuristics. The 'Fixed Random' heuristic has an error rate of 42.81%, while 'Dynamic Random' has 44.85% and 'Adaptive RTT' has 7.55%.", "statement": "The 'Fixed Random' heuristic has the highest error rate for 240 buses.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table II: Preliminary results on Radix.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/478.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table II: Preliminary results on Radix." ], "reasoning": "The table reports the average latency, confidence interval, and error percentage for adding transactions on the Radix alphanet, averaged over 12 test repetitions, showing low latencies and a low error rate.", "description": "This gave us some preliminary results that we report in Table II. In the table, we show the average latency, confidence interval and error percentage to add transactions on this Radix alphanet. Results are averaged over an amount of 12 test repetitions. In this case, we obtained very low latencies (below 1 sec), with a non negligible (but low) error rate." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table II: Preliminary results on Radix.'\nProblem: 'What is the average latency and the error percentage for adding transactions on the Radix alphanet?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/478.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table II: Preliminary results on Radix." ], "reasoning": "The table shows that the average latency is 777.17 msec and the error percentage is 2.73%.", "question": "What is the error percentage for adding transactions on the Radix alphanet?", "answer": "So, the answer is {\"answer\": \"2.73%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average latency for adding transactions on the Radix alphanet is below 800 msec.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/478.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table II: Preliminary results on Radix." ], "reasoning": "The table shows that the average latency is 777.17 msec, which is below 800 msec.", "statement": "The average latency for adding transactions on the Radix alphanet is below 800 msec.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Zyzzyva’s [14] latencies (in ms) in a geo-scale deployment with primary at different locations. Columns indicate the primary’s location. Rows indicate average client-side latency for commands issued from that region. For example, the entry at the 4th row and the 3rd column shows the client-side latency for commands issued from India to the primary in Japan. Lowest latency per primary location is highlighted.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/479.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Zyzzyva’s [14] latencies (in ms) in a geo-scale deployment with primary at different locations. Columns indicate the primary’s location. Rows indicate average client-side latency for commands issued from that region. For example, the entry at the 4th row and the 3rd column shows the client-side latency for commands issued from India to the primary in Japan. Lowest latency per primary location is highlighted." ], "reasoning": "The lowest latencies are observed when the primary is colocated within the same region as the client.", "description": "To validate these hypotheses, we deployed Zyzzyva in a 4-replica geo-scale setting with nodes located in the US (Virginia), Japan, India, and Australia, using Amazon's EC2 infrastructure . We deployed clients alongside each replica to inject requests, and measured the client-side latencies by changing the primary's location. Table. shows the results. We observe that the lowest latencies are when the primary is colocated within the same region.. Zyzzyva's latencies (in ms) in a geo-scale deployment with primary at different locations. Columns indicate the primary's location. Rows indicate average client-side latency for commands issued from that region. For example, the entry at the 4th row and the 3rd column shows the clientside latency for commands issued from India to the primary in Japan. Lowest latency per primary location is highlighted." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Zyzzyva’s [14] latencies (in ms) in a geo-scale deployment with primary at different locations. Columns indicate the primary’s location. Rows indicate average client-side latency for commands issued from that region. For example, the entry at the 4th row and the 3rd column shows the client-side latency for commands issued from India to the primary in Japan. Lowest latency per primary location is highlighted.'\nProblem: 'What is the average client-side latency for commands issued from Japan to the primary in Virginia (US)?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/479.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Zyzzyva’s [14] latencies (in ms) in a geo-scale deployment with primary at different locations. Columns indicate the primary’s location. Rows indicate average client-side latency for commands issued from that region. For example, the entry at the 4th row and the 3rd column shows the client-side latency for commands issued from India to the primary in Japan. Lowest latency per primary location is highlighted." ], "reasoning": "To find the average client-side latency for commands issued from Japan to the primary in Virginia (US), locate the entry in the second row and the first column of the table.", "question": "What is the average client-side latency for commands issued from Japan to the primary in Virginia (US)?", "answer": "So, the answer is {\"answer\": \"236\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe lowest latency for commands issued from India is when the primary is in Japan.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/479.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Zyzzyva’s [14] latencies (in ms) in a geo-scale deployment with primary at different locations. Columns indicate the primary’s location. Rows indicate average client-side latency for commands issued from that region. For example, the entry at the 4th row and the 3rd column shows the client-side latency for commands issued from India to the primary in Japan. Lowest latency per primary location is highlighted." ], "reasoning": "To verify this statement, check the row for India and identify the lowest latency value. The lowest latency for India is 229 ms, which occurs when the primary is in Japan.", "statement": "The lowest latency for commands issued from India is when the primary is in Japan.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Comparison of existing BFT protocols and ezBFT.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/480.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Comparison of existing BFT protocols and ezBFT." ], "reasoning": "EZBFT and Zyzzyva both require 3 communication steps in the best case and tolerate f < n/3 faults, but EZBFT reduces latency by minimizing the first-step communication time and operates leaderlessly.", "description": "Zyzzyva , uses 3f + 1 nodes to solve consensus in three steps (including client communication), requiring a quorum of 3f +1 responses. The protocol tolerates f faults, so it takes an additional two steps when nodes are faulty. Zyzzyva uses the minimum number of nodes, communication steps, and one-to-one messages to achieve fast consensus. It is cheaper than the aforementioned protocols, but also more complex. Zyzzyva's performance boost is due to speculative execution, active participation of the clients in the agreement process, and tolerating temporary inconsistencies among replicas. EZBFT has the same node and quorum requirements as well as the number of communication steps as Zyzzyva. However, by minimizing the latency of the first communication step and alleviating the specialized role of the primary, EZBFT reduces the request processing latency. Aliph builds a BFT protocol by composing three different sub-protocols, each handling a specific system factor such as contention, slow links, and byzantine faults. Under zero contention, the sub-protocol Quorum can deliver agreement in two steps with 3f + 1 nodes by allowing clients to send the requests directly to the nodes. However, as contention or link latency increases, or as faults occur, Aliph switches to the subprotocol Chain whose additional steps is equal to the number of nodes in the system, or to the sub-protocol Backup which takes at least three steps.. EZBFT exploits the trade-off between the slow and fast path steps. EZBFT uses three steps compared to Aliph's two steps in the common case, and in return, provides slow path in only two extra communication steps unlike Aliph.. Table II summarizes the comparison of existing work with EZBFT. Note that EZBFT and Zyzzyva have the same bestcase communication steps. However, for EZBFT, the latency for the first-step communication is minuscule (tending towards zero) when compared to Zyzzyva's first-step latency." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Comparison of existing BFT protocols and ezBFT.'\nProblem: 'Which protocol has the highest number of best-case communication steps and what is its leader type?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/480.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Comparison of existing BFT protocols and ezBFT." ], "reasoning": "From the table, PBFT has the highest number of best-case communication steps, which is 5, and it operates with a single leader.", "question": "Which protocol has the highest number of best-case communication steps?", "answer": "So, the answer is {\"answer\": \"PBFT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAliph requires the least number of best-case communication steps among the protocols.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/480.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Comparison of existing BFT protocols and ezBFT." ], "reasoning": "The table shows that Aliph requires 2 best-case communication steps, which is the least among the protocols listed.", "statement": "Aliph requires the least number of best-case communication steps among the protocols.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Average runtimes of workflow parts in the ring scenario in client-only tests and using AoT addressing.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/481.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Average runtimes of workflow parts in the ring scenario in client-only tests and using AoT addressing." ], "reasoning": "The total execution time for the Client mode is about 12.52 seconds with a deviation of 300 ms, while the AoT mode takes about 34.15 seconds with a deviation of 400 ms.", "description": "Tables. and II show the average time needed for the parts of a workflow (the numbers in brackets show the standard deviation) in seconds. Table II indicates that the overall workflow time highly depends on the worker assignment in. As shown in Table I, the total execution time is about 12.52 seconds and is pretty stable with only about 300 ms deviation. Finally, the AoT mode needs about 34.15 seconds in total and is also stable with only about 400 ms deviation. Since in AoT mode a worker is always two hops away from the next hop, the transmission is even faster than using JiT mode with the best worker assignment." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Average runtimes of workflow parts in the ring scenario in client-only tests and using AoT addressing.'\nProblem: 'What is the difference in total execution time between Client mode and AoT mode?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/481.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Average runtimes of workflow parts in the ring scenario in client-only tests and using AoT addressing." ], "reasoning": "The total execution time for Client mode is 12.52 seconds and for AoT mode is 34.15 seconds. The difference is 34.15 - 12.52 = 21.63 seconds.", "question": "What is the difference in total execution time between Client mode and AoT mode?", "answer": "So, the answer is {\"answer\": \"21.63\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe transmission time in Client mode is faster than in AoT mode.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/481.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Average runtimes of workflow parts in the ring scenario in client-only tests and using AoT addressing." ], "reasoning": "The transmission time for Client mode is 0.87 seconds, while for AoT mode it is 20.44 seconds. Therefore, the transmission time in Client mode is indeed faster.", "statement": "The transmission time in Client mode is faster than in AoT mode.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Average runtimes of workflow parts in the ring scenario using JiT addressing and all four assignments.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/482.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Average runtimes of workflow parts in the ring scenario using JiT addressing and all four assignments." ], "reasoning": "The spread algorithm and the best worker algorithm have the lowest workflow times, but the spread algorithm has a more consistent performance with a lower standard deviation.", "description": "the JiT experiments. The recent worker assignment with an average of about 64.48 seconds requires the longest time, due to the long distance between the nodes, since their offers take longer to reach the client and thus arrive more recently. The standard deviation is also relatively high with more than 10 seconds, indicating long running tasks and differing results. The random worker assignment achieves better results with about 46.35 seconds on average and a deviation of 4.25 seconds, since closer workers are chosen. Always selecting the best available worker leads to significantly lower workflow times, requiring about 37.49 seconds, but with a standard deviation of 9.99 seconds. Finally, using the spread assignment algorithm, the workflow time does not significantly differ from the previous assignment algorithm, using about 37.94 seconds, but has a better standard deviation of 7.11 seconds. If all workers are equally capable, the workflow times using the best worker or the spread algorithm do not differ. This shows clearly that in terms of workflow time, the algorithm using the best workers and our spread approach outperform the other approaches. But since not all workers are equally capable in the different capability tests, tests using the best worker have a broader standard deviation, since the capable workers are further away in the topology. This means that always using the best worker is slightly faster than using the spread algorithm, but is more unpredictable in how long the execution of a workflow will take, since the very best workers will be worn up and worse workers have to be chosen consequently. Therefore, we propose our spread algorithm as the best available solution.. Overall, as previously shown in Table II, the workflow times are nearly as good as always selecting the best worker." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Average runtimes of workflow parts in the ring scenario using JiT addressing and all four assignments.'\nProblem: 'Which assignment algorithm has the highest average total workflow time and what is its standard deviation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/482.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Average runtimes of workflow parts in the ring scenario using JiT addressing and all four assignments." ], "reasoning": "The table shows that the 'Recent' assignment algorithm has the highest average total workflow time of 64.48 seconds with a standard deviation of 10.50 seconds.", "question": "Which assignment algorithm has the highest average total workflow time?", "answer": "So, the answer is {\"answer\": \"64.48\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe spread algorithm has a lower average total workflow time than the random worker assignment.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/482.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Average runtimes of workflow parts in the ring scenario using JiT addressing and all four assignments." ], "reasoning": "The table shows that the spread algorithm has an average total workflow time of 37.94 seconds, which is lower than the random worker assignment's average total workflow time of 46.35 seconds.", "statement": "The spread algorithm has a lower average total workflow time than the random worker assignment.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Average runtimes of tasks in mobile JiT scenarios in seconds.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/483.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Average runtimes of tasks in mobile JiT scenarios in seconds." ], "reasoning": "Using the spread algorithm results in better performance compared to random assignment, despite high standard deviation in transmission times due to node mobility and re-transmissions.", "description": "using our spread algorithm gives better results than random assignment and using. using a recent worker. Note that the transmission times (and thus also the total times) have a rather high standard deviation. This is due to the mobility of the nodes and potentially disappearing links between two nodes, resulting in re-transmissions. These increase the time, whereas many transmissions are successful within the first try, reducing the mean transmission time." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Average runtimes of tasks in mobile JiT scenarios in seconds.'\nProblem: 'Which assignment method has the lowest total time?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/483.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Average runtimes of tasks in mobile JiT scenarios in seconds." ], "reasoning": "By comparing the 'Total (s)' column, the 'Best' assignment method has the lowest total time of 149.6 seconds.", "question": "Which assignment method has the lowest total time?", "answer": "So, the answer is {\"answer\": \"Best\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe recent assignment method has the highest transmission time.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/483.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Average runtimes of tasks in mobile JiT scenarios in seconds." ], "reasoning": "By comparing the 'Transm. (s)' column, the 'Recent' assignment method has the highest transmission time of 269.0 seconds.", "statement": "The recent assignment method has the highest transmission time.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Average latency of sending and getting a message from the BSMD'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/484.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average latency of sending and getting a message from the BSMD" ], "reasoning": "The average latency of sending messages from the chief node is higher than from worker nodes due to batch sending, but the overall latency remains low, making it feasible for complex distributed models.", "description": "Table 2 shows the average latency of sending and receiving messages on BSMD for the dSA based estimation process. The latency of worker nodes remains under 0.05 secs, thus the response times are promising for implementing more complex distributed choice models over BSMD. In this particular case, the computational resources needed for running. the model on worker nodes are minimal.. The average latency of sending a massage from chief node is 0.149 secs, a bit higher than the workers. The increment is due to chief sending the information to the four workers in batches." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Average latency of sending and getting a message from the BSMD'\nProblem: 'What is the average latency of sending a message from worker 2 and how does it compare to the chief node?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/484.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average latency of sending and getting a message from the BSMD" ], "reasoning": "The average latency of sending a message from worker 2 is 0.037 seconds, which is significantly lower than the chief node's latency of 0.149 seconds.", "question": "What is the average latency of sending a message from worker 2?", "answer": "So, the answer is {\"answer\": \"0.037 seconds\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average latency of sending a message from worker 3 is higher than from worker 1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/484.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average latency of sending and getting a message from the BSMD" ], "reasoning": "The table shows that the average latency of sending a message from worker 3 is 0.036 seconds, which is equal to the latency from worker 1, not higher.", "statement": "The average latency of sending a message from worker 3 is higher than from worker 1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: The Mean and Std of Malytics and the baselines for Drebin Dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/485.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: The Mean and Std of Malytics and the baselines for Drebin Dataset." ], "reasoning": "Malytics outperforms all models on DexShare, which is more complicated than Drebin.", "description": "The performance of the models on DexShare is presented in table III.. It is to be expected that all models perform weaker on the dataset compared to Drebin, since the detaset is more complicated to deal with. Malytics again outperforms all models on DexShare." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: The Mean and Std of Malytics and the baselines for Drebin Dataset.'\nProblem: 'Which model has the highest f1-score on DexShare?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/485.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: The Mean and Std of Malytics and the baselines for Drebin Dataset." ], "reasoning": "By comparing the f1-scores of all models in the table, Malytics has the highest f1-score of 97.36%.", "question": "Which model has the highest f1-score on DexShare?", "answer": "So, the answer is {\"answer\": \"Malytics\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMalytics has the lowest FNR on DexShare.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/485.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: The Mean and Std of Malytics and the baselines for Drebin Dataset." ], "reasoning": "By comparing the FNR values of all models in the table, Malytics has the lowest FNR of 01.44%.", "statement": "Malytics has the lowest FNR on DexShare.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: The Mean and Std of Malytics and the baselines for DexShare Dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/486.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: The Mean and Std of Malytics and the baselines for DexShare Dataset." ], "reasoning": "Table IV shows the average FNR, precision, f1-score, accuracy, and FPR of Malytics with the proposed feature representation, highlighting FNR as the key measure for detecting new families.", "description": "Table IV provides more inside into Malytics.. Table IV shows the average FNR, precision, f1-score, accuracy and FPR of Malytics with our proposed feature representation. The main important index of the test is FNR as a measure that shows how well Malytics detected new families." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: The Mean and Std of Malytics and the baselines for DexShare Dataset.'\nProblem: 'Which model has the highest precision and what is its FNR?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/486.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: The Mean and Std of Malytics and the baselines for DexShare Dataset." ], "reasoning": "From the table, Malytics has the highest precision at 95.88%. The corresponding FNR for Malytics is 05.53%.", "question": "Which model has the highest precision and what is its FNR?", "answer": "So, the answer is {\"answer\": \"05.53%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMalytics has the lowest FNR among all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/486.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: The Mean and Std of Malytics and the baselines for DexShare Dataset." ], "reasoning": "The table shows that Malytics has an FNR of 05.53%, which is lower than the FNRs of all other models listed.", "statement": "Malytics has the lowest FNR among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: The Mean and Std of Malytics for DexShare dataset on the APK, Dex. Also, the results when the dataset is imbalanced and for zero-day (novel families) detection.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/487.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: The Mean and Std of Malytics for DexShare dataset on the APK, Dex. Also, the results when the dataset is imbalanced and for zero-day (novel families) detection." ], "reasoning": "Malytics shows improved false positive rates and hit-rates close to dense settings, with DexShare samples benefiting from richer hidden representations due to hashing.", "description": "Table V presents the results of the experiment on both datasets. Malytics False positive improves slightly while hit-rate is very close to dense setting (see table II). The size of hashing provides richer hidden representation for DexShare samples. In addition to being more precise, Malytics has better hit-rate (see table III). The imbalanced setting shows Malytics performance for real word application." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: The Mean and Std of Malytics for DexShare dataset on the APK, Dex. Also, the results when the dataset is imbalanced and for zero-day (novel families) detection.'\nProblem: 'Which model has the highest precision and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/487.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: The Mean and Std of Malytics for DexShare dataset on the APK, Dex. Also, the results when the dataset is imbalanced and for zero-day (novel families) detection." ], "reasoning": "By examining the table, the model 'MALytics (Dex, MBR=0.2)' has the highest precision value of 98.45%.", "question": "Which model has the highest precision and what is its value?", "answer": "So, the answer is {\"answer\": \"98.45%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMalytics (Zero-day) has the highest f1-score among all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/487.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: The Mean and Std of Malytics for DexShare dataset on the APK, Dex. Also, the results when the dataset is imbalanced and for zero-day (novel families) detection." ], "reasoning": "By examining the table, the model 'Malytics (Zero-day)' has an f1-score of 92.68%, which is not the highest. The highest f1-score is 96.55% by 'Malytics (Dex, MBR=0.2)'.", "statement": "Malytics (Zero-day) has the highest f1-score among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: The Mean and Std of Malytics with sparse tf-simashing for both Drebin and DexShare Datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/488.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: The Mean and Std of Malytics with sparse tf-simashing for both Drebin and DexShare Datasets." ], "reasoning": "Malytics and Wuechner models have higher precision and accuracy in distinguishing original Windows PE clean files from PE malware compared to other models.", "description": "Table VI shows that Malytics and Wuechner outperform other models when it comes to distinguishing original Windows PE clean files from PE malware." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: The Mean and Std of Malytics with sparse tf-simashing for both Drebin and DexShare Datasets.'\nProblem: 'Which dataset has the lowest False Positive Rate (FPR) and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/488.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: The Mean and Std of Malytics with sparse tf-simashing for both Drebin and DexShare Datasets." ], "reasoning": "By examining the FPR column, the dataset DexShare (MBR=0.2) has the lowest FPR value of 2.60%.", "question": "Which dataset has the lowest False Positive Rate (FPR) and what is its value?", "answer": { "answer": "DexShare (MBR=0.2)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDexShare (MBR=0.2) has the highest precision among the datasets.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/488.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: The Mean and Std of Malytics with sparse tf-simashing for both Drebin and DexShare Datasets." ], "reasoning": "The precision for DexShare (MBR=0.2) is 98.91%, which is higher than the precision values for Drebin (96.68%) and DexShare (96.69%).", "statement": "DexShare (MBR=0.2) has the highest precision among the datasets.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VI: The Mean and Std of Malytics and the baselines for WinPE and Mal2016 of the PEShare Dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/489.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: The Mean and Std of Malytics and the baselines for WinPE and Mal2016 of the PEShare Dataset." ], "reasoning": "The proposed solution, Malytics, outperforms other models in all evaluation indices, demonstrating the competitiveness of deep learning models in the malware application domain.", "description": "Table VII shows that the proposed solution outperforms other models over all evaluation indices.. An interesting result of trying to distinguish Mal2016 malware set from WinAppPE benign set is in the comparison of DNN with Raff . Raff used deep CNN for detection. The results show deep learning models also can be competitive for malware application domain." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VI: The Mean and Std of Malytics and the baselines for WinPE and Mal2016 of the PEShare Dataset.'\nProblem: 'Which model has the highest f1-score and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/489.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: The Mean and Std of Malytics and the baselines for WinPE and Mal2016 of the PEShare Dataset." ], "reasoning": "To find the model with the highest f1-score, compare the f1-score values of all models. Malytics has the highest f1-score of 99.70%.", "question": "Which model has the highest f1-score?", "answer": "So, the answer is {\"answer\": \"Malytics\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMalytics has the lowest False Negative Rate (FNR) among all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/489.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: The Mean and Std of Malytics and the baselines for WinPE and Mal2016 of the PEShare Dataset." ], "reasoning": "By comparing the FNR values of all models, Malytics has the lowest FNR of 0.55%.", "statement": "Malytics has the lowest False Negative Rate (FNR) among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table I: Single sample classification accuracy for each subject in classifying between IC and INC states. Comparison between the proposed entropy-based method and the PSD-based method taken from [17].'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/490.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table I: Single sample classification accuracy for each subject in classifying between IC and INC states. Comparison between the proposed entropy-based method and the PSD-based method taken from [17]." ], "reasoning": "The entropy-based BCI achieved better classification accuracy for 7 out of 10 subjects compared to the PSD-based BCI, with an average improvement of 3.3% ± 5.5%, although not statistically significant.", "description": "Table. shows the performance for each subject of the proposed entropy-based BCI in detecting periods of movement execution (IC) from periods of resting (INC).. From the results, our system achieved comparable performance and better classification accuracy for 7 out of 10 subjects, with an average improve of 3.3% ± 5.5%, even if not statistically significant (t-test, p>0.05).. The group of subjects belonging to the 'FPc' configuration shows on average slightly better performances compared to subjects wearing the 'Mc' configuration, with classification accuracy of 82.0% ± 4.6% and 77.0% ± 4.4% respectively for the entropy-based BCI, and 79.0% ± 3.8% and 73.0% ± 9.5% respectively for the PSD-based BCI, but with no statistically significant difference (t-test, p>0.05).. the entropy provides different additional information to motion detection respect to the spectrogram analysis, that could improve the identification of self-paced motion intention, as shown in Table I." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table I: Single sample classification accuracy for each subject in classifying between IC and INC states. Comparison between the proposed entropy-based method and the PSD-based method taken from [17].'\nProblem: 'What is the average classification accuracy for subjects wearing the 'FPc' configuration using the entropy-based BCI?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/490.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table I: Single sample classification accuracy for each subject in classifying between IC and INC states. Comparison between the proposed entropy-based method and the PSD-based method taken from [17]." ], "reasoning": "The table shows the classification accuracy for each subject. To find the average for the 'FPc' configuration using the entropy-based BCI, we look at the values for s01, s02, s03, s08, and s09, which are 0.83, 0.74, 0.83, 0.85, and 0.85 respectively. The average is given as 0.82.", "question": "What is the average classification accuracy for subjects wearing the 'FPc' configuration using the entropy-based BCI?", "answer": "So, the answer is {\"answer\": \"0.82\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe entropy-based BCI has a higher average classification accuracy than the PSD-based BCI for subjects wearing the 'Mc' configuration.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/490.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table I: Single sample classification accuracy for each subject in classifying between IC and INC states. Comparison between the proposed entropy-based method and the PSD-based method taken from [17]." ], "reasoning": "The table shows that the average classification accuracy for the entropy-based BCI for subjects wearing the 'Mc' configuration is 0.77, while for the PSD-based BCI it is 0.73. Therefore, the statement is supported.", "statement": "The entropy-based BCI has a higher average classification accuracy than the PSD-based BCI for subjects wearing the 'Mc' configuration.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table II: Classification performance for each subject in classifying between INC and IC-cue states.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/491.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table II: Classification performance for each subject in classifying between INC and IC-cue states." ], "reasoning": "The proposed entropy-based BCI system achieves an overall classification accuracy of 74% ± 5% and can predict motion intention with a single trial accuracy of 76% ± 11% in 1.7 ± 0.7 seconds on average.", "description": "the proposed entropy-based BCI has been trained to classify between INC state and IC-cue state.. The system provides an overall classification accuracy of 74% ± 5% with maximum performance of 82% for s04. The single trial classification performance are measured as the accuracy and the delay in predicting motion intention after the appearance of the ICcue.. The proposed BCI is able to predict the intention of performing a motion with a single trial accuracy of 76% ± 11% in 1.7 ± 0.7 seconds on average. On average, 15% ± 6% of the trials are missed, while less than 9% are classified before the cue, showing the robustness of the system to missclassifications.. Results in Table II shows acceptable classification accuracy of movement preparation respect to resting phases. the proposed entropy-based BCI is able to predict motion intention also when no muscle activity is generated." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table II: Classification performance for each subject in classifying between INC and IC-cue states.'\nProblem: 'What is the single trial accuracy for s04 and how does it compare to the average single trial accuracy?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/491.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table II: Classification performance for each subject in classifying between INC and IC-cue states." ], "reasoning": "The single trial accuracy for s04 is 0.95. The average single trial accuracy is 0.76 ± 0.11. Comparing these values, s04's single trial accuracy is higher than the average.", "question": "What is the single trial accuracy for s04 and how does it compare to the average single trial accuracy?", "answer": "So, the answer is {\"answer\": \"0.95\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe delay for s08 is higher than the average delay.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/491.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table II: Classification performance for each subject in classifying between INC and IC-cue states." ], "reasoning": "The delay for s08 is 2.6 ± 0.6 seconds, while the average delay is 1.7 ± 0.7 seconds. Since 2.6 is greater than 1.7, the statement is supported.", "statement": "The delay for s08 is higher than the average delay.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Evaluative test results in AirSim.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/492.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Evaluative test results in AirSim." ], "reasoning": "The agent performs well in the first three environments with a 0% crash rate and close to optimal navigation time. In the last three environments, the agent achieves an 87% success rate in reaching the goal and an 86% collision-free flight rate.", "description": "Table. states the values for reward, navigation distance, navigation time, and crash rate.. In the first three environments, the agent is able to learn a policy which is close to the optimal. Successful navigation from the start in these relatively simple environments. The navigation time performance is also desirable as being close to 60s in 1m/s is observed for each trial to the goal setting.. The crash rate is 0% in these environments, even for the narrow corridor in Env. 3.. Still, the agent successfully reaches the goal position in 67% of the trials. In terms of safety, the agent completes 73% of the trials without a crash in these dense environments.. On the other hand, there are some trials such as the third one in Env. 4 in which the agent cannot reach the goal position but does not take decisions which might yield a crash either.. In the last three environments, the agent yields better performance as compared to the former group. It successfully reaches the goal position in 87% of the trials. The other 13% stands for two trials in Env. 8. The agent yields fairly safe flights with collision-free flight percentage of 86% over the trials in AirSim.. It reaches the goal point successfully during 88% of these safe flights in AirSim" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Evaluative test results in AirSim.'\nProblem: 'What is the average total reward in Env. 1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/492.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Evaluative test results in AirSim." ], "reasoning": "To find the average total reward in Env. 1, sum all the total rewards for Env. 1 and divide by the number of trials. The total rewards are 12.15, 11.89, 10.96, 12.66, 11.51, and 10.94. The sum is 70.11, and there are 6 trials. The average is 70.11 / 6 = 11.685.", "question": "What is the average total reward in Env. 1?", "answer": "So, the answer is {\"answer\": \"11.685\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe agent has a 0% crash rate in Env. 3.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/492.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Evaluative test results in AirSim." ], "reasoning": "The table shows that in Env. 3, the agent did not crash in any of the trials, as indicated by 'N' in the Crash column for all entries of Env. 3.", "statement": "The agent has a 0% crash rate in Env. 3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Evaluative test results in real flights.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/493.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Evaluative test results in real flights." ], "reasoning": "In the first environment, the agent successfully reached the goal point in all five trials without crashing, while in the second environment, the agent had a crash in one out of five trials and the furthest navigation point was 1.55m.", "description": "Table II summarizes the real flight test results.. In the first environment without obstacles, the agent's performance is desirable. There is no single crash and the agent reaches to the goal point with 100% success out of five trials as can be seen in Table II. The navigation time is relatively high as compared to the results in AirSim though.. Still, the agent yields adequate end-to-end reasoning and completes the task with 100% accuracy in the first environment.. It cannot reach the goal position out of five trials. The furthest navigation point on the initial rough path is recorded as 1.55m in the third trial as can be seen in Table II. On the other hand, the agent yields safe flights for 80% of the trials in this environment.. This number is 0% for real flights with relatively simpler environments.. while this percentage is 56% for real flights." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Evaluative test results in real flights.'\nProblem: 'What is the total reward for the trial where the agent navigated the furthest distance in the second environment?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/493.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Evaluative test results in real flights." ], "reasoning": "The furthest navigation distance in the second environment is 1.55m, which occurred in the third trial. The total reward for this trial is 0.32.", "question": "What is the total reward for the trial where the agent navigated the furthest distance in the second environment?", "answer": "So, the answer is {\"answer\": \"0.32\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe agent crashed in the second environment during the trial with the shortest navigation distance.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/493.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Evaluative test results in real flights." ], "reasoning": "In the second environment, the shortest navigation distance is 0.65m, and the agent did not crash during this trial as indicated by 'N' in the Crash column.", "statement": "The agent crashed in the second environment during the trial with the shortest navigation distance.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Hyper-parameters for Growing Neural Gas Clustering.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/494.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Hyper-parameters for Growing Neural Gas Clustering." ], "reasoning": "GNG does not require a predefined number of nodes, unlike k-means clustering, making it suitable for unknown input distributions.", "description": "It is because of GNGs incremental nature that it is not necessary to decide on the number of nodes to use a priori, unlike the k-means clustering algorithm, where several trials may be required to determine an appropriate number of centres. GNG is, therefore, suitable for problems where we know nothing or little about the input distribution, or the cases in which deciding on network size and decaying parameters are very difficult or impossible." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Hyper-parameters for Growing Neural Gas Clustering.'\nProblem: 'What is the maximum number of nodes allowed in Domain State, and how does it compare to Device Mode?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/494.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Hyper-parameters for Growing Neural Gas Clustering." ], "reasoning": "The table shows that the maximum number of nodes in Domain State is 20000, while in Device Mode it is 10000. Comparing these values, Domain State allows twice as many nodes as Device Mode.", "question": "What is the maximum number of nodes allowed in Domain State, and how does it compare to Device Mode?", "answer": "So, the answer is {\"answer\": \"20000\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe decay rate after split is higher in Device Mode than in Domain State.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/494.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Hyper-parameters for Growing Neural Gas Clustering." ], "reasoning": "The table shows that the decay rate after split is 0.5 in Device Mode and 0.3 in Domain State, indicating that the decay rate after split is indeed higher in Device Mode.", "statement": "The decay rate after split is higher in Device Mode than in Domain State.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Comparison between different methods.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/495.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Comparison between different methods." ], "reasoning": "The adaptive configuration of smart devices achieves a 30% energy saving, outperforming Byun et al.'s 22% by considering user behavior and preferences.", "description": "In Table II, we show how the adaptive configuration of smart devices is quantitatively better than the other existing techniques. The mentioned methods are the closest to our problem statement, however, not exact.. As explained in Section II, Byun et. al. do not perceive user behavior to the extent as our method does, while Adika et. al. do not take user preference into account at all , . Their result shows around 22% energy saving as opposed to a much better 30% energy saving achieved by our method. Hence, our work justifies which states are compromised to obtain energy efficiency, and performs better at this task while considering user device setting preferences against other methods." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Comparison between different methods.'\nProblem: 'Which method achieves the highest energy saving while considering user behavior?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/495.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Comparison between different methods." ], "reasoning": "The table shows that 'Making Smart Home Smarter: Optimizing Energy Consumption with Human in the Loop' achieves the highest energy saving of 30% and considers user behavior.", "question": "Which method achieves the highest energy saving while considering user behavior?", "answer": "So, the answer is {\"answer\": \"Making Smart Home Smarter: Optimizing Energy Consumption with Human in the Loop\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe method 'Intelligent Household LED Lighting System Considering Energy Efficiency and User Satisfaction' does not consider user behavior.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/495.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Comparison between different methods." ], "reasoning": "The table indicates that the 'Intelligent Household LED Lighting System Considering Energy Efficiency and User Satisfaction' method does consider user behavior.", "statement": "The method 'Intelligent Household LED Lighting System Considering Energy Efficiency and User Satisfaction' does not consider user behavior.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: Rendering Results of the Hospital Game Generated by the Scenario-Based Video Game Generator vs. Hospital Game'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/496.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Rendering Results of the Hospital Game Generated by the Scenario-Based Video Game Generator vs. Hospital Game" ], "reasoning": "The rendering profile uses parameters such as SetPass Calls, Draw Calls, Total Batches, Triangles, and Vertices to measure performance.", "description": "The rendering profile used the SetPass Calls, Draw Calls, Total Batches, Triangles and Vertices as parameters. SetPass parameter is defined as \"the number of rendering passes\" , a Draw Call as a \"call to the graphics API to draw objects\" and Batch as a \"package with data that will be sent to the GPU\" on the Unity's Renderer Profiler page ." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: Rendering Results of the Hospital Game Generated by the Scenario-Based Video Game Generator vs. Hospital Game'\nProblem: 'What is the difference in the number of Draw Calls between the Hospital (Generator) and Hospital Game?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/496.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Rendering Results of the Hospital Game Generated by the Scenario-Based Video Game Generator vs. Hospital Game" ], "reasoning": "To find the difference in Draw Calls, subtract the number of Draw Calls for Hospital (Generator) from the number of Draw Calls for Hospital Game: 298 - 252.", "question": "What is the difference in the number of Draw Calls between the Hospital (Generator) and Hospital Game?", "answer": "So, the answer is {\"answer\": \"46\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Hospital Game has more SetPass Calls than the Hospital (Generator).\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/496.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Rendering Results of the Hospital Game Generated by the Scenario-Based Video Game Generator vs. Hospital Game" ], "reasoning": "The table shows that the Hospital Game has 136 SetPass Calls, while the Hospital (Generator) has 106 SetPass Calls, indicating that the Hospital Game has more SetPass Calls.", "statement": "The Hospital Game has more SetPass Calls than the Hospital (Generator).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Summary of costs for running the IMDfence protocol on an IMD'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/497.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Summary of costs for running the IMDfence protocol on an IMD" ], "reasoning": "IMDfence increases energy requirements significantly for a basic session, but the total daily IMD consumption increases only slightly due to the dominance of basic medical functionality.", "description": "Table IV summarizes the impact of IMDfence on an IMD in terms of energy, performance and program-memory footprint. For the hardware implementation of IMDfence it can be observed that, although the energy requirements increase by more than 6 times for a basic session, the total daily IMD consumption (that includes a two-minute communication session and electrical-stimulation costs) increases from 16.60 J to just 17.37 J, which amounts to a mere 4.64% increase, as previously shown in Fig. 12. The reason for this small increase is that the basic medical functionality, e.g., the continuous electrical stimulation of a pacemaker, dominates the security provisions since the reader accesses are far less frequent. In the case of software (AES-128) implementation of IMDfence, the total daily IMD consumption increases by 13.01% (as shown in Fig. 12). Moreover, there is a minimal increase in the computational delay and required program-memory size. In the context of current MCU technology, 6–7 kB of additional footprint is negligible. Hence, we conclude that there is no noticeable change in the IMD costs when IMDfence is employed." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Summary of costs for running the IMDfence protocol on an IMD'\nProblem: 'What is the percentage increase in total daily IMD consumption when using IMDfence (H/W) compared to without security?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/497.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Summary of costs for running the IMDfence protocol on an IMD" ], "reasoning": "The total daily IMD consumption without security is 16.60 J, and with IMDfence (H/W) it is 17.69 J. The percentage increase is calculated as ((17.69 - 16.60) / 16.60) * 100.", "question": "What is the percentage increase in total daily IMD consumption when using IMDfence (H/W) compared to without security?", "answer": "So, the answer is {\"answer\": \"6.57%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe program-memory footprint increases significantly when using IMDfence (H/W) compared to without security.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/497.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Summary of costs for running the IMDfence protocol on an IMD" ], "reasoning": "The program-memory footprint without security is ~1.00 kB, and with IMDfence (H/W) it is 6.86 kB. This is a significant increase.", "statement": "The program-memory footprint increases significantly when using IMDfence (H/W) compared to without security.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Details of the information for sources found in tables 2, 3 and 4. We generally use green-to-purple for good-to-poor reliability/credibility, with grey as inconclusive. For bias we use blue-to-red for left-to-right bias, with grey as unbiased. Orange is used for special cases. In NewsGuard data it represents missing information, in Open Sources it marks auxiliary labels and for Media Bias / Fact Check it marks satire.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/498.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Details of the information for sources found in tables 2, 3 and 4. We generally use green-to-purple for good-to-poor reliability/credibility, with grey as inconclusive. For bias we use blue-to-red for left-to-right bias, with grey as unbiased. Orange is used for special cases. In NewsGuard data it represents missing information, in Open Sources it marks auxiliary labels and for Media Bias / Fact Check it marks satire." ], "reasoning": "The table provides detailed descriptions of various assessment sites, their granular labels, and overall credibility scores, which are used to determine the trustworthiness and bias of news sources.", "description": "These assessment sites are: 1. NewsGuard 2. Pew Research Center 3. Wikipedia 4. OpenSources 5. Media Bias/Fact Check (MBFC) 6. AllSides 7. BuzzFeed News 8. Politifact. Table 1 provide a detailed described of each assessment. Their analysis produces 9 granular, binary labels for each site, with assigned point scores that sums to 100. Based on the sum of points the sites get an overall label for credibility - green for good score, red for bad score. Three additional overall labels exist for satire, user-produced content and sites with unfinished analysis. Table 1 describes the granular labels.. conservatives. This analysis used 5 groups of people, ranging from liberals to conservatives, and each group provided a rating of how much they trust each source. The ratings are aggregated to show whether readers with different political leanings predominantly trust or distrust a specific source. We provide this trust label for each source and political leaning, as a label for congruency between bias a readership (rather than a factchecking label).. We provide a fake-news tag for sources on the list.. The site provides a list of sources with 1-3 tags per source (See Table 1).. We were able to find a factual reporting label, which is derived from the previously mentioned scores. Many sources also had descriptive labels, some of which were related to reliability and some of which were related to bias. All these labels are described in Table 1.. We include their bias label and feedback numbers (votes agreeing and votes disagreeing) for each source. The feedback number are not shown in the paper, but can be found in the dataset.. They publish an associated dataset with news sources and their political leaning (left and right), which we include.. We have counted the types of statements coming from each source, which could be used to indicate their truthfulness.. We include the position of the sources in this rating in the dataset based on our access to Alexa on 13th of January 2019." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Details of the information for sources found in tables 2, 3 and 4. We generally use green-to-purple for good-to-poor reliability/credibility, with grey as inconclusive. For bias we use blue-to-red for left-to-right bias, with grey as unbiased. Orange is used for special cases. In NewsGuard data it represents missing information, in Open Sources it marks auxiliary labels and for Media Bias / Fact Check it marks satire.'\nProblem: 'Which assessment site provides a factual reporting label ranging from 5 (good) to 1 (bad)?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/498.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Details of the information for sources found in tables 2, 3 and 4. We generally use green-to-purple for good-to-poor reliability/credibility, with grey as inconclusive. For bias we use blue-to-red for left-to-right bias, with grey as unbiased. Orange is used for special cases. In NewsGuard data it represents missing information, in Open Sources it marks auxiliary labels and for Media Bias / Fact Check it marks satire." ], "reasoning": "By examining the table, we can see that Media Bias/Fact Check provides a factual reporting label ranging from 5 (good) to 1 (bad).", "question": "Which assessment site provides a factual reporting label ranging from 5 (good) to 1 (bad)?", "answer": "So, the answer is {\"answer\": \"Media Bias/Fact Check\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPew Research Center provides a special label for conspiracy, pseudoscience, or questionable sources.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/498.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Details of the information for sources found in tables 2, 3 and 4. We generally use green-to-purple for good-to-poor reliability/credibility, with grey as inconclusive. For bias we use blue-to-red for left-to-right bias, with grey as unbiased. Orange is used for special cases. In NewsGuard data it represents missing information, in Open Sources it marks auxiliary labels and for Media Bias / Fact Check it marks satire." ], "reasoning": "The table shows that Media Bias/Fact Check, not Pew Research Center, provides a special label for conspiracy, pseudoscience, or questionable sources.", "statement": "Pew Research Center provides a special label for conspiracy, pseudoscience, or questionable sources.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table I: Normal form of the game in Section III with modified payoffs.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/499.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table I: Normal form of the game in Section III with modified payoffs." ], "reasoning": "The unique Nash Equilibrium (NE) of the game is yield/go, as no player benefits from changing their strategy, ensuring cyclists yield and AVs go.", "description": "For cyclists, the worst cases are when they cycle (−600) or walk (−500), respectively, and in both cases the AV drives; the values are lower than the previous ones because in this new setup the cyclist is also partially responsible for the accent. Both walk/stop and cycle/stop have been penalized with a fine of 15, which is assumed to be a certain punishment and it is common knowledge: then yield/stop (15) and yield/go (18) are the right solutions according to the new street regulations. For the AV, the worst outcomes are to hit the pedestrian (−400) or the cyclist (−200). If they both yield (4), they need to renegotiate the solution. Then walk/stop and cycle/stop (5) are the next worst outcomes because the AV had the right to pass. The best outcome is achieved when they play yield/go (10).. The resulting game in normal form is represented by Table I. The unique NE of the game is yield/go since it is immediate to. see that no player has anything to gain by changing only their own strategy, therefore in a fully autonomous environment the cyclists will yield and the AVs will go, thus validating the effectiveness of the changes." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table I: Normal form of the game in Section III with modified payoffs.'\nProblem: 'What is the outcome for the cyclist and AV when the cyclist yields and the AV goes?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/499.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table I: Normal form of the game in Section III with modified payoffs." ], "reasoning": "According to the table, when the cyclist yields and the AV goes, the outcome is represented by the values in the 'Yield' row and 'Go' column, which are 18 for the cyclist and 10 for the AV.", "question": "What is the outcome for the cyclist and AV when the cyclist yields and the AV goes?", "answer": "So, the answer is {\"answer\": \"18,10\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe worst outcome for the AV is when the cyclist cycles and the AV goes.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/499.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table I: Normal form of the game in Section III with modified payoffs." ], "reasoning": "The table shows that the worst outcome for the AV is when the AV hits the pedestrian (-400) or the cyclist (-200). When the cyclist cycles and the AV goes, the outcome is -600 for the cyclist and -200 for the AV, which is not the worst outcome for the AV.", "statement": "The worst outcome for the AV is when the cyclist cycles and the AV goes.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Number of scripts found using heuristics from (a) Englehardt & Narayanan [12] and (b) Das et al. [30]'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/500.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Number of scripts found using heuristics from (a) Englehardt & Narayanan [12] and (b) Das et al. [30]" ], "reasoning": "The table shows the Jaccard similarity between flagged scripts for different implementations, highlighting that Canvas Font and WebRTC have the highest similarity, while Audio has the lowest.", "description": "However, small implementation changes result in differences in the flagged scripts. We summarize this in Table 3 using the Jaccard similarity—the size of the intersection of two sets relative to the size of the union of those sets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Number of scripts found using heuristics from (a) Englehardt & Narayanan [12] and (b) Das et al. [30]'\nProblem: 'Which implementation has the lowest Jaccard similarity between flagged scripts?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/500.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Number of scripts found using heuristics from (a) Englehardt & Narayanan [12] and (b) Das et al. [30]" ], "reasoning": "By comparing the similarity percentages in the table, it is evident that Audio has the lowest Jaccard similarity at 31.34%.", "question": "Which implementation has the lowest Jaccard similarity between flagged scripts?", "answer": "So, the answer is {\"answer\": \"Audio\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Canvas Font implementation has a Jaccard similarity of 100%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/500.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Number of scripts found using heuristics from (a) Englehardt & Narayanan [12] and (b) Das et al. [30]" ], "reasoning": "The table shows that the Canvas Font implementation has a Jaccard similarity of 100%, confirming the statement.", "statement": "The Canvas Font implementation has a Jaccard similarity of 100%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Validation summary of scripts grouped at selected score thresholds. Uncharacterized scripts from the 12,000 score group were manually reviewed.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/501.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Validation summary of scripts grouped at selected score thresholds. Uncharacterized scripts from the 12,000 score group were manually reviewed." ], "reasoning": "Of the scripts given a score of 12,000 or higher, 94.9% are identified as fingerprinting by the heuristic list, which increases to 98.9% at a score of 6,000.", "description": "The results summarized in Table 4.. Of the 7,915 scripts given a score of 12,000 or higher by the model, 5,722 (94.9%) are fingerprinting as identified by the heuristic list. This increases to 5,962 (98.9%) at a score of 6,000." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Validation summary of scripts grouped at selected score thresholds. Uncharacterized scripts from the 12,000 score group were manually reviewed.'\nProblem: 'What percentage of scripts with a score of 10,000 are identified as fingerprinting by the heuristic list?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/501.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Validation summary of scripts grouped at selected score thresholds. Uncharacterized scripts from the 12,000 score group were manually reviewed." ], "reasoning": "The table shows that 5,875 scripts with a score of 10,000 are in the heuristic list, which is 97.5% of the heuristic list.", "question": "What percentage of scripts with a score of 10,000 are identified as fingerprinting by the heuristic list?", "answer": { "answer": "97.5%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAt a score of 14,000, 24.5% of scripts are identified as fingerprinting by the heuristic list.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/501.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Validation summary of scripts grouped at selected score thresholds. Uncharacterized scripts from the 12,000 score group were manually reviewed." ], "reasoning": "The table shows that at a score of 14,000, 1,476 scripts are in the heuristic list, which is 24.5% of the heuristic list.", "statement": "At a score of 14,000, 24.5% of scripts are identified as fingerprinting by the heuristic list.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Validation Table - Summary of scripts flagged by keyword “fingerprint” grouped at selected score thresholds.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/502.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Validation Table - Summary of scripts flagged by keyword “fingerprint” grouped at selected score thresholds." ], "reasoning": "At the 350 score threshold, the performance is similar to Experiment 1 at a score of 12,000, with the same recall rate and device-class fingerprinting scripts.", "description": "Table 5 summarizes the performance as we did for Experiment 1. At the 350 score threshold we get very similar performance to that seen in Experiment 1 at a score of 12,000: we get essentially the same recall rate compared to the heuristic-based reference list and we picked up the same set of device-class fingerprinting scripts." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Validation Table - Summary of scripts flagged by keyword “fingerprint” grouped at selected score thresholds.'\nProblem: 'What is the percentage of the dataset at the 350 score threshold?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/502.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Validation Table - Summary of scripts flagged by keyword “fingerprint” grouped at selected score thresholds." ], "reasoning": "To find the percentage of the dataset at the 350 score threshold, look at the row labeled '% of dataset' under the column for the 350 score threshold.", "question": "What is the percentage of the dataset at the 350 score threshold?", "answer": "So, the answer is {\"answer\": \"1.8%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe number of scripts over the score at the 250 threshold is higher than at the 450 threshold.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/502.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Validation Table - Summary of scripts flagged by keyword “fingerprint” grouped at selected score thresholds." ], "reasoning": "To verify this statement, compare the 'n scripts over score' values for the 250 and 450 score thresholds. The value for 250 is 28,927, and for 450 it is 4,491.", "statement": "The number of scripts over the score at the 250 threshold is higher than at the 450 threshold.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Comparison to StreamSpot on the StreamSpot dataset. We estimate StreamSpot’s average accuracy and precision from the figure included in the paper [manzoor2016fast], which does not report exact values. They did not report recall or F-score.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/503.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Comparison to StreamSpot on the StreamSpot dataset. We estimate StreamSpot’s average accuracy and precision from the figure included in the paper [manzoor2016fast], which does not report exact values. They did not report recall or F-score." ], "reasoning": "UNICORN significantly improves precision and accuracy when using a larger neighborhood size (R = 3) compared to StreamSpot and R = 1.", "description": "We compare UNICORN to StreamSpot, using StreamSpot's own dataset.. examine with different neighborhood sizes, R = 1 (equivalent to StreamSpot) and R = 3.. UNICORN's ability to trivially consider larger neighborhoods (R = 3) produces significant precision/accuracy improvement." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Comparison to StreamSpot on the StreamSpot dataset. We estimate StreamSpot’s average accuracy and precision from the figure included in the paper [manzoor2016fast], which does not report exact values. They did not report recall or F-score.'\nProblem: 'What is the accuracy improvement of UNICORN when using R = 3 compared to StreamSpot?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/503.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Comparison to StreamSpot on the StreamSpot dataset. We estimate StreamSpot’s average accuracy and precision from the figure included in the paper [manzoor2016fast], which does not report exact values. They did not report recall or F-score." ], "reasoning": "StreamSpot has an accuracy of 0.66, while UNICORN with R = 3 has an accuracy of 0.96. The improvement is 0.96 - 0.66.", "question": "What is the accuracy improvement of UNICORN when using R = 3 compared to StreamSpot?", "answer": "So, the answer is {\"answer\": \"0.30\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUNICORN with R = 1 has a higher precision than StreamSpot.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/503.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Comparison to StreamSpot on the StreamSpot dataset. We estimate StreamSpot’s average accuracy and precision from the figure included in the paper [manzoor2016fast], which does not report exact values. They did not report recall or F-score." ], "reasoning": "The table shows that UNICORN with R = 1 has a precision of 0.51, while StreamSpot has a precision of 0.74.", "statement": "UNICORN with R = 1 has a higher precision than StreamSpot.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Decomposition of Unicorn’s false positive results of the StreamSpot dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/504.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Decomposition of Unicorn’s false positive results of the StreamSpot dataset." ], "reasoning": "Analyzing larger neighborhoods significantly reduces the false positive rate, with UNICORN only raising false positive alarms on the Download dataset.", "description": "Table III further show that analyzing larger neighborhoods greatly reduces the false positive rate.. o UNICORN raises false positive alarms only on the Download dataset," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Decomposition of Unicorn’s false positive results of the StreamSpot dataset.'\nProblem: 'Which experiment had false positives when the neighborhood size was increased to 3?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/504.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Decomposition of Unicorn’s false positive results of the StreamSpot dataset." ], "reasoning": "By examining the column '# of FPs (R=3)', we see that only the Download experiment had false positives (2) when the neighborhood size was increased to 3.", "question": "Which experiment had false positives when the neighborhood size was increased to 3?", "answer": "So, the answer is {\"answer\": \"Download\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Gmail experiment had false positives when the neighborhood size was 3.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/504.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Decomposition of Unicorn’s false positive results of the StreamSpot dataset." ], "reasoning": "The table shows that the Gmail experiment had 0 false positives when the neighborhood size was 3.", "statement": "The Gmail experiment had false positives when the neighborhood size was 3.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VIII: Experimental results of the supply-chain APT attack scenarios.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/505.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VIII: Experimental results of the supply-chain APT attack scenarios." ], "reasoning": "UNICORN demonstrates high accuracy in detecting attacks with minimal false alarms, as indicated by the precision and recall values in Table VIII.", "description": "Table VIII shows the experimental results.. We see in Table VIII that UNICORN is able to detect attacks with high accuracy with only a small number of false alarms (as reflected in precision and recall)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE VIII: Experimental results of the supply-chain APT attack scenarios.'\nProblem: 'Which experiment had the highest recall value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/505.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VIII: Experimental results of the supply-chain APT attack scenarios." ], "reasoning": "By comparing the recall values of both experiments, SC-1 has a recall of 0.96, which is higher than SC-2's recall of 0.80.", "question": "Which experiment had the highest recall value?", "answer": "So, the answer is {\"answer\": \"SC-1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSC-2 had a higher precision than SC-1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/505.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VIII: Experimental results of the supply-chain APT attack scenarios." ], "reasoning": "The table shows that SC-1 had a precision of 0.85, while SC-2 had a precision of 0.75. Therefore, SC-2 did not have a higher precision than SC-1.", "statement": "SC-2 had a higher precision than SC-1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: The performance of the pROM for the domain partition techniques presented in Figure 1(b). The RErf error of Equation (4.1) with respect to the restoring forces rf is evaluated. The average and max error across the domain is presented along with the speed up factor. The pROM variants compared are described in Table 2.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/506.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The performance of the pROM for the domain partition techniques presented in Figure 1(b). The RErf error of Equation (4.1) with respect to the restoring forces rf is evaluated. The average and max error across the domain is presented along with the speed up factor. The pROM variants compared are described in Table 2." ], "reasoning": "The Global Basis pROM in Table 3 is not accurate enough, while the Coefficients Interpolation approach achieves similar accuracy to Entries Interpolation.", "description": "The performance of the pROM variants presented in Table 2 is summarized in Table 3. The average and maximum error measure for validation samples spanning across the parametric domain are presented. The accuracy is evaluated with respect to the response time history and the time history of the nonlinear terms, namely the restoring forces.. it seems that the use of a single projection basis on the Global Basis pROM of Table 3 is not capable of approximating the underlying phenomena accurately enough.. addition, the proposed Coefficients Interpolation approach seems able to achieve a similar accuracy to the established element-wise Entries Interpolation and thus reproduce the high fidelity dynamic behavior and response.. For this reason, the efficiency achieved is negligible due to the full evaluation of the nonlinear terms." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: The performance of the pROM for the domain partition techniques presented in Figure 1(b). The RErf error of Equation (4.1) with respect to the restoring forces rf is evaluated. The average and max error across the domain is presented along with the speed up factor. The pROM variants compared are described in Table 2.'\nProblem: 'What is the maximum error measure for validation samples using the Global Basis approach in Partition A?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/506.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The performance of the pROM for the domain partition techniques presented in Figure 1(b). The RErf error of Equation (4.1) with respect to the restoring forces rf is evaluated. The average and max error across the domain is presented along with the speed up factor. The pROM variants compared are described in Table 2." ], "reasoning": "The maximum error measure for validation samples using the Global Basis approach in Partition A is found in the 'Max RE u' and 'Max RE rf' columns under Partition A. The values are 5.22% and 7.51%, respectively.", "question": "What is the maximum error measure for validation samples using the Global Basis approach in Partition A?", "answer": "So, the answer is {\"answer\": \"7.51%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Coefficients Interpolation approach achieves a maximum error measure of less than 1% in Partition B.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/506.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The performance of the pROM for the domain partition techniques presented in Figure 1(b). The RErf error of Equation (4.1) with respect to the restoring forces rf is evaluated. The average and max error across the domain is presented along with the speed up factor. The pROM variants compared are described in Table 2." ], "reasoning": "The maximum error measure for the Coefficients Interpolation approach in Partition B is found in the 'Max RE u' and 'Max RE rf' columns under Partition B. The values are 0.30% and 0.36%, respectively, both of which are less than 1%.", "statement": "The Coefficients Interpolation approach achieves a maximum error measure of less than 1% in Partition B.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Test results for 1048576 bit strings'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/507.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Test results for 1048576 bit strings" ], "reasoning": "The NIST test suite evaluates the randomness of bit strings using 15 different tests, all of which the proposed mechanism passed with high p-values, indicating good randomness properties.", "description": "The NIST Randomness Tests The NIST test suite is a statistical package that consisting of up to 15 tests.. The 15 tests concentrate on a variety of different non-randomness types that could exist in a bit strings. The proposed mechanism produces a very random bit strings as reflected by the high p-values as shown in Table II.. pretty good randomness properties and also high periodicity.. the obtained bit strings have high periodicity and good randomness properties." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Test results for 1048576 bit strings'\nProblem: 'Which test had the highest p-value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/507.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Test results for 1048576 bit strings" ], "reasoning": "To find the test with the highest p-value, we need to look at the 'P-value' column and identify the maximum value. The highest p-value is 1.000000 for the 'Lempel Ziv Complexity' test.", "question": "Which test had the highest p-value?", "answer": "So, the answer is {\"answer\": \"Lempel Ziv Complexity\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed mechanism failed the Universal test.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/507.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Test results for 1048576 bit strings" ], "reasoning": "The table shows that the result for the Universal test (L = 7, Q = 1280) is 'Succeed', indicating that the proposed mechanism passed this test.", "statement": "The proposed mechanism failed the Universal test.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Regression models predicting perceived fairness from (un)favorable outcome to an individual (Model 1), (un)biased treatment across groups (Model 2), and both factors together (Model 3).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/508.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Regression models predicting perceived fairness from (un)favorable outcome to an individual (Model 1), (un)biased treatment across groups (Model 2), and both factors together (Model 3)." ], "reasoning": "Model 1 shows that an unfavorable outcome significantly lowers perceived fairness more than biased treatment in Model 2, and Model 3 confirms that the effect of an unfavorable outcome is stronger than biased treatment.", "description": "The models in Table 3 illustrate the impacts of (un)favorable outcome to individual and (un)biased treatment to group on the perception of fairness. Model 1 tests the effect of (un)favorable outcome to an individual, model 2 tests the effect of (un)biased treatment to a group, and model 3 includes both variables. All models include a control variable of self-expectation to control for the participants' own expectations of whether they would pass or fail. Model 1 predicts that participants who were told that they failed the Master qualification test will rate the fairness of the algorithm 1.040 (95% CI: [0.819, 1.260]) point lower on a. Model 2 predicts that participants will rate the biased algorithm 0.410 (95% CI: [0.175, 0.644]) points lower on a 7-point scale (p<0.01), compared with the unbiased algorithm. Therefore, both H1a and H1b are supported. The effect of an unfavorable algorithm outcome is stronger than the effect of a biased algorithm. We compared the adjusted R2 for models 1 and 2, finding that algorithm outcome explains more variance of the perceived fairness than whether the algorithm is biased or not (model 1 adjusted R2=0.161, model 2 adjusted R2=0.055). Model 3 compares the effect more explicitly. The effect size of (un)favorable outcome on perceived fairness (Coef.=-1.034, p<0.001, 95% CI: [-1.253 -0.816]) is twice as much as the effect size of (un)biased algorithm (Coef.=-0.396, p<0.001, 95% CI: [-0.615 -0.177]), which supports H1c." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Regression models predicting perceived fairness from (un)favorable outcome to an individual (Model 1), (un)biased treatment across groups (Model 2), and both factors together (Model 3).'\nProblem: 'Which model explains more variance in perceived fairness, and what is the adjusted R2 value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/508.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Regression models predicting perceived fairness from (un)favorable outcome to an individual (Model 1), (un)biased treatment across groups (Model 2), and both factors together (Model 3)." ], "reasoning": "Model 3 has the highest adjusted R2 value of 0.177, indicating it explains more variance in perceived fairness compared to Model 1 and Model 2.", "question": "Which model explains more variance in perceived fairness?", "answer": "So, the answer is {\"answer\": \"Model 3\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe effect of an unfavorable outcome on perceived fairness is stronger than the effect of biased treatment.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/508.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Regression models predicting perceived fairness from (un)favorable outcome to an individual (Model 1), (un)biased treatment across groups (Model 2), and both factors together (Model 3)." ], "reasoning": "Model 3 shows that the effect size of an unfavorable outcome on perceived fairness (Coef.=-1.034) is larger than the effect size of biased treatment (Coef.=-0.396), supporting the statement.", "statement": "The effect of an unfavorable outcome on perceived fairness is stronger than the effect of biased treatment.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Regression models predicting perceived fairness from algorithm outcomes and development procedures. Model 1 shows the main effects, and Model 2 includes interaction terms.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/509.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Regression models predicting perceived fairness from algorithm outcomes and development procedures. Model 1 shows the main effects, and Model 2 includes interaction terms." ], "reasoning": "Model 1 shows no significant main effects of algorithm development procedures on perceived fairness, while Model 2 finds interaction effects between development procedures and algorithm outcomes.", "description": "Hypothesis 2 relates to how different algorithm creation and deployment procedures affect perceived fairness. Model 1 in Table 4 examines the main effects of different algorithm development procedures on perceived fairness, which allows us to test hypothesis 2a and 2b. Model 2 in Table 4 examines the interaction effects between development procedures and algorithm outcomes. Model 1 in Table 4 shows that the algorithm development manipulations had no significant main effects on perceived fairness. Therefore, Hypothesis 2a and 2b are not supported. The five variables reflecting the algorithm's development procedures collectively explained less than 1% of the variance in perceived fairness. Although there is no significant main effect, Model 2 in Table 4 finds several interaction effects between development procedures and algorithm outcomes. The effects of algorithm bias are moderated by the procedural factors of design and transparency. The negative effect of a biased algorithm is strongest when the algorithm is built by an outsourced team, as compared with when it is built by computer science experts within the organization (Coef.=0.971, p<0.001, 95% CI: [0.437, 1.504]), or by a mix of computer science experts and other MTurk staff within the organization (Coef.=1.070, p<0.001, 95% CI: [0.537, 1.602])." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Regression models predicting perceived fairness from algorithm outcomes and development procedures. Model 1 shows the main effects, and Model 2 includes interaction terms.'\nProblem: 'What is the coefficient for the interaction effect between biased treatment and CS team in Model 2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/509.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Regression models predicting perceived fairness from algorithm outcomes and development procedures. Model 1 shows the main effects, and Model 2 includes interaction terms." ], "reasoning": "In Model 2, the coefficient for the interaction effect between biased treatment and CS team is listed as 0.971.", "question": "What is the coefficient for the interaction effect between biased treatment and CS team in Model 2?", "answer": "So, the answer is {\"answer\": \"0.971\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe algorithm development procedures explained more than 1% of the variance in perceived fairness.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/509.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Regression models predicting perceived fairness from algorithm outcomes and development procedures. Model 1 shows the main effects, and Model 2 includes interaction terms." ], "reasoning": "The description states that the five variables reflecting the algorithm's development procedures collectively explained less than 1% of the variance in perceived fairness.", "statement": "The algorithm development procedures explained more than 1% of the variance in perceived fairness.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Regression models predicting perceived fairness from algorithm outcomes and individual differences. Model 1 shows the main effects, and Model 2 includes interaction terms.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/510.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Regression models predicting perceived fairness from algorithm outcomes and individual differences. Model 1 shows the main effects, and Model 2 includes interaction terms." ], "reasoning": "Model 1 shows that computer literacy is positively correlated with perceived fairness, while Model 2 shows that female participants and those with lower education levels react more strongly to unfavorable outcomes.", "description": "Table 5 describes a regression model predicting perceived fairness from individual differences (e.g., education level, computer literacy, and demographics). Model 1 describes main effects, while Model 2 explores the interaction effects between these variables and algorithm outcomes. Model 1 in Table 5 shows that computer literacy is positively correlated with perceived fairness. On average, participants with low computer literacy report significantly lower perceived fairness than participants with high computer literacy (Coef.=0.285, p<0.05, 95% CI: [-0.515, -0.055]), which supports hypothesis 3b. . Model 2 in Table 5 shows that certain groups react more strongly to an unfavorable outcome. For example, on average, female participants react more strongly to an unfavorable outcome than male participants (Coef.=0.558, p<0.05, 95% CI: [ 0.082, 1.034]) and participants with a lower education level react more strongly to an unfavorable outcome than participants with a higher education level (Coef.=-1.144, p<0.01, 95% CI: [-1.916, -0.372]). " }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Regression models predicting perceived fairness from algorithm outcomes and individual differences. Model 1 shows the main effects, and Model 2 includes interaction terms.'\nProblem: 'What is the coefficient for the interaction between unfavorable outcome and male participants in Model 2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/510.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Regression models predicting perceived fairness from algorithm outcomes and individual differences. Model 1 shows the main effects, and Model 2 includes interaction terms." ], "reasoning": "To find the coefficient for the interaction between unfavorable outcome and male participants in Model 2, locate the row labeled 'Unfavorable Outcome × Male' under Model 2. The coefficient is 0.558.", "question": "What is the coefficient for the interaction between unfavorable outcome and male participants in Model 2?", "answer": "So, the answer is {\"answer\": \"0.558\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nParticipants with low computer literacy report significantly lower perceived fairness than participants with high computer literacy.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/510.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Regression models predicting perceived fairness from algorithm outcomes and individual differences. Model 1 shows the main effects, and Model 2 includes interaction terms." ], "reasoning": "Model 1 shows that the coefficient for low literacy vs. high literacy is -0.285 with a p-value less than 0.05, indicating a significant difference in perceived fairness between participants with low and high computer literacy.", "statement": "Participants with low computer literacy report significantly lower perceived fairness than participants with high computer literacy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Similarity distance measurements for line and circular trajectories with different noises for distance correlation(dCor), RV coefficient and GMCC'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/511.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Similarity distance measurements for line and circular trajectories with different noises for distance correlation(dCor), RV coefficient and GMCC" ], "reasoning": "Despite GMCC giving the worst results, all three similarity distances (dCor, RV, GMCC) remain robust against noisy signals as their values are close to 0.", "description": "From Table I, it can be observed that in terms of noise the three similarity distances are pretty robust. GMCC is giving the worst results in comparison with dCor and RV, but from a general perspective, three of them remains robust in front of noisy signals as the obtained values are close to 0." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Similarity distance measurements for line and circular trajectories with different noises for distance correlation(dCor), RV coefficient and GMCC'\nProblem: 'Which similarity distance gives the worst results in terms of noise robustness?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/511.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Similarity distance measurements for line and circular trajectories with different noises for distance correlation(dCor), RV coefficient and GMCC" ], "reasoning": "The description states that GMCC gives the worst results in comparison with dCor and RV. By observing the table, GMCC values are generally higher than those of dCor and RV, confirming the description.", "question": "Which similarity distance gives the worst results in terms of noise robustness?", "answer": "So, the answer is {\"answer\": \"GMCC\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRV is the most robust similarity distance in terms of noise.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/511.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Similarity distance measurements for line and circular trajectories with different noises for distance correlation(dCor), RV coefficient and GMCC" ], "reasoning": "The description mentions that GMCC gives the worst results, implying that dCor and RV are more robust. By observing the table, RV consistently has values of 0.0, indicating high robustness.", "statement": "RV is the most robust similarity distance in terms of noise.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Similarity distance measurements for circular trajectories with different transformations for distance correlation(dCor), RV coefficient and GMCC'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/512.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Similarity distance measurements for circular trajectories with different transformations for distance correlation(dCor), RV coefficient and GMCC" ], "reasoning": "GMCC remains robust with the lowest distance for linear transformations, while dCor can find nonlinear mappings but shows higher distances for shear and squeeze transformations.", "description": "These tables are representative of the real power of GMCC. While the RV coefficient is measuring big distances between the original line and the one transformed by shear mapping, rotation or squeezing, the GMCC remains robust in null distance. dCor's distance is also pretty close to 0, but in shear mapping and squeezing gives a little bit worse results. GMCC is the coefficient that works better for similarity invariant to linear transformations and will remain always in lowest distance for any linear transformations. GMCC is limited to linear transformations. GMCC distance starts increasing when no linear mapping can be set between the compared trajectories. Meanwhile, dCor is capable. of finding also nonlinear mappings between trajectories. The distance measured by dCor will be smaller than the distance with GMCC for nonlinear correlations." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Similarity distance measurements for circular trajectories with different transformations for distance correlation(dCor), RV coefficient and GMCC'\nProblem: 'Which transformation type shows the highest distance for dCor?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/512.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Similarity distance measurements for circular trajectories with different transformations for distance correlation(dCor), RV coefficient and GMCC" ], "reasoning": "By examining the table, the highest distance for dCor is found in the 'shear' transformation type with a value of 0.12.", "question": "Which transformation type shows the highest distance for dCor?", "answer": "So, the answer is {\"answer\": \"shear\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGMCC shows the highest distance for shear transformations.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/512.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Similarity distance measurements for circular trajectories with different transformations for distance correlation(dCor), RV coefficient and GMCC" ], "reasoning": "The table shows that GMCC has a distance of 0.0 for shear transformations, which is not the highest distance.", "statement": "GMCC shows the highest distance for shear transformations.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Similarity distance measurements for linear trajectories with different transformations for dCor, RV and GMCC'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/513.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Similarity distance measurements for linear trajectories with different transformations for dCor, RV and GMCC" ], "reasoning": "GMCC remains robust with null distance for linear transformations, while dCor can find nonlinear mappings with smaller distances.", "description": "These tables are representative of the real power of GMCC. While the RV coefficient is measuring big distances between the original line and the one transformed by shear mapping, rotation or squeezing, the GMCC remains robust in null distance. dCor's distance is also pretty close to 0, but in shear mapping and squeezing gives a little bit worse results. GMCC is the coefficient that works better for similarity invariant to linear transformations and will remain always in lowest distance for any linear transformations. GMCC is limited to linear transformations. GMCC distance starts increasing when no linear mapping can be set between the compared trajectories. Meanwhile, dCor is capable. of finding also nonlinear mappings between trajectories. The distance measured by dCor will be smaller than the distance with GMCC for nonlinear correlations." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Similarity distance measurements for linear trajectories with different transformations for dCor, RV and GMCC'\nProblem: 'Which transformation type shows the highest distance for RV?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/513.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Similarity distance measurements for linear trajectories with different transformations for dCor, RV and GMCC" ], "reasoning": "By examining the table, the highest distance for RV is found in the 'shear' transformation type with a value of 0.88.", "question": "Which transformation type shows the highest distance for RV?", "answer": "So, the answer is {\"answer\": \"shear\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGMCC shows a distance of 0.007 for shear transformation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/513.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Similarity distance measurements for linear trajectories with different transformations for dCor, RV and GMCC" ], "reasoning": "The table shows that GMCC has a distance of 0.0 for shear transformation, not 0.007.", "statement": "GMCC shows a distance of 0.007 for shear transformation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: Similarity distance measurements for line Vs circle'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/514.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Similarity distance measurements for line Vs circle" ], "reasoning": "GMCC measures a higher distance (0.87) compared to dCor (0.57), indicating it is more sensitive to nonlinear transformations.", "description": "The distance measured by GMCC is 0.87 while the distance measured by dCor is 0.57. dCor is less sensible to nonlinear transformations than GMCC. our correlation coefficient is a worse tool for measuring nonlinear correlation but at the same time is a better metric for measuring only linear correlations." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: Similarity distance measurements for line Vs circle'\nProblem: 'Which metric is more sensitive to nonlinear transformations, GMCC or dCor?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/514.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Similarity distance measurements for line Vs circle" ], "reasoning": "The table shows that GMCC has a higher distance (0.871) compared to dCor (0.5258) for the transformation type 'line Vs circle'. The description states that dCor is less sensitive to nonlinear transformations than GMCC.", "question": "Which metric is more sensitive to nonlinear transformations, GMCC or dCor?", "answer": "So, the answer is {\"answer\": \"GMCC\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\ndCor is more sensitive to nonlinear transformations than GMCC.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/514.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Similarity distance measurements for line Vs circle" ], "reasoning": "The table shows that GMCC has a higher distance (0.871) compared to dCor (0.5258) for the transformation type 'line Vs circle'. The description states that dCor is less sensitive to nonlinear transformations than GMCC.", "statement": "dCor is more sensitive to nonlinear transformations than GMCC.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Rewards attributed to each player based on the action a player performs, whether the letter is useful for them, how is their task progression compared to the other player, and the scoring system.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/515.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Rewards attributed to each player based on the action a player performs, whether the letter is useful for them, how is their task progression compared to the other player, and the scoring system." ], "reasoning": "Players can increase their score by taking letters that the other player does not need, and decrease the other player's score by taking letters that the other player needs.", "description": "The scoring systems are presented in Table I.. when both players need the same letter, this version rewards players who take letters (their score increases by five points) and punishes the other players (the other players' score decreases by five points).. they can progress on their task by taking letters that the other player does not want, where their score increases by five points.. players can just punish their peers by taking a letter. that the other needs, while they do not need it. This leads to a decrease in five points in the score of the other player." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Rewards attributed to each player based on the action a player performs, whether the letter is useful for them, how is their task progression compared to the other player, and the scoring system.'\nProblem: 'What happens to the scores when a player takes a letter that is useful to both players and they are equal in task progression?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/515.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Rewards attributed to each player based on the action a player performs, whether the letter is useful for them, how is their task progression compared to the other player, and the scoring system." ], "reasoning": "When a player takes a letter that is useful to both players and they are equal in task progression, the player's score increases by 10 points and the other player's score decreases by 5 points.", "question": "How many points does the Player's scores are increased when a player takes a letter that is useful to both players and they are equal in task progression?", "answer": "So, the answer is {\"answer\": \"10\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIf a player takes a letter that is useful to them but not to the other player, and they are ahead in task progression, their score increases by 10 points.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/515.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Rewards attributed to each player based on the action a player performs, whether the letter is useful for them, how is their task progression compared to the other player, and the scoring system." ], "reasoning": "According to the table, when a player takes a letter that is useful to them but not to the other player, and they are ahead in task progression, their score increases by 10 points.", "statement": "If a player takes a letter that is useful to them but not to the other player, and they are ahead in task progression, their score increases by 10 points.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Similarity degrees for illustrating how contradicting plagiarism pairs are selected'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/516.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Similarity degrees for illustrating how contradicting plagiarism pairs are selected" ], "reasoning": "Contradicting pairs are identified by comparing the similarity degrees of plagiarized codes using two detection approaches, ensuring significant differences between the approaches.", "description": "To provide more insight, suppose we have three plagiarized codes (A, B, and C); their similarity degree toward original code—measured using two detection approaches: P1 and P2— can be seen on Table I.. According to given example, P1's and P2's ranking order are {A, C, B} and {C, A, B} respectively. As a result, A and. are considered as a contradicting pair; they satisfy (1) since P1(A) > P1(C) and P2(A) < P2(C).. Two things should be considered while selecting contradicting pairs. First, plagiarized codes in each contradicting pair should be explicitly different to each other when perceived by human. Hence, it is preferable to select pairs that generate high delta similarity degree between evaluated approaches. Second, the difference between evaluated approaches should not be coincidental on selected contradicting pairs. Hence, it is important to assure that similarity degrees resulted from both approaches are significantly different to each other; wherein the significance can be measured using t-test." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Similarity degrees for illustrating how contradicting plagiarism pairs are selected'\nProblem: 'Which plagiarized code has the highest similarity degree according to P2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/516.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Similarity degrees for illustrating how contradicting plagiarism pairs are selected" ], "reasoning": "By comparing the similarity degrees in the P2 column, we see that code C has the highest similarity degree of 95%.", "question": "Which plagiarized code has the highest similarity degree according to P2?", "answer": "So, the answer is {\"answer\": \"C\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCode B has a higher similarity degree than code A according to P1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/516.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Similarity degrees for illustrating how contradicting plagiarism pairs are selected" ], "reasoning": "According to the table, P1's similarity degree for code B is 50%, while for code A it is 70%. Therefore, code B does not have a higher similarity degree than code A according to P1.", "statement": "Code B has a higher similarity degree than code A according to P1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Considered aspects while suspecting plagiarism cases'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/517.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Considered aspects while suspecting plagiarism cases" ], "reasoning": "Most aspects mentioned by respondents are related to SBA, indicating its higher effectiveness compared to ABA from a think-aloud evaluation perspective.", "description": "Table II displays those aspects, including their occurrences (calculated based on the number of respondents mentioning it) and their relationship to evaluated plagiarism detection approaches.. Most of mentioned aspects are related to SBA. Hence, it can be stated that SBA is more effective than ABA from think-aloud evaluation perspective." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Considered aspects while suspecting plagiarism cases'\nProblem: 'Which aspect has the highest number of occurrences and what is its relationship to the evaluated approaches?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/517.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Considered aspects while suspecting plagiarism cases" ], "reasoning": "The aspect with the highest number of occurrences is 'Statement order' with 11 occurrences, and it is related to SBA since order is a part of source code structure.", "question": "Which aspect has the highest number of occurrences and what is its relationship to the evaluated approaches?", "answer": "So, the answer is {\"answer\": \"Statement order\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe aspect 'Identifier name' is related to SBA.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/517.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Considered aspects while suspecting plagiarism cases" ], "reasoning": "The table shows that 'Identifier name' is related to ABA since identifier name is a source code characteristic.", "statement": "The aspect 'Identifier name' is related to SBA.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: RDB-tree leaf order (page size = 4 KB).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/518.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: RDB-tree leaf order (page size = 4 KB)." ], "reasoning": "Table 3 lists the Hilbert curve orders and RDB-tree leaf orders for various datasets along with their dimensions and other parameters.", "description": "Table 3 lists the RDB-tree leaf orders for the datasets we use.. Table 4 shows the details of the datasets including their domain sizes while Table 3 lists the Hilbert curve orders and other parameters.. The Hilbert curve and RDB-tree orders are shown in Table 3." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: RDB-tree leaf order (page size = 4 KB).'\nProblem: 'What is the Hilbert curve order for the dataset with the highest RDB-tree leaf order?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/518.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: RDB-tree leaf order (page size = 4 KB)." ], "reasoning": "The dataset with the highest RDB-tree leaf order is SIFTn with an order of 63. The Hilbert curve order for SIFTn is 8.", "question": "What is the Hilbert curve order for the dataset with the highest RDB-tree leaf order?", "answer": "So, the answer is {\"answer\": \"8\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe dataset with the smallest Hilbert curve order has 128 dimensions.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/518.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: RDB-tree leaf order (page size = 4 KB)." ], "reasoning": "The smallest Hilbert curve order is 8, which corresponds to the SIFTn dataset. The SIFTn dataset has 128 dimensions.", "statement": "The dataset with the smallest Hilbert curve order has 128 dimensions.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Comparison of HD-Index with other techniques. DNF indicates that index construction did not terminate even after running for 20 times the duration of the slowest among the other techniques. CR indicates that index construction crashed due to running out of resources. NP indicates that index construction is not at all possible due to an inherent limitation of the technique.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/519.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of HD-Index with other techniques. DNF indicates that index construction did not terminate even after running for 20 times the duration of the slowest among the other techniques. CR indicates that index construction crashed due to running out of resources. NP indicates that index construction is not at all possible due to an inherent limitation of the technique." ], "reasoning": "HD-Index shows superior performance compared to most techniques, except for HNSW and OPQ, in terms of quality.", "description": "Table 5 shows that HD-Index significantly outperforms all the other techniques except HNSW and OPQ in terms of quality." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Comparison of HD-Index with other techniques. DNF indicates that index construction did not terminate even after running for 20 times the duration of the slowest among the other techniques. CR indicates that index construction crashed due to running out of resources. NP indicates that index construction is not at all possible due to an inherent limitation of the technique.'\nProblem: 'Which dataset shows the highest gain of HD-Index in MAP@100 over SRS?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/519.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of HD-Index with other techniques. DNF indicates that index construction did not terminate even after running for 20 times the duration of the slowest among the other techniques. CR indicates that index construction crashed due to running out of resources. NP indicates that index construction is not at all possible due to an inherent limitation of the technique." ], "reasoning": "To find the dataset with the highest gain of HD-Index in MAP@100 over SRS, look at the 'Gain of HD-Index in MAP@100 over SRS' column and identify the highest value. The highest value is 80.00x for the Glove dataset.", "question": "Which dataset shows the highest gain of HD-Index in MAP@100 over SRS?", "answer": "So, the answer is {\"answer\": \"Glove\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nHD-Index has a higher gain in query time over QALSH for the SIFT1M dataset compared to the SUN dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/519.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of HD-Index with other techniques. DNF indicates that index construction did not terminate even after running for 20 times the duration of the slowest among the other techniques. CR indicates that index construction crashed due to running out of resources. NP indicates that index construction is not at all possible due to an inherent limitation of the technique." ], "reasoning": "To verify this statement, compare the 'Gain of HD-Index in Query Time over QALSH' values for the SIFT1M and SUN datasets. The gain for SIFT1M is 11.27x, and for SUN, it is 0.15x. Since 11.27x is greater than 0.15x, the statement is supported.", "statement": "HD-Index has a higher gain in query time over QALSH for the SIFT1M dataset compared to the SUN dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Compression Ratios'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/520.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Compression Ratios" ], "reasoning": "The table shows that IDEALEM achieves significantly higher compression ratios compared to other methods for µPMU data, confirming its effectiveness as a new lossy compression method.", "description": "We present the compression ratio of IDEALEM with the ratios of other floating-point compres sion methods. Table. shows the compression ratios of four compression methods for µPMU data.. In Table I, we can see that for many µPMU data, compression ratios reach close to the maximum achievable compression ratios: 256 for the standard mode and 99.56 for the residual mode.. With the same parameter configurations used in Table I, Table II shows the reconstruction qualities of all compression methods along with original data in terms of these six measures.. Considering its very high compression ratios, the results shown here confirms the effectiveness of IDEALEM as a new lossy compression method." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Compression Ratios'\nProblem: 'Which compression method achieves the highest compression ratio for the A6BUS1C1MAG data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/520.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Compression Ratios" ], "reasoning": "To determine the highest compression ratio for the A6BUS1C1MAG data, compare the values in the row corresponding to A6BUS1C1MAG across all compression methods. IDEALEM has the highest value of 242.3.", "question": "Which compression method achieves the highest compression ratio for the A6BUS1C1MAG data?", "answer": "So, the answer is {\"answer\": \"IDEALEM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIDEALEM achieves a compression ratio of 120.0 for the A6BUS1L1MAG data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/520.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Compression Ratios" ], "reasoning": "The table shows that the compression ratio for IDEALEM with the A6BUS1L1MAG data is indeed 120.0.", "statement": "IDEALEM achieves a compression ratio of 120.0 for the A6BUS1L1MAG data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Reconstruction Qualities with Six Measures'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/521.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Reconstruction Qualities with Six Measures" ], "reasoning": "ISABELA generally performs the best in terms of reconstruction quality for most measures, while IDEALEM shows high compression ratios but performs poorly in measures affected by the number of peaks and jumps.", "description": "We present the reconstruction quality with the following measures: (#1) number of local maxima (peaks), (#2) mean distance between peaks, (#3) mean distance between the values of peaks, (#4) mean size of jumps, (#5) number of jumps higher than 10% of difference between the maximum and minimum values of series, and (#6) the percentage of points that lie outside the whiskers of the Tukey box plot (more than 1.5 interquartile ranges above the upper quartile or below the lower quartile).. With the same parameter configurations used in Table I, Table II shows the reconstruction qualities of all compression methods along with original data in terms of these six measures.. Note that in Table II, smaller difference between the first column of each measure and results of compression methods indicates better reconstruction quality.. For (#1), ISABELA performs the best for the most part.. For (#2), (#3), and (#4), IDEALEM is the second best compression method after ISABELA.. For (#5), SZ shows overall the best performance. On the other hand, IDEALEM performs poorly in most cases.. for (#6), IDEALEM is comparable with ISABELA and SZ.. All in all, we note that IDEALEM preserves reconstruction quality with the exception of measures affected by the number of peaks/jumps.. Considering its very high compression ratios, the results shown here confirms the effectiveness of IDEALEM as a new lossy compression method." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Reconstruction Qualities with Six Measures'\nProblem: 'Which compression method shows the best performance for measure #5?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/521.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Reconstruction Qualities with Six Measures" ], "reasoning": "Measure #5 shows that SZ has the lowest values across most datasets, indicating the best performance.", "question": "Which compression method shows the best performance for measure #5?", "answer": "So, the answer is {\"answer\": \"SZ\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIDEALEM performs the best for measure #2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/521.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Reconstruction Qualities with Six Measures" ], "reasoning": "The table shows that ISABELA has the lowest values for measure #2 across most datasets, not IDEALEM.", "statement": "IDEALEM performs the best for measure #2.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE IV: GPU join time decomposition for processing the complete DBLP dataset (in secs)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/522.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: GPU join time decomposition for processing the complete DBLP dataset (in secs)" ], "reasoning": "The GPU join time is primarily due to the index/filtering time, with the join time being the sum of filtering, index building, and serialization.", "description": "Further, we drill-down on the GPU join time, as shown in Table 4, where it is shown that the GPU join time is solely attributed to the index/filtering time; the join time is roughly equal to the sum of filtering, index building and serialization." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE IV: GPU join time decomposition for processing the complete DBLP dataset (in secs)'\nProblem: 'What is the join time when t is 0.9?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/522.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: GPU join time decomposition for processing the complete DBLP dataset (in secs)" ], "reasoning": "The table shows the join time for different values of t. For t = 0.9, the join time is listed in the second row under the 'join' column.", "question": "What is the join time when t is 0.9?", "answer": "So, the answer is {\"answer\": \"2815\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe join time for t = 0.85 is greater than 10000.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/522.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: GPU join time decomposition for processing the complete DBLP dataset (in secs)" ], "reasoning": "The table shows the join time for t = 0.85 in the third row under the 'join' column, which is 11367.", "statement": "The join time for t = 0.85 is greater than 10000.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: Number of datasets in which each algorithm is the best per threshold'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/523.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Number of datasets in which each algorithm is the best per threshold" ], "reasoning": "No single algorithm dominates in performance on the GPU; ALL performs best at low thresholds, PPJ at mid-range, and GRP at high thresholds.", "description": "Table 5 shows which algorithm exhibited the best performance on the GPU. We can observe, that as in the CPU comparison in , there is no algorithm that dominates the others. However, ALL favors low thresholds, PPJ mid-range and GRP the high ones," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE V: Number of datasets in which each algorithm is the best per threshold'\nProblem: 'Which algorithm performs best at a threshold of 0.75?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/523.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Number of datasets in which each algorithm is the best per threshold" ], "reasoning": "At a threshold of 0.75, the table shows that ALL has a performance score of 2, PPJ has a score of 4, and GRP has a score of 1. Since GRP has the lowest score of 2, it perform best at this threshold.", "question": "Which algorithm performs best at a threshold of 0.75?", "answer": "So, the answer is {\"answer\": \"GRP\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPPJ performs best at a threshold of 0.65.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/523.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Number of datasets in which each algorithm is the best per threshold" ], "reasoning": "At a threshold of 0.65, the table shows that PPJ has a performance score of 2, while ALL has a score of 4 and GRP has a score of 1. Since GRP has the lowest score, it performs best at this threshold.", "statement": "PPJ performs best at a threshold of 0.65.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: An end-to-end comparison between Lightor and Joint-LSTM.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/524.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: An end-to-end comparison between Lightor and Joint-LSTM." ], "reasoning": "LIGHTOR is significantly more efficient and effective than Joint-LSTM, requiring much less training time and achieving higher Video Precision@K scores.", "description": "Table. reports their training time and Video Precision of Top-5 highlights (k=5). In terms of efficiency, LIGHTOR required 100000× less training time compared to Joint-LSTM. In terms of effectiveness, LIGHTOR achieved a Video Precision@K (start) of 0.906 and a Video Precision@K (end) of 0.719, while the Video Precision@K (start) and the Video Precision@K (end) of Joint-LSTM are both round 0.6. This is because that LIGHTOR has a much better generalization than Joint-LSTM.. TABLE I: An end-to-end comparison between LIGHTOR and JointLSTM." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: An end-to-end comparison between Lightor and Joint-LSTM.'\nProblem: 'How much more efficient is LIGHTOR compared to Joint-LSTM in terms of training time?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/524.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: An end-to-end comparison between Lightor and Joint-LSTM." ], "reasoning": "The table shows that LIGHTOR required 1.06 seconds for training, while Joint-LSTM required more than 3 days. Converting 3 days to seconds (3 days * 24 hours/day * 3600 seconds/hour = 259200 seconds), LIGHTOR is approximately 259200 / 1.06 ≈ 244528 times more efficient.", "question": "How much time does LIGHTOR need in terms of training time?", "answer": "So, the answer is {\"answer\": \"1.06\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nLIGHTOR achieved a Video Precision@K (end) of 0.719.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/524.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: An end-to-end comparison between Lightor and Joint-LSTM." ], "reasoning": "The table shows that LIGHTOR has a Video Precision@K (end) score of 0.719, which matches the statement.", "statement": "LIGHTOR achieved a Video Precision@K (end) of 0.719.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Respondents characterization'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/525.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Respondents characterization" ], "reasoning": "The majority of respondents were Portuguese, with a mean age of 26.60 years. Most were female, had a college degree, and were students.", "description": "Table 1 shows the characterization of the respondents. The majority of them were Portuguese (96.3%), and the age varies between 18 and 58 years old, with M=26.60 (SD=8.628). Regarding their gender, 55% was female, and 45% was male. 40.2% has a college degree, 29.2% has a master's degree, 28.4% has a high school level, and 2.2% has a Ph.D. Additionally, 65.7% are students, 18.5% are student workers, 13.7% are workers and only 2.2% are unemployed." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Respondents characterization'\nProblem: 'What percentage of respondents are female students?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/525.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Respondents characterization" ], "reasoning": "55% of the respondents are female, and 65.7% are students. To find the percentage of female students, multiply these percentages: 55% * 65.7% = 36.135%.", "question": "What percentage of respondents are female students?", "answer": { "answer": "36.135%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe majority of respondents have a master's degree.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/525.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Respondents characterization" ], "reasoning": "The table shows that 40.2% of respondents have a college degree, 29.2% have a master's degree, 28.4% have a high school level, and 2.2% have a Ph.D. Therefore, the majority have a college degree, not a master's degree.", "statement": "The majority of respondents have a master's degree.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Workers’ situation'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/526.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Workers’ situation" ], "reasoning": "Among the 37 workers, 70.3% are technicians, 21.6% are managers, and 8.1% are commercials, distributed across various departments with the highest percentages in research and tourism (25.0% each).", "description": "When the workers were questioned (N=37), 70.3% of them affirmed to be technicians, 21.6% are managers, and 8.1% are commercials (Table 2). These are distributed by different departments: 25.0% in research, 25.0% in tourism, 22.2% in the Technical Department, 13.9% in Healthcare, 8.3% in Financial Management and 5.6% in the Commercial Department." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Workers’ situation'\nProblem: 'How many workers are in the Healthcare department?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/526.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Workers’ situation" ], "reasoning": "The table shows that 13.9% of the workers are in the Healthcare department. Given that the total number of workers is 37, the number of workers in Healthcare is calculated as 13.9% of 37.", "question": "How many workers are in the Healthcare department?", "answer": "So, the answer is {\"answer\": \"5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThere are more workers in the Financial Management department than in the Commercial department.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/526.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Workers’ situation" ], "reasoning": "The table shows that there are 3 workers in the Financial Management department and 2 workers in the Commercial department, indicating that the statement is true.", "statement": "There are more workers in the Financial Management department than in the Commercial department.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Students’ situation'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/527.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Students’ situation" ], "reasoning": "The majority of students live in urban areas and are studying to obtain a master's degree, with engineering being the most common study area.", "description": "Regarding students (228 respondents), the majority lives in the urban area (78.1%), and 21.9% lives in rural area (Table 3). 74.1% of the students are studying to obtain the master's degree, 19.7% to obtain the. Ph.D. and 6.1% to obtain the college degree. The study area of the students is engineering (48.5%), health (23.3%), sciences (14.5%), economics and management (10.6%), and arts and architecture (3.1%)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Students’ situation'\nProblem: 'What percentage of students are studying health and live in rural areas?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/527.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Students’ situation" ], "reasoning": "From the table, 23.3% of students are studying health. 21.9% of students live in rural areas. To find the percentage of students studying health and living in rural areas, multiply these percentages: 23.3% * 21.9% = 5.1%.", "question": "What percentage of students are studying health and live in rural areas?", "answer": "So, the answer is {\"answer\": \"5.1%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMost students studying engineering live in urban areas.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/527.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Students’ situation" ], "reasoning": "The table shows that 48.5% of students study engineering and 78.1% of students live in urban areas. Since the majority of students live in urban areas, it is likely that most engineering students also live in urban areas.", "statement": "Most students studying engineering live in urban areas.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Searching for pharmacies'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/528.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Searching for pharmacies" ], "reasoning": "63.1% of respondents do not use applications to find pharmacies, while 36.9% do, with most searching less than five times per month and 54% knowing software for locating pharmacies.", "description": "When asked about the past usage of applications for support in the contact/location of pharmacies (results in Table 4), 63.1% responded no, and 36.9% (100 respondents) answered yes. The respondents who said yes, 95.0% of them consider to search for a pharmacy-related subject less than five times per month, 1.0% between five and ten times per month, and 4.0% more than ten times per month. Additionally, these respondents were asked about the need for more information about in-service pharmacies or available drugs to achieve higher efficiency in obtaining their necessities. 11% of them said never, 40.0% rarely, 38.0% sometimes, 10% frequently, and 1% always.. Despite these opinions, 54% of these respondents consider to know any software that allows the location of pharmacies in service: 77.8% knows \"Farm´acias de Servic¸o,\" 63% knows \"Farm´acias Portuguesas\" and 5.6% indicates other software as Google." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Searching for pharmacies'\nProblem: 'What percentage of respondents who use software to locate pharmacies know 'Farmácias Portuguesas'?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/528.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Searching for pharmacies" ], "reasoning": "Out of the respondents who use software to locate pharmacies (54%), 63% know 'Farmácias Portuguesas'.", "question": "What percentage of respondents who use software to locate pharmacies know 'Farmácias Portuguesas'?", "answer": "So, the answer is {\"answer\": \"63%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMost respondents who use software to locate pharmacies know 'Farmácias de Serviço'.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/528.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Searching for pharmacies" ], "reasoning": "Among the respondents who use software to locate pharmacies (54%), 77.8% know 'Farmácias de Serviço', which is the highest percentage among the options.", "statement": "Most respondents who use software to locate pharmacies know 'Farmácias de Serviço'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Difficulties to find drugs'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/529.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Difficulties to find drugs" ], "reasoning": "51.0% of respondents find it difficult to get all drugs in a single pharmacy, with the main issues being unavailability of drugs in the nearest or open pharmacy (60%) and the need to order unavailable drugs (25%).", "description": "Based on Table 5, 51.0% of the respondents who used applications for support in the contact/location of pharmacies, find difficulties to find all the drugs in their prescription in a single pharmacy. The main difficulties mentioned are: 1. sometimes the nearest or open pharmacy does not have all the necessary drugs available (60%), 2. the need to order unavailable drugs and with it a considerable waiting time (25%),. it is more difficult to get drugs in weekends or holiday periods (24%), 4. it takes a long time to find what the respondents want, and sometimes they have to go to several pharmacies (21%), 6. pharmacies unavailability (1%). 5. software systems are not very explanatory of pharmacy search results or are outdated (15%),. To solve these difficulties, 10% has already previously contacted the pharmacy in service in order to check in advance if all the drugs in their prescription are available and do not waste time in traveling." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Difficulties to find drugs'\nProblem: 'What percentage of respondents mentioned that it is more difficult to get drugs in weekends or holiday periods?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/529.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Difficulties to find drugs" ], "reasoning": "The table shows that 24% of respondents mentioned that it is more difficult to get drugs in weekends or holiday periods.", "question": "What percentage of respondents mentioned that it is more difficult to get drugs in weekends or holiday periods?", "answer": "So, the answer is {\"answer\": \"24\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\n15% of respondents find that software systems are not very explanatory of pharmacy search results or are outdated.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/529.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Difficulties to find drugs" ], "reasoning": "The table indicates that 15% of respondents mentioned that software systems are not very explanatory of pharmacy search results or are outdated.", "statement": "15% of respondents find that software systems are not very explanatory of pharmacy search results or are outdated.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Behavior of respondents to find drugs'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/530.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Behavior of respondents to find drugs" ], "reasoning": "Most respondents search for all results near them when looking for a pharmacy, and if a pharmacy does not have all their medicines, they mostly search or inquire about nearby pharmacies to complete their prescription.", "description": "In case the respondent has to search in applications or on the internet about a particular local pharmacy (Table 6), 27.3% consider that they only search results less or equal than 5km away, 8.1% search results for more than 5km, 31.3% search all results near them, 27.3% search for 3 or fewer results, and 6.1% search more than 3 results near them.. Considering the scenario of the respondent going to a local pharmacy and, after arriving there, verifying that the same does not have all of their medicines available: 1. 59.0% search or inquire about nearby pharmacies to complete their prescription, 2. 32% give up on going to a new pharmacy and order the necessary drugs, getting on the waiting list, 3. 20% call other nearby pharmacies to see if they have the necessary drugs, 4. 16.0% try asking someone for advice on the subject or more information.. only 1.0% have no interest in a new system of contact with pharmacies, 2. 6.0% have low interest, 3. 39% have some interest in the proposed system, 4. moreover, 54% of them have high interest. After the descriptive analysis of the sample, it is essential to perceive the influence of some factors in the opinion of the respondents. Thus, a cross-tabulation analysis of the information was performed using the chi-square test, whose null hypothesis is that the variables are independent. Additionally, the chi-square test provides two statistics that indicate whether the variables are associated or independent: a chi-square statistic and a p-value. Thus, if the p-value is less than or equal to 0.05 (level of significance considered as acceptable), the null hypothesis is rejected, and the variables are associated. If the p-value is more significant than 0.05, the null hypothesis is not rejected, and it is possible to conclude that variables are independent." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Behavior of respondents to find drugs'\nProblem: 'What percentage of respondents search for all results near them when looking for a pharmacy and have high interest in a new system of contact with pharmacies?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/530.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Behavior of respondents to find drugs" ], "reasoning": "From the table, 31.3% of respondents search for all results near them when looking for a pharmacy. Additionally, 54% of respondents have high interest in a new system of contact with pharmacies.", "question": "What percentage of respondents search for all results near them when looking for a pharmacy?", "answer": "So, the answer is {\"answer\": \"31.3%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMost respondents give up on going to a new pharmacy and order the necessary drugs if a pharmacy does not have all their medicines.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/530.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Behavior of respondents to find drugs" ], "reasoning": "The table shows that 32% of respondents give up on going to a new pharmacy and order the necessary drugs, which is not the majority compared to the 59% who search or inquire about nearby pharmacies.", "statement": "Most respondents give up on going to a new pharmacy and order the necessary drugs if a pharmacy does not have all their medicines.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Influence of age in the respondent opinion'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/531.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Influence of age in the respondent opinion" ], "reasoning": "The null hypothesis for age vs. difficulty to obtain drugs is rejected, indicating that younger people find it easier to obtain drugs compared to older people.", "description": "Table 7 presents the main results of the influence of the age in some opinions of the respondents. Regarding the first comparison (age vs. difficulty to obtain drugs), the null hypothesis is rejected, and variables are associated. This means that young people have fewer difficulties to find drugs, and older people have more difficulties, which could be explained by the fact that old people have more health problems or have already lived more experiences." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Influence of age in the respondent opinion'\nProblem: 'What is the p-value for the comparison between age and difficulty in obtaining drugs?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/531.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Influence of age in the respondent opinion" ], "reasoning": "The table shows the p-value for the comparison between age and difficulty in obtaining drugs as 0.009.", "question": "What is the p-value for the comparison between age and difficulty in obtaining drugs?", "answer": "So, the answer is {\"answer\": \"0.009\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOlder people have more difficulty obtaining drugs than younger people.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/531.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Influence of age in the respondent opinion" ], "reasoning": "The table shows that the null hypothesis for age vs. difficulty in obtaining drugs is rejected, with a p-value of 0.009, indicating that older people have more difficulty obtaining drugs.", "statement": "Older people have more difficulty obtaining drugs than younger people.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: Relation between necessity of more information and difficulty to obtain drugs'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/532.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Relation between necessity of more information and difficulty to obtain drugs" ], "reasoning": "People who had more difficulties obtaining drugs frequently felt the necessity for more information, while those with fewer difficulties rarely felt this necessity.", "description": "The comparison between the necessity of more information and the difficulty in obtaining drugs (Table 8) show that these variables are associated (p=0.045<0.05). Thus, people who had more difficulties to get drugs are precisely those who feel more necessity to have more information available. By the opposite, people with few difficulties to obtain their drugs are who rarely feel the necessity to have more information available." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 8: Relation between necessity of more information and difficulty to obtain drugs'\nProblem: 'What is the p-value associated with the relationship between the necessity of more information and the difficulty in obtaining drugs?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/532.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Relation between necessity of more information and difficulty to obtain drugs" ], "reasoning": "The table shows the p-value for the association between the necessity of more information and the difficulty in obtaining drugs as 0.045.", "question": "What is the p-value associated with the relationship between the necessity of more information and the difficulty in obtaining drugs?", "answer": "So, the answer is {\"answer\": \"0.045\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPeople who rarely felt the necessity for more information had fewer difficulties obtaining drugs.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/532.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Relation between necessity of more information and difficulty to obtain drugs" ], "reasoning": "The table shows that 30 people who rarely felt the necessity for more information had no difficulty obtaining drugs, compared to 21 people who had difficulty.", "statement": "People who rarely felt the necessity for more information had fewer difficulties obtaining drugs.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 9: Factors that influence the interest in a new system'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/533.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Factors that influence the interest in a new system" ], "reasoning": "People who face difficulties in obtaining drugs, waste time finding necessary drugs, or search for nearby pharmacies show higher interest in a new system.", "description": "Table 9 present some factor that could influence the interest in a new system. Regarding the difficulty to obtain drugs, it is possible to verify that people who had to feel that, are the most interested in a new system, and people with fewer difficulties does not feel the same interest.. Additionally, people who had already a waste of time to find the necessary drugs have more interest in a new system. If they do not waste time to find drugs, they have less interest in a new system.. Regarding the situation \"sometimes the open pharmacy does not have the needed drugs\" vs. \"interest in a new system,\" the null hypothesis is rejected and, then, both variables are associated. Similar to previous analysis, people who consider that sometimes the open pharmacy does not have the needed drugs have a high interest in a new system.. Finally, people that search or inform of nearby pharmacies are the most interested in a new system (p=0.004), and the opposite also happens.. To conclude, it turns out that the most active people in the search for pharmacies, drugs or even information are those who are most interested in a new system that easily makes all this information available." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 9: Factors that influence the interest in a new system'\nProblem: 'What is the relationship between the difficulty to obtain drugs and the interest in a new system?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/533.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Factors that influence the interest in a new system" ], "reasoning": "The tables shows that the p value for difficulty obtaining drugs is 0.001.", "question": "What is the p value for Difficulty to obtain drugs?", "answer": "So, the answer is {\"answer\": \"0.001\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPeople who do not waste time finding necessary drugs have high interest in a new system.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/533.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Factors that influence the interest in a new system" ], "reasoning": "The table shows that people who do not waste time finding necessary drugs have less high interest (38) compared to those who do waste time (16).", "statement": "People who do not waste time finding necessary drugs have high interest in a new system.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: F1 Score of KB Update Given Different KB Sizes'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/534.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: F1 Score of KB Update Given Different KB Sizes" ], "reasoning": "The Dual Track Manager consistently achieves higher F1 scores compared to Baseline I and Baseline II across different KB sizes.", "description": "We further evaluated whether the entity added to KB matches with the human intention or not (Hypothesis-II). As presented in Table I, our algorithm consistently maintains higher F1 score in comparison to other baseline agents in the medium sized KB." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: F1 Score of KB Update Given Different KB Sizes'\nProblem: 'What is the F1 score of the Dual Track Manager for a KB size of 26?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/534.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: F1 Score of KB Update Given Different KB Sizes" ], "reasoning": "The table shows the F1 scores for different agents across various KB sizes. For a KB size of 26, the F1 score of the Dual Track Manager is 0.77.", "question": "What is the F1 score of the Dual Track Manager for a KB size of 26?", "answer": "So, the answer is {\"answer\": \"0.77\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBaseline II has a higher F1 score than Baseline I for a KB size of 17.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/534.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: F1 Score of KB Update Given Different KB Sizes" ], "reasoning": "The table shows that for a KB size of 17, Baseline II has an F1 score of 0.61, while Baseline I has an F1 score of 0.59. Therefore, Baseline II has a higher F1 score than Baseline I for a KB size of 17.", "statement": "Baseline II has a higher F1 score than Baseline I for a KB size of 17.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Results of the human participant experiment.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/535.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Results of the human participant experiment." ], "reasoning": "Our dialog agent performed significantly better in reducing frustration and increasing future use compared to the baseline agent.", "description": "By the end of each dialog, each participant filled out a survey form that includes prompts: Q1, Task is easy to participants; Q2, Robot understood participant; Q3, Robot frustrated participant; Q4, Participant will use the robot in the future. The response choices range from 0 (Strongly disagree) to 4 (Strongly agree).. Table II shows the average scores. At the 0.1 confidence level, our dialog agent performed significantly better in response for Q3 (frustrated) and Q4 (usefulness). There is no significance difference observed in responses to the other two questions." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Results of the human participant experiment.'\nProblem: 'Which agent had a higher average score for Q4 (usefulness)?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/535.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Results of the human participant experiment." ], "reasoning": "Compare the average scores for Q4 between 'Our dialog agent' and 'A baseline agent with static KB'. Our dialog agent scored 2.50, while the baseline agent scored 1.75.", "question": "Which agent had a higher average score for Q4 (usefulness)?", "answer": "So, the answer is {\"answer\": \"Our dialog agent\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe baseline agent had a higher average score for Q2 (understanding) than our dialog agent.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/535.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Results of the human participant experiment." ], "reasoning": "Compare the average scores for Q2 between 'Our dialog agent' and 'A baseline agent with static KB'. Our dialog agent scored 2.50, while the baseline agent scored 1.83.", "statement": "The baseline agent had a higher average score for Q2 (understanding) than our dialog agent.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Hyperparameters of the Random Forest models'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/536.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Hyperparameters of the Random Forest models" ], "reasoning": "Both person-specific and generic models were developed using Random Forest models with similar performance across all folds, using simple RF models to avoid overfitting.", "description": "Person-specific models—they were developed using Random Forest (RF) models (Table III). All. Generic models—they were also developed using Random Forest (RF) models (Table III).. some folds. In our case, all folds achieved similar performance2. Secondly, all the models use a very simple RF model (Table III) that is less likely to overfit." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: Hyperparameters of the Random Forest models'\nProblem: 'What is the maximum depth of the trees used in both classification and regression models?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/536.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Hyperparameters of the Random Forest models" ], "reasoning": "The table shows that the maximum depth of the trees for both classification and regression models is 2.", "question": "What is the maximum depth of the trees used in both classification and regression models?", "answer": "So, the answer is {\"answer\": \"2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe number of trees used in the classification model is 1000.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/536.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Hyperparameters of the Random Forest models" ], "reasoning": "The table indicates that the number of trees used in the classification model is indeed 1000.", "statement": "The number of trees used in the classification model is 1000.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Performance (False Match Rate in % @ False Non-Match Rate = 0.1%) of Face Recognition Models on the DiveFace dataset. We show in brackets the relative error growth rates with respect to the best class (Group 1 Male).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/537.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance (False Match Rate in % @ False Non-Match Rate = 0.1%) of Face Recognition Models on the DiveFace dataset. We show in brackets the relative error growth rates with respect to the best class (Group 1 Male)." ], "reasoning": "The table shows significant performance gaps between demographic groups, with the largest degradation for Group 3 Female, leading to a 196% and 217% increase in EER for VGGFace and ResNet-50, respectively.", "description": "Results reported in Table 1 exhibit large gaps between performances obtained by different demographic groups, suggesting that both gender and ethnicity significantly affect the performance of biased models. These effects are particularly high for ethnicity, with a very large degradation of the results for the class less represented in the training data (Group 3 Female). This degradation produces a relative increment of the Equal Error Rate (EER) of 196% and 217% for VGGFace and ResNet-50, respectively, with regard to the best class (Group 1 Male)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Performance (False Match Rate in % @ False Non-Match Rate = 0.1%) of Face Recognition Models on the DiveFace dataset. We show in brackets the relative error growth rates with respect to the best class (Group 1 Male).'\nProblem: 'What is the relative increment of the Equal Error Rate (EER) for Group 2 Female using the ResNet-50 model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/537.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance (False Match Rate in % @ False Non-Match Rate = 0.1%) of Face Recognition Models on the DiveFace dataset. We show in brackets the relative error growth rates with respect to the best class (Group 1 Male)." ], "reasoning": "The table indicates that the relative increment of the EER for Group 2 Female using the ResNet-50 model is 126%.", "question": "What is the relative increment of the Equal Error Rate (EER) for Group 2 Female using the ResNet-50 model?", "answer": "So, the answer is {\"answer\": \"126%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGroup 1 Male has the lowest EER for both models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/537.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance (False Match Rate in % @ False Non-Match Rate = 0.1%) of Face Recognition Models on the DiveFace dataset. We show in brackets the relative error growth rates with respect to the best class (Group 1 Male)." ], "reasoning": "The table shows that Group 1 Male has an EER of 7.99 for VGGFace and 1.60 for ResNet-50, which are the lowest values for each model.", "statement": "Group 1 Male has the lowest EER for both models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Geographical location information of the 26 DoH recursors and the median of their resolution time.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/538.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Geographical location information of the 26 DoH recursors and the median of their resolution time." ], "reasoning": "Anycast recursors generally have shorter ping times and better resolution times compared to unicast recursors.", "description": "Table. shows the geographical location, the ping time, and the median DNS resolution time of the 26 recursors used in our experiments. Except for those recursors that do not respond to our ping (ICMP) packets (whose ping time is marked as N/A), there is a correlation between the geographical location and the ping time. Notably, most recursors that are hosted on anycast IP address(es) have the shortest ping time, while unicast recursors have a longer ping time.. As shown in Table I, the top four anycast recursors with the best resolution time also have relatively small ping time. However, there are other four anycast recursors also have relatively small ping time, but are not among the top recursors with shortest resolution time." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Geographical location information of the 26 DoH recursors and the median of their resolution time.'\nProblem: 'Which anycast recursor has the shortest ping time and what is its resolution time?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/538.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Geographical location information of the 26 DoH recursors and the median of their resolution time." ], "reasoning": "By examining the table, the anycast recursor with the shortest ping time is dns.google with a ping time of 2.93 ms. Its resolution time is 312 ms.", "question": "Which anycast recursor has the shortest ping time?", "answer": "So, the answer is {\"answer\": \"dns.google\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe anycast recursor with the shortest ping time has a resolution time of 312 ms.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/538.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Geographical location information of the 26 DoH recursors and the median of their resolution time." ], "reasoning": "The table shows that the anycast recursor dns.google has the shortest ping time of 2.93 ms and a resolution time of 312 ms.", "statement": "The anycast recursor with the shortest ping time has a resolution time of 312 ms.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Values for the parameters that satisfy constraints (1)–(7).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/539.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Values for the parameters that satisfy constraints (1)–(7)." ], "reasoning": "The table shows that the churn rate α is never more than 0.05, and Smin is always greater than 8.5 times the number of failures f.", "description": "Table. gives a few sets of values for which the above constraints are satisfied. In all consistent sets of parameter values, the churn rate α is never more than 0.05 and. Smin > 8.5f . The algorithm can tolerate any size of f as long as. Smin is proportionally big." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Values for the parameters that satisfy constraints (1)–(7).'\nProblem: 'What is the minimum system size (NSmin) when the number of failures (f) is 100?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/539.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Values for the parameters that satisfy constraints (1)–(7)." ], "reasoning": "To find the minimum system size (NSmin) when the number of failures (f) is 100, look at the row where f is 100 and find the corresponding value in the NSmin column.", "question": "What is the minimum system size (NSmin) when the number of failures (f) is 100?", "answer": "So, the answer is {\"answer\": \"838\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe churn rate α is 0.05 when the number of failures (f) is 5.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/539.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Values for the parameters that satisfy constraints (1)–(7)." ], "reasoning": "To verify this statement, look at the row where f is 5 and check the value in the churn rate (α) column. The table shows that the churn rate α is 0.05 when f is 5.", "statement": "The churn rate α is 0.05 when the number of failures (f) is 5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: The performance of spectral conformal parameterization (SCP) [17], conformal equivalence of triangle meshes (CETM) [18] and PGCP for free-boundary conformal parameterization of simply-connected open surfaces.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/540.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The performance of spectral conformal parameterization (SCP) [17], conformal equivalence of triangle meshes (CETM) [18] and PGCP for free-boundary conformal parameterization of simply-connected open surfaces." ], "reasoning": "Our method is significantly faster than SCP and CETM by over 40% and 70% respectively, while maintaining comparable accuracy in terms of average angular distortion.", "description": "To assess the performance of our method, we compare it with the spectral conformal parameterization (SCP) and conformal equivalence of triangle meshes (CETM) in terms of the computation time and the angular distortion (see Table 1).. The experimental results show that our proposed method is significantly faster than both SCP and CETM by over 40% and 70% respectively on average, while maintaining comparable accuracy in terms of the average angular distortion. This demonstrates the effectiveness of our method for free-boundary global conformal parameterization." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: The performance of spectral conformal parameterization (SCP) [17], conformal equivalence of triangle meshes (CETM) [18] and PGCP for free-boundary conformal parameterization of simply-connected open surfaces.'\nProblem: 'What is the average computation time for the PGCP method across all surfaces?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/540.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The performance of spectral conformal parameterization (SCP) [17], conformal equivalence of triangle meshes (CETM) [18] and PGCP for free-boundary conformal parameterization of simply-connected open surfaces." ], "reasoning": "To find the average computation time for the PGCP method, sum the PGCP Time values for all surfaces and divide by the number of surfaces. The sum is 0.6 + 0.7 + 0.9 + 1.5 + 2.0 + 6.0 + 6.1 + 47.6 = 65.4. There are 8 surfaces, so the average is 65.4 / 8 = 8.175 seconds.", "question": "What is the average computation time for the PGCP method across all surfaces?", "answer": { "answer": "8.175 seconds" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe PGCP method has a lower average angular distortion than the SCP method for all surfaces.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/540.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The performance of spectral conformal parameterization (SCP) [17], conformal equivalence of triangle meshes (CETM) [18] and PGCP for free-boundary conformal parameterization of simply-connected open surfaces." ], "reasoning": "By comparing the PGCP mean(|dl|) and SCP mean(|dl|) values for all surfaces, we see that PGCP has lower or equal values for all surfaces. Therefore, the statement is supported.", "statement": "The PGCP method has a lower average angular distortion than the SCP method for all surfaces.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: The performance of linear disk conformal map (LDM) [25], conformal energy minimization (CEM) [26] and PGCP for disk conformal parameterization of simply-connected open surfaces.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/541.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The performance of linear disk conformal map (LDM) [25], conformal energy minimization (CEM) [26] and PGCP for disk conformal parameterization of simply-connected open surfaces." ], "reasoning": "Our method is significantly faster than LDM and CEM by over 50% and 30% on average respectively, and achieves comparable or smaller angular distortion.", "description": "To evaluate the performance of our method, we compare it with the state-of-the-art linear disk conformal map (LDM) method and the conformal energy minimization (CEM) method (see Table 2).. It can be observed that our method is significantly faster than LDM and CEM by over 50% and 30% on average respectively. Also, our method achieves comparable or smaller angular distortion when compared to the two other methods. This shows that our method is advantageous for disk conformal parameterization." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: The performance of linear disk conformal map (LDM) [25], conformal energy minimization (CEM) [26] and PGCP for disk conformal parameterization of simply-connected open surfaces.'\nProblem: 'What is the average time taken by the PGCP method across all surfaces?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/541.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The performance of linear disk conformal map (LDM) [25], conformal energy minimization (CEM) [26] and PGCP for disk conformal parameterization of simply-connected open surfaces." ], "reasoning": "To find the average time taken by the PGCP method, sum the PGCP Time (s) for all surfaces and divide by the number of surfaces.", "question": "What is the average time taken by the PGCP method across all surfaces?", "answer": "{\"answer\": \"10.3\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe PGCP method is faster than the LDM method for the Brain surface.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/541.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The performance of linear disk conformal map (LDM) [25], conformal energy minimization (CEM) [26] and PGCP for disk conformal parameterization of simply-connected open surfaces." ], "reasoning": "The table shows that the PGCP Time (s) for the Brain surface is 1.5, while the LDM Time (s) is 2.9, indicating that the PGCP method is indeed faster.", "statement": "The PGCP method is faster than the LDM method for the Brain surface.", "answer": "{\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: The performance of folding-free global conformal mapping (FFGCM) [30], FLASH [31] and PGCP for spherical conformal parameterization of genus-0 closed surfaces.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/542.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The performance of folding-free global conformal mapping (FFGCM) [30], FLASH [31] and PGCP for spherical conformal parameterization of genus-0 closed surfaces." ], "reasoning": "Our proposed PGCP method achieves a significant reduction in computational time by over 90% on average compared to FFGCM and around 25% for dense meshes compared to FLASH, while also producing spherical conformal parameterizations with smaller angular distortions.", "description": "To evaluate the performance, we compare our proposed method with the state-of-the-art folding-free global conformal mapping (FFGCM) algorithm and the FLASH algorithm (see Table 3). The MATLAB version of FFGCM is kindly provided by its authors, and the MATLAB version of FLASH can be found at . Because of the \"divide-and-conquer\" nature of our method, our method is capable of producing spherical conformal parameterizations with a smaller angular distortion when compared to the two state-of-the-art algorithms. In particular, the FLASH algorithm involves puncturing a triangle from the input surface and flattening the punctured surface onto a big triangular domain. This step unavoidably creates squeezed regions and produces certain angular distortions. While the distortions are alleviated in the subsequent step using quasiconformal composition, the step again involves a domain where most vertices are squeezed at the interior, which leads to some distortions. By contrast, our proposed PGCP method flattens each submesh naturally, with the shape of the submesh boundary taken into consideration. This effectively reduces the angular distortions, thereby producing a spherical conformal parameterization with a better accuracy. Moreover, because of the ability of exploiting parallelism, our method achieves a significant reduction in computational time by over 90% on average when compared to FFGCM. When compared to FLASH, our method achieves comparable efficiency for moderate meshes and a notable reduction in computational time by around 25% for dense meshes. This shows the advantages of our method for spherical conformal parameterization." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: The performance of folding-free global conformal mapping (FFGCM) [30], FLASH [31] and PGCP for spherical conformal parameterization of genus-0 closed surfaces.'\nProblem: 'What is the computational time for the PGCP method on the Asian Dragon surface?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/542.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The performance of folding-free global conformal mapping (FFGCM) [30], FLASH [31] and PGCP for spherical conformal parameterization of genus-0 closed surfaces." ], "reasoning": "The table lists the computational time for the PGCP method for various surfaces. For the Asian Dragon surface, the PGCP Time (s) is 48.5 seconds.", "question": "What is the computational time for the PGCP method on the Asian Dragon surface?", "answer": "So, the answer is {\"answer\": \"48.5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe PGCP method has a lower mean angular distortion than the FLASH method for the Duck surface.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/542.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The performance of folding-free global conformal mapping (FFGCM) [30], FLASH [31] and PGCP for spherical conformal parameterization of genus-0 closed surfaces." ], "reasoning": "The table shows the mean angular distortion (mean(d)) for both the PGCP and FLASH methods for the Duck surface. The PGCP method has a mean angular distortion of 2.4, while the FLASH method has a mean angular distortion of 2.2. Therefore, the statement is refuted.", "statement": "The PGCP method has a lower mean angular distortion than the FLASH method for the Duck surface.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Comparison between BFF [56] and PGCP for free-boundary conformal parameterization for simply-connected open surfaces, disk conformal parameterization for simply-connected open surfaces, and spherical conformal parameterization for genus-0 closed surfaces.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/543.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison between BFF [56] and PGCP for free-boundary conformal parameterization for simply-connected open surfaces, disk conformal parameterization for simply-connected open surfaces, and spherical conformal parameterization for genus-0 closed surfaces." ], "reasoning": "Our method achieves better or comparable conformality and efficiency for free-boundary and disk conformal parameterization, and is advantageous in both aspects for spherical conformal parameterization.", "description": "Table 4 shows the comparison between the two methods. For free-boundary and disk. conformal parameterization, it can be observed that our method achieves at least comparable and sometimes better conformality, with a shorter computational time. For spherical conformal. parameterization, our method is advantageous in both the conformality and efficiency. A possible reason is that BFF handles genus-0 closed surfaces by removing an arbitrary vertex star, flattening the punctured surface onto a disk conformally followed by a suitable rescaling, and finally mapping the disk onto the sphere using stereographic projection and filling the punctured vertex star at the pole. The choice of the vertex star greatly affects the overall shape of the disk parameterization of the punctured surface and the final angular distortion of the spherical parameterization. By contrast, our \"divide and conquer\" approach enables us to tackle the conformal flattening problem of subdomains which are obtained from a more natural partition of the surface, thereby achieving better conformality." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Comparison between BFF [56] and PGCP for free-boundary conformal parameterization for simply-connected open surfaces, disk conformal parameterization for simply-connected open surfaces, and spherical conformal parameterization for genus-0 closed surfaces.'\nProblem: 'Which method achieves better conformality for the 'Octopus' surface under spherical parameterization?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/543.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison between BFF [56] and PGCP for free-boundary conformal parameterization for simply-connected open surfaces, disk conformal parameterization for simply-connected open surfaces, and spherical conformal parameterization for genus-0 closed surfaces." ], "reasoning": "For the 'Octopus' surface under spherical parameterization, compare the BFF mean(|dl|) and PGCP mean(|dl|) values. BFF mean(|dl|) is 7.3, and PGCP mean(|dl|) is 2.6. Since a lower mean(|dl|) indicates better conformality, PGCP achieves better conformality.", "question": "Which method achieves better conformality for the 'Octopus' surface under spherical parameterization?", "answer": "So, the answer is {\"answer\": \"PGCP\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur method achieves better conformality for the 'Brain' surface under disk-boundary parameterization.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/543.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison between BFF [56] and PGCP for free-boundary conformal parameterization for simply-connected open surfaces, disk conformal parameterization for simply-connected open surfaces, and spherical conformal parameterization for genus-0 closed surfaces." ], "reasoning": "For the 'Brain' surface under disk-boundary parameterization, compare the BFF mean(|dl|) and PGCP mean(|dl|) values. BFF mean(|dl|) is 1.6, and PGCP mean(|dl|) is 1.5. Since a lower mean(|dl|) indicates better conformality, PGCP achieves better conformality, not our method.", "statement": "Our method achieves better conformality for the 'Brain' surface under disk-boundary parameterization.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: The performance of our proposed PGCP method with different choices of cut paths. The mean, standard deviation, median, and interquartile range of the absolute angular distortion |d| are evaluated.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/544.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The performance of our proposed PGCP method with different choices of cut paths. The mean, standard deviation, median, and interquartile range of the absolute angular distortion |d| are evaluated." ], "reasoning": "The table shows that the angular distortions are consistent across different cut paths, demonstrating the robustness of the method.", "description": "The accuracy of the resulting parameterizations is recorded in Table 5. It can be observed that the angular distortions produced by different choices of cut paths are highly consistent, which indicates that our method is robust to the choice of the cut paths." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: The performance of our proposed PGCP method with different choices of cut paths. The mean, standard deviation, median, and interquartile range of the absolute angular distortion |d| are evaluated.'\nProblem: 'Which surface has the highest mean angular distortion and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/544.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The performance of our proposed PGCP method with different choices of cut paths. The mean, standard deviation, median, and interquartile range of the absolute angular distortion |d| are evaluated." ], "reasoning": "By examining the 'mean(|d|)' column, the highest value is 0.49, which corresponds to the Bunny surface.", "question": "Which surface has the highest mean angular distortion and what is its value?", "answer": "So, the answer is {\"answer\": \"Bunny\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Duck surface has a higher mean angular distortion than the Face surface.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/544.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The performance of our proposed PGCP method with different choices of cut paths. The mean, standard deviation, median, and interquartile range of the absolute angular distortion |d| are evaluated." ], "reasoning": "The table shows that the mean angular distortion for the Duck surface ranges from 0.27 to 0.28, while for the Face surface it ranges from 0.24 to 0.26. Therefore, the statement is refuted.", "statement": "The Duck surface has a higher mean angular distortion than the Face surface.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: The performance of our proposed PGCP method for parameterizing the bunny model, with different number of subdomains used. Note that the welding algorithm requires at least two subdomains and hence we use n=2 as the baseline. For this reason, we define the parallel speedup to be Sn=T2Tn where Ti is the time taken with i subdomains used, and the parallel efficiency to be En=Snn/2.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/545.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The performance of our proposed PGCP method for parameterizing the bunny model, with different number of subdomains used. Note that the welding algorithm requires at least two subdomains and hence we use n=2 as the baseline. For this reason, we define the parallel speedup to be Sn=T2Tn where Ti is the time taken with i subdomains used, and the parallel efficiency to be En=Snn/2." ], "reasoning": "The table shows that as the number of subdomains increases, the parallel speedup improves, but the parallel efficiency decreases.", "description": "Table 6 shows the experimental results with 2, 3, 4, 5, 6 subdomains. Note that 2 is used as the baseline as the welding algorithm requires at least two subdomains. From the experimental result, we observe a speedup achieved by exploiting parallelization. To evaluate the parallel efficiency, we consider the ratio En = Sn n/2 , where Sn is the speedup achieved by n subdomains compared to the baseline and n/2 is the subdomain ratio. It can be observed that En is close to 1 for small n but shows a decreasing trend as n increases." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: The performance of our proposed PGCP method for parameterizing the bunny model, with different number of subdomains used. Note that the welding algorithm requires at least two subdomains and hence we use n=2 as the baseline. For this reason, we define the parallel speedup to be Sn=T2Tn where Ti is the time taken with i subdomains used, and the parallel efficiency to be En=Snn/2.'\nProblem: 'What is the parallel efficiency when using 5 subdomains?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/545.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The performance of our proposed PGCP method for parameterizing the bunny model, with different number of subdomains used. Note that the welding algorithm requires at least two subdomains and hence we use n=2 as the baseline. For this reason, we define the parallel speedup to be Sn=T2Tn where Ti is the time taken with i subdomains used, and the parallel efficiency to be En=Snn/2." ], "reasoning": "From the table, the parallel efficiency (En) for 5 subdomains is listed directly as 0.76.", "question": "What is the parallel efficiency when using 5 subdomains?", "answer": "So, the answer is {\"answer\": \"0.76\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe parallel speedup achieved with 6 subdomains is more than double the baseline.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-Other/545.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The performance of our proposed PGCP method for parameterizing the bunny model, with different number of subdomains used. Note that the welding algorithm requires at least two subdomains and hence we use n=2 as the baseline. For this reason, we define the parallel speedup to be Sn=T2Tn where Ti is the time taken with i subdomains used, and the parallel efficiency to be En=Snn/2." ], "reasoning": "The table shows that the parallel speedup (Sn) for 6 subdomains is 2.05, which is more than double the baseline speedup of 1.", "statement": "The parallel speedup achieved with 6 subdomains is more than double the baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Accuracies (in %) and perplexities for different models and scenarios. The script model substantially outperforms linguistic and base models (with p<0.001, significance tested with McNemar’s test [Everitt1992]). As expected, the human prediction model outperforms the script model (with p<0.001, significance tested by McNemar’s test).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/127.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracies (in %) and perplexities for different models and scenarios. The script model substantially outperforms linguistic and base models (with p<0.001, significance tested with McNemar’s test [Everitt1992]). As expected, the human prediction model outperforms the script model (with p<0.001, significance tested by McNemar’s test)." ], "reasoning": "The script model consistently outperforms other models in both accuracy and perplexity across 10 scenarios, although all models are weaker than humans.", "description": "See Table 3 for the averages across 10 scenarios.. As we can see from Table 3, the perplexity scores are consistent with the accuracies: the script model again outperforms other methods, and, as expected, all the models are weaker than humans." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Accuracies (in %) and perplexities for different models and scenarios. The script model substantially outperforms linguistic and base models (with p<0.001, significance tested with McNemar’s test [Everitt1992]). As expected, the human prediction model outperforms the script model (with p<0.001, significance tested by McNemar’s test).'\nProblem: 'Which model has the highest average accuracy across all scenarios?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/127.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracies (in %) and perplexities for different models and scenarios. The script model substantially outperforms linguistic and base models (with p<0.001, significance tested with McNemar’s test [Everitt1992]). As expected, the human prediction model outperforms the script model (with p<0.001, significance tested by McNemar’s test)." ], "reasoning": "To find the model with the highest average accuracy, compare the average accuracy values for each model. The human model has an average accuracy of 73.63, the script model has 62.63, the linguistic model has 49.52, and the Tily model has 31.34.", "question": "Which model has the highest average accuracy across all scenarios?", "answer": "So, the answer is {\"answer\": \"Human Model\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Tily model has the lowest average perplexity across all scenarios.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/127.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracies (in %) and perplexities for different models and scenarios. The script model substantially outperforms linguistic and base models (with p<0.001, significance tested with McNemar’s test [Everitt1992]). As expected, the human prediction model outperforms the script model (with p<0.001, significance tested by McNemar’s test)." ], "reasoning": "To verify this statement, compare the average perplexity values for each model. The human model has an average perplexity of 2.34, the script model has 3.88, the linguistic model has 7.05, and the Tily model has 23.22. The human model has the lowest average perplexity, not the Tily model.", "statement": "The Tily model has the lowest average perplexity across all scenarios.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Evaluation on topic relevance—models that generate arguments highly related with OP should be ranked high by a separately trained relevance estimation model, i.e., higher Mean Reciprocal Rank (MRR) and Precision at 1 (P@1) scores. All models trained with evidence significantly outperform seq2seq trained without evidence (approximation randomization testing, p<0.0001).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/128.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluation on topic relevance—models that generate arguments highly related with OP should be ranked high by a separately trained relevance estimation model, i.e., higher Mean Reciprocal Rank (MRR) and Precision at 1 (P@1) scores. All models trained with evidence significantly outperform seq2seq trained without evidence (approximation randomization testing, p<0.0001)." ], "reasoning": "The ranker shows significantly better MRR and P@1 scores for arguments generated from models trained with evidence compared to those generated by the SEQ2SEQ model.", "description": "Ranking metrics of MRR and Precision at 1 (P@1) are utilized, with results reported in Table 4. The ranker yields significantly better scores over arguments generated from models trained with evidence, compared to arguments generated by SEQ2SEQ model." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Evaluation on topic relevance—models that generate arguments highly related with OP should be ranked high by a separately trained relevance estimation model, i.e., higher Mean Reciprocal Rank (MRR) and Precision at 1 (P@1) scores. All models trained with evidence significantly outperform seq2seq trained without evidence (approximation randomization testing, p<0.0001).'\nProblem: 'Which model has the highest MRR score in the 'Our Decoder' category?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/128.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluation on topic relevance—models that generate arguments highly related with OP should be ranked high by a separately trained relevance estimation model, i.e., higher Mean Reciprocal Rank (MRR) and Precision at 1 (P@1) scores. All models trained with evidence significantly outperform seq2seq trained without evidence (approximation randomization testing, p<0.0001)." ], "reasoning": "To find the highest MRR score in the 'Our Decoder' category, compare the MRR scores of the models listed under 'Our Decoder MRR'. The highest score is 93.48, achieved by the '+ attend KP' model.", "question": "Which model has the highest MRR score in the 'Our Decoder' category?", "answer": "So, the answer is {\"answer\": \"93.48\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'Dec-shared' model has a higher P@1 score than the 'Dec-separate' model in the 'Standard Decoder' category.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/128.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluation on topic relevance—models that generate arguments highly related with OP should be ranked high by a separately trained relevance estimation model, i.e., higher Mean Reciprocal Rank (MRR) and Precision at 1 (P@1) scores. All models trained with evidence significantly outperform seq2seq trained without evidence (approximation randomization testing, p<0.0001)." ], "reasoning": "In the 'Standard Decoder' category, the P@1 score for the 'Dec-shared' model is 65.57, while the P@1 score for the 'Dec-separate' model is 76.74. Therefore, the statement is refuted.", "statement": "The 'Dec-shared' model has a higher P@1 score than the 'Dec-separate' model in the 'Standard Decoder' category.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Coefficients obtained from regression analysis for different models. Two NP types considered: full NP and Pronoun/ProperNoun, with base class full NP. Significance: ‘***’ <0.001, ‘**’ <0.01, ‘*’ <0.05, and ‘.’ <0.1.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/129.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Coefficients obtained from regression analysis for different models. Two NP types considered: full NP and Pronoun/ProperNoun, with base class full NP. Significance: ‘***’ <0.001, ‘**’ <0.01, ‘*’ <0.05, and ‘.’ <0.1." ], "reasoning": "The logistic regression models show no significant effect of surprisal on referring expression type, even when residualEntropy is included as a predictor.", "description": "The results of all four logistic regression models are shown in Table 5.. The results for the full dataset are fully consistent with the findings shown in Table 5: there was no significant effect of surprisal on referring expression type.. In order to replicate their settings as closely as possible, we also included residualEntropy as a predictor in our model (see last predictor in Table 5); however, this did not change the results." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Coefficients obtained from regression analysis for different models. Two NP types considered: full NP and Pronoun/ProperNoun, with base class full NP. Significance: ‘***’ <0.001, ‘**’ <0.01, ‘*’ <0.05, and ‘.’ <0.1.'\nProblem: 'What is the estimate for surprisal in the Base model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/129.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Coefficients obtained from regression analysis for different models. Two NP types considered: full NP and Pronoun/ProperNoun, with base class full NP. Significance: ‘***’ <0.001, ‘**’ <0.01, ‘*’ <0.05, and ‘.’ <0.1." ], "reasoning": "To find the estimate for surprisal in the Base model, locate the row for surprisal and the column for Estimate Base.", "question": "What is the estimate for surprisal in the Base model?", "answer": "So, the answer is {\"answer\": \"-0.131\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIncluding residualEntropy as a predictor changed the results of the logistic regression models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/129.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Coefficients obtained from regression analysis for different models. Two NP types considered: full NP and Pronoun/ProperNoun, with base class full NP. Significance: ‘***’ <0.001, ‘**’ <0.01, ‘*’ <0.05, and ‘.’ <0.1." ], "reasoning": "The description states that including residualEntropy as a predictor did not change the results, and the table shows residualEntropy as the last predictor with no significant effect.", "statement": "Including residualEntropy as a predictor changed the results of the logistic regression models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Results on different SP acquisition evaluation sets. As Keller is created based on the PP distribution and has relatively small size while SP-10K is created based on random sampling and has a much larger size, we treat the performance on SP-10K as the main evaluation metric. Spearman’s correlation between predicated plausibility and annotations are reported. The best performing models are denoted with bold font. † indicates statistical significant (p <0.005) overall baseline methods.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/130.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on different SP acquisition evaluation sets. As Keller is created based on the PP distribution and has relatively small size while SP-10K is created based on random sampling and has a much larger size, we treat the performance on SP-10K as the main evaluation metric. Spearman’s correlation between predicated plausibility and annotations are reported. The best performing models are denoted with bold font. † indicates statistical significant (p <0.005) overall baseline methods." ], "reasoning": "Our embeddings outperform other baselines on SP-10K, except for PP on the Keller dataset due to its biased distribution.", "description": "The experimental results are shown in Table 1. As Keller is created based on the PP distribution and have relatively small size while SP-10K is created based on random sampling and has a much larger size, we treat the performance on SP-10K as the major evaluation. Our embeddings significantly outperform other baselines, especially embedding based baselines. The only exception is PP on the Keller dataset due to its biased distribution. In addition, there are other interesting observations. First, compared with 'dobj' and 'nsubj', 'amod' is simpler for word2vec and GloVe. The reason behind is that conventional embeddings only capture the co-occurrence information, which is enough to predict the selectional preference of" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Results on different SP acquisition evaluation sets. As Keller is created based on the PP distribution and has relatively small size while SP-10K is created based on random sampling and has a much larger size, we treat the performance on SP-10K as the main evaluation metric. Spearman’s correlation between predicated plausibility and annotations are reported. The best performing models are denoted with bold font. † indicates statistical significant (p <0.005) overall baseline methods.'\nProblem: 'Which model has the highest average score on the SP-10K dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/130.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on different SP acquisition evaluation sets. As Keller is created based on the PP distribution and has relatively small size while SP-10K is created based on random sampling and has a much larger size, we treat the performance on SP-10K as the main evaluation metric. Spearman’s correlation between predicated plausibility and annotations are reported. The best performing models are denoted with bold font. † indicates statistical significant (p <0.005) overall baseline methods." ], "reasoning": "To find the model with the highest average score on the SP-10K dataset, compare the SP-10K average scores of all models. MWE has the highest SP-10K average score of 0.78.", "question": "Which model has the highest average score on the SP-10K dataset?", "answer": "So, the answer is {\"answer\": \"MWE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPP has the highest score on the Keller dobj task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/130.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on different SP acquisition evaluation sets. As Keller is created based on the PP distribution and has relatively small size while SP-10K is created based on random sampling and has a much larger size, we treat the performance on SP-10K as the main evaluation metric. Spearman’s correlation between predicated plausibility and annotations are reported. The best performing models are denoted with bold font. † indicates statistical significant (p <0.005) overall baseline methods." ], "reasoning": "To verify this statement, compare the Keller dobj scores of all models. PP has the highest score of 0.66 on the Keller dobj task.", "statement": "PP has the highest score on the Keller dobj task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Spearman’s correlation of different embeddings for the WS measurement. ‘nsubj’, ‘dobj’, ‘amod’ represents the embeddings of the corresponding relation and ‘center’ indicates the center embeddings. h, t, h+t, and [h,t] refer to the head, tail, sum of two embeddings, and the concatenation of them, respectively. The best scores are marked in bold fonts.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/131.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Spearman’s correlation of different embeddings for the WS measurement. ‘nsubj’, ‘dobj’, ‘amod’ represents the embeddings of the corresponding relation and ‘center’ indicates the center embeddings. h, t, h+t, and [h,t] refer to the head, tail, sum of two embeddings, and the concatenation of them, respectively. The best scores are marked in bold fonts." ], "reasoning": "Our model achieves the best overall performance, especially on nouns, and center embeddings outperform other relation-dependent embeddings.", "description": "Results are reported in Table 3 with several observations. First, our model achieves the best overall performance and significantly better on nouns, which can be explained by that nouns appear in all 9The only possible SP relation between nouns and adjectives is 'amod', while multiple SP relations could exist between nouns and verbs, and co occurrence information cannot effectively distinguish them. (2017) that three relations while most of the verbs and adjectives only appear in one or two relations. This result is promising since it is analyzed by Solovyev et al. two-thirds of the frequent words are nouns; thus there are potential benefits if our embeddings are used in downstream NLP tasks. Second, the center embeddings achieve the best performance against all the other relationdependent embeddings, which demonstrates the effectiveness of our model in learning relationdependent information over words and also enhancing their overall semantics." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Spearman’s correlation of different embeddings for the WS measurement. ‘nsubj’, ‘dobj’, ‘amod’ represents the embeddings of the corresponding relation and ‘center’ indicates the center embeddings. h, t, h+t, and [h,t] refer to the head, tail, sum of two embeddings, and the concatenation of them, respectively. The best scores are marked in bold fonts.'\nProblem: 'Which model achieves the highest overall performance?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/131.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Spearman’s correlation of different embeddings for the WS measurement. ‘nsubj’, ‘dobj’, ‘amod’ represents the embeddings of the corresponding relation and ‘center’ indicates the center embeddings. h, t, h+t, and [h,t] refer to the head, tail, sum of two embeddings, and the concatenation of them, respectively. The best scores are marked in bold fonts." ], "reasoning": "By examining the 'overall' column, the highest value is 0.48, achieved by the 'center' model with different word2vec embeddings.", "question": "Which model achieves the highest overall performance?", "answer": { "answer": "center" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'amod' model with 'h+t' word2vec embedding achieves the highest performance on adjectives.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/131.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Spearman’s correlation of different embeddings for the WS measurement. ‘nsubj’, ‘dobj’, ‘amod’ represents the embeddings of the corresponding relation and ‘center’ indicates the center embeddings. h, t, h+t, and [h,t] refer to the head, tail, sum of two embeddings, and the concatenation of them, respectively. The best scores are marked in bold fonts." ], "reasoning": "By examining the 'adjective' column, the highest value is 0.57, achieved by the 'center' model with 'h' and '[h,t]' word2vec embeddings, not 'amod' with 'h+t'.", "statement": "The 'amod' model with 'h+t' word2vec embedding achieves the highest performance on adjectives.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Comparison of MWE against language models on the WS task. Overall performance, embedding dimension, and training time (days) on a single GPU are reported.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/132.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of MWE against language models on the WS task. Overall performance, embedding dimension, and training time (days) on a single GPU are reported." ], "reasoning": "MWE outperforms ELMo and achieves comparable results with BERT, while having a smaller embedding dimension and significantly less training time.", "description": "We also compare MWE with pre-trained contextualized word embedding models in Table 4 for this task, with overall performance, embedding dimensions, and training times reported. It is observed that that MWE outperforms ELMo and achieves comparable results with BERT with smaller embedding dimension and much less training complexities." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Comparison of MWE against language models on the WS task. Overall performance, embedding dimension, and training time (days) on a single GPU are reported.'\nProblem: 'Which model has the smallest embedding dimension and how does its training time compare to the others?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/132.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of MWE against language models on the WS task. Overall performance, embedding dimension, and training time (days) on a single GPU are reported." ], "reasoning": "MWE has the smallest embedding dimension of 300. Its training time is 4.17, which is significantly less than ELMo's ≈40 and BERT's ≈300.", "question": "Which model has the smallest embedding dimension and how does its training time compare to the others?", "answer": "So, the answer is {\"answer\": \"MWE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMWE has a larger embedding dimension than ELMo.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/132.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of MWE against language models on the WS task. Overall performance, embedding dimension, and training time (days) on a single GPU are reported." ], "reasoning": "The table shows that MWE has an embedding dimension of 300, while ELMo has an embedding dimension of 512. Therefore, MWE does not have a larger embedding dimension than ELMo.", "statement": "MWE has a larger embedding dimension than ELMo.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Comparisons of different training strategies.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/133.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparisons of different training strategies." ], "reasoning": "The model with λ fixed to 1 achieves similar word similarity performance but is inferior in SP acquisition compared to other strategies.", "description": "As shown in Table 5, we compare our model with several different strategies. The first one is to put all weights to the center embedding (fix λ to 1), which never updates the local relational embeddings. As a result, it can achieve similar performance on word similarity measurement but is inferior in SP acquisition because no relationdependent information is preserved." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Comparisons of different training strategies.'\nProblem: 'Which training strategy achieves the highest Averaged SPA?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/133.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparisons of different training strategies." ], "reasoning": "By comparing the Averaged SPA values in the table, the 'Alternating optimization' strategy has the highest value of 0.775.", "question": "Which training strategy achieves the highest Averaged SPA?", "answer": "So, the answer is {\"answer\": \"Alternating optimization\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe training strategy with λ = 0 achieves the lowest Overall WS.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/133.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparisons of different training strategies." ], "reasoning": "By comparing the Overall WS values in the table, the strategy with λ = 0 has the lowest value of 0.018.", "statement": "The training strategy with λ = 0 achieves the lowest Overall WS.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Single Transformers trained to convergence on 1M WAT Ja-En, batch size 4096'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/134.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Single Transformers trained to convergence on 1M WAT Ja-En, batch size 4096" ], "reasoning": "Large batch training significantly improves the performance of single Transformers, especially for longer sequences, with an 8-batch size of 4096 yielding a 3 BLEU improvement for the linear derivation model.", "description": "results in Table 3 show that large batch training can significantly improve the performance of single Transformers, particularly when trained to produce longer sequences. Accumulating the gradient over 8 batches of size 4096 gives a 3 BLEU improvement for the linear derivation model. It has been suggested that decaying the learning rate can have a similar effect to large batch training (Smith et al., 2017), but reducing the initial learning rate by a factor of 8 alone did not give the same improvements." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Single Transformers trained to convergence on 1M WAT Ja-En, batch size 4096'\nProblem: 'What is the Test BLEU score for the Linearized derivation model when the gradient is accumulated over 8 batches?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/134.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Single Transformers trained to convergence on 1M WAT Ja-En, batch size 4096" ], "reasoning": "The table shows that for the Linearized derivation model with 8 batches per update, the Test BLEU score is 28.7.", "question": "What is the Test BLEU score for the Linearized derivation model when the gradient is accumulated over 8 batches?", "answer": { "answer": "28.7" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nReducing the initial learning rate by a factor of 8 alone gave the same improvements as large batch training.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/134.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Single Transformers trained to convergence on 1M WAT Ja-En, batch size 4096" ], "reasoning": "The description states that reducing the initial learning rate by a factor of 8 alone did not give the same improvements as large batch training.", "statement": "Reducing the initial learning rate by a factor of 8 alone gave the same improvements as large batch training.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Single models on Ja-En. Previous evaluation result included for comparison.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/135.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Single models on Ja-En. Previous evaluation result included for comparison." ], "reasoning": "Our plain BPE baseline outperforms the current best system on WAT Ja-En, and our syntax models achieve similar results despite producing longer sequences.", "description": "Our plain BPE baseline (Table 4) outperforms the current best system on WAT Ja-En, an 8-model ensemble (Morishita et al., 2017). Our syntax models achieve similar results despite producing much longer sequences." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Single models on Ja-En. Previous evaluation result included for comparison.'\nProblem: 'Which model achieved the highest Test BLEU score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/135.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Single models on Ja-En. Previous evaluation result included for comparison." ], "reasoning": "To find the model with the highest Test BLEU score, we need to compare the Test BLEU scores of all models listed in the table. The highest Test BLEU score is 29.1, achieved by the Transformer model with POS/BPE representation.", "question": "Which model achieved the highest Test BLEU score with POS/BPE?", "answer": "So, the answer is {\"answer\": \"Transformer\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Seq2seq model with Plain BPE representation achieved a higher Test BLEU score than the Transformer model with Linearized tree representation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/135.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Single models on Ja-En. Previous evaluation result included for comparison." ], "reasoning": "The Seq2seq model with Plain BPE representation has a Test BLEU score of 21.2, while the Transformer model with Linearized tree representation has a Test BLEU score of 28.4. Therefore, the statement is refuted by the table.", "statement": "The Seq2seq model with Plain BPE representation achieved a higher Test BLEU score than the Transformer model with Linearized tree representation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Ja-En Transformer ensembles: † marks significant improvement on plain BPE baseline shown in Table 4 (p<0.05 using bootstrap resampling (Koehn et al., 2007)).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/136.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ja-En Transformer ensembles: † marks significant improvement on plain BPE baseline shown in Table 4 (p<0.05 using bootstrap resampling (Koehn et al., 2007))." ], "reasoning": "Ensembles of two identical models trained with different seeds show slight improvement, while combining models producing plain BPE and linearized derivations improves by 0.5 BLEU over the plain BPE baseline.", "description": "Ensembles of two identical models trained with different seeds only slightly improve over the single model (Table 5). However, an ensemble of models producing plain BPE and linearized derivations improves by 0.5 BLEU over the plain BPE baseline." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Ja-En Transformer ensembles: † marks significant improvement on plain BPE baseline shown in Table 4 (p<0.05 using bootstrap resampling (Koehn et al., 2007)).'\nProblem: 'What is the BLEU score improvement when combining models producing plain BPE and linearized derivations compared to the plain BPE baseline?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/136.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ja-En Transformer ensembles: † marks significant improvement on plain BPE baseline shown in Table 4 (p<0.05 using bootstrap resampling (Koehn et al., 2007))." ], "reasoning": "The BLEU score for plain BPE is 29.2. The BLEU score for the combination of plain BPE and linearized derivations is 29.4. The improvement is 29.4 - 29.2 = 0.2 BLEU.", "question": "What is the BLEU score improvement when combining models producing plain BPE and linearized derivations compared to the plain BPE baseline?", "answer": "So, the answer is {\"answer\": \"0.2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCombining models producing plain BPE and linearized derivations improves the BLEU score by 0.5 over the plain BPE baseline.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/136.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ja-En Transformer ensembles: † marks significant improvement on plain BPE baseline shown in Table 4 (p<0.05 using bootstrap resampling (Koehn et al., 2007))." ], "reasoning": "The BLEU score for plain BPE is 29.2. The BLEU score for the combination of plain BPE and linearized derivations is 29.4. The improvement is 29.4 - 29.2 = 0.2 BLEU, not 0.5.", "statement": "Combining models producing plain BPE and linearized derivations improves the BLEU score by 0.5 over the plain BPE baseline.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: BLEU score variation across WMT’17 language arcs for cased (top) and uncased (bottom) BLEU. Each column varies the processing of the “online-B” system output and its references. basic denotes basic user-supplied tokenization, split adds compound splitting, unk replaces words not appearing at least twice in the training data with UNK, and metric denotes the metric-supplied tokenization used by WMT. The range row lists the difference between the smallest and largest scores, excluding unk.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/137.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: BLEU score variation across WMT’17 language arcs for cased (top) and uncased (bottom) BLEU. Each column varies the processing of the “online-B” system output and its references. basic denotes basic user-supplied tokenization, split adds compound splitting, unk replaces words not appearing at least twice in the training data with UNK, and metric denotes the metric-supplied tokenization used by WMT. The range row lists the difference between the smallest and largest scores, excluding unk." ], "reasoning": "Table 1 shows the effect of different reference tokenizations on BLEU scores, with variations up to 1.8 points, particularly influenced by case treatment.", "description": "Table 1 demonstrates the effect of computing BLEU scores with different reference tokenizations.. The changes in each column show the effect these different schemes have, as high as 1.8 for one arc, and averaging around 1.0. The biggest is the treatment of case, which is well known, yet many papers are not clear about whether they report cased or case-insensitive BLEU.. The variations in Table 1 are only some of the possible configurations, since there is no limit to the preprocessing that a group could apply." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: BLEU score variation across WMT’17 language arcs for cased (top) and uncased (bottom) BLEU. Each column varies the processing of the “online-B” system output and its references. basic denotes basic user-supplied tokenization, split adds compound splitting, unk replaces words not appearing at least twice in the training data with UNK, and metric denotes the metric-supplied tokenization used by WMT. The range row lists the difference between the smallest and largest scores, excluding unk.'\nProblem: 'What is the highest BLEU score for the English→en-ru configuration?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/137.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: BLEU score variation across WMT’17 language arcs for cased (top) and uncased (bottom) BLEU. Each column varies the processing of the “online-B” system output and its references. basic denotes basic user-supplied tokenization, split adds compound splitting, unk replaces words not appearing at least twice in the training data with UNK, and metric denotes the metric-supplied tokenization used by WMT. The range row lists the difference between the smallest and largest scores, excluding unk." ], "reasoning": "To find the highest BLEU score for the English→en-ru configuration, look at the column labeled 'English→en-ru' and identify the highest value. The highest value is 33.8 under the 'unk lc' configuration.", "question": "What is the highest BLEU score for the English→en-ru configuration?", "answer": "So, the answer is {\"answer\": \"33.8\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BLEU score for the English→en-de configuration is higher in the 'metric' configuration than in the 'basic' configuration.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/137.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: BLEU score variation across WMT’17 language arcs for cased (top) and uncased (bottom) BLEU. Each column varies the processing of the “online-B” system output and its references. basic denotes basic user-supplied tokenization, split adds compound splitting, unk replaces words not appearing at least twice in the training data with UNK, and metric denotes the metric-supplied tokenization used by WMT. The range row lists the difference between the smallest and largest scores, excluding unk." ], "reasoning": "To verify this statement, compare the BLEU scores for the 'metric' and 'basic' configurations in the 'English→en-de' column. The 'metric' configuration has a score of 26.6, which is higher than the 'basic' configuration score of 25.8.", "statement": "The BLEU score for the English→en-de configuration is higher in the 'metric' configuration than in the 'basic' configuration.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: System performance on the SPMRL and Wiki5K datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/138.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: System performance on the SPMRL and Wiki5K datasets." ], "reasoning": "The RFTokenizer achieves the highest performance on both datasets, outperforming other systems in terms of precision, recall, and F-score.", "description": "Table 2 shows the results of several systems on both datasets.7 The column '% perf' indicates the proportion of perfectly segmented super-tokens, while the next three columns indicate precision, recall and F-score for boundary detection, not including the trivial final position characters. The first baseline strategy of not segmenting anything is given in the first row, and unsurprisingly gets many cases right, but performs badly overall. A more intelligent baseline is provided by UDPipe (Straka et al. 2016; retrained on the SPMRL data), which, for super-tokens in morphologically rich languages such as Hebrew, implements a 'most common segmentation' baseline. (i.e. each super-token is given its most common segmentation from training data, forgoing segmentation for OOV items).8 Results for yap represent pure segmentation performance from the previous state of the art (More and Tsarfaty, 2016). The best two approaches in the present paper are represented next: the Extra Trees Random Forest variant,9 called RFTokenizer, is labeled RF and the DNN-based system is labeled DNN. Surprisingly, while the DNN is a close runner up, the best performance is achieved by the RFTokenizer, de. spite not having access to word embeddings. Its high performance on the SPMRL dataset makes it difficult to converge to a better solution using the DNN, though it is conceivable that substantially more data, a better feature representation and/or more hyperparameter tuning could equal or surpass the RFTokenizer's performance. Coupled with a lower cost in system resources and external dependencies, and the ability to forgo large model files to store word embeddings, we consider the RFTokenizer solution to be better given the current training data size. Performance on the out of domain dataset is encouragingly nearly as good as on SPMRL, suggesting our features are robust. This is especially clear compared to UDPipe and yap, which degrade more substantially. A key advantage of the present approach is its comparatively high precision. While other approaches have good recall, and yap approaches RFTokenizer on recall for SPMRL, RFTokenizer's reduction in spurious segmentations boosts its F-score substantially. To see why, we examine some errors in the next section, and perform feature ablations in the following one." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: System performance on the SPMRL and Wiki5K datasets.'\nProblem: 'Which system has the highest F-score on the Wiki5K dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/138.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: System performance on the SPMRL and Wiki5K datasets." ], "reasoning": "By examining the F-score column for the Wiki5K dataset, the RFTokenizer has the highest F-score of 96.35.", "question": "Which system has the highest F-score on the Wiki5K dataset?", "answer": { "answer": "RF" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe DNN system has the highest precision on the SPMRL dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/138.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: System performance on the SPMRL and Wiki5K datasets." ], "reasoning": "The table shows that the RFTokenizer has the highest precision (97.59) on the SPMRL dataset, not the DNN system.", "statement": "The DNN system has the highest precision on the SPMRL dataset.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Effects of removing features on performance, ordered by descending F-score impact on SPMRL.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/139.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effects of removing features on performance, ordered by descending F-score impact on SPMRL." ], "reasoning": "Removing lexicon lookup features significantly degrades performance, while other features have a minor impact.", "description": "Table 3 gives an overview of the impact on performance when specific features are removed: the entire lexicon, lexicon expansion, letter identity, 'vowel' features from Section 3.1, and both of the latter. Performance is high even in ablation scenarios, though we keep in mind that baselines for the task are high (e.g. 'most frequent lookup', the UDPipe strategy, achieves close to 90%). The results show the centrality of the lexicon: removing lexicon lookup features degrades performance by about 3.5% perfect accuracy, or 5.5 F-score points. All other ablations impact performance by less than 1% or 1.5 F-score points. Expanding the lexicon using Wikipedia data offers a contribution of 0.3–0.4 points, confirming the original lexicon's incompleteness.10 Looking more closely at the other features, it is surprising that identity of the letters is not crucial, as long as we have access to dictionary lookup using the letters. Nevertheless, removing letter identity impacts especially boundary recall, perhaps. does not break down drastically. The impact on Wiki5k is stronger, possibly because the necessary memorization of familiar contexts is less effective out of domain.. because some letters receive identical lookup values (e.g. single letter prepositions such as b 'in', l 'to') but have different segmentation likelihoods. The 'vowel' features, though ostensibly redundant with letter identity, help a little, causing 0.33 SPMRL F-score point degradation if removed. A" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Effects of removing features on performance, ordered by descending F-score impact on SPMRL.'\nProblem: 'What is the impact on the F-score when lexicon lookup features are removed for the SPMRL dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/139.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effects of removing features on performance, ordered by descending F-score impact on SPMRL." ], "reasoning": "The table shows that removing lexicon lookup features for the SPMRL dataset results in an F-score of 91.77, compared to the final F-score of 97.08, indicating a degradation of 5.31 points.", "question": "What is the impact on the F-score when lexicon lookup features are removed for the SPMRL dataset?", "answer": "So, the answer is {\"answer\": \"5.31\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRemoving vowel features has a significant impact on performance for the Wiki5K dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/139.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effects of removing features on performance, ordered by descending F-score impact on SPMRL." ], "reasoning": "The table shows that removing vowel features for the Wiki5K dataset results in an F-score of 96.19, compared to the final F-score of 96.35, indicating a minor degradation of 0.16 points.", "statement": "Removing vowel features has a significant impact on performance for the Wiki5K dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Maximum perturbation space size in the SST and AG News test set using word / character substitutions, which is the maximum number of forward passes per sentence to evaluate in the exhaustive verification.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/140.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Maximum perturbation space size in the SST and AG News test set using word / character substitutions, which is the maximum number of forward passes per sentence to evaluate in the exhaustive verification." ], "reasoning": "The perturbation space size increases exponentially with the perturbation radius δ, as shown for SST and AG News test sets.", "description": "The allowable input perturbation space is much larger than for word-level synonym substitutions, as shown in Table 3.. In Table 3, we show the maximum perturbation space size in the SST and AG News test set for different perturbation radii δ.. This number grows exponentially as δ increases." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Maximum perturbation space size in the SST and AG News test set using word / character substitutions, which is the maximum number of forward passes per sentence to evaluate in the exhaustive verification.'\nProblem: 'What is the perturbation space size for SST-character at δ=2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/140.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Maximum perturbation space size in the SST and AG News test set using word / character substitutions, which is the maximum number of forward passes per sentence to evaluate in the exhaustive verification." ], "reasoning": "To find the perturbation space size for SST-character at δ=2, look at the intersection of the SST-character row and the δ=2 column.", "question": "What is the perturbation space size for SST-character at δ=2?", "answer": "So, the answer is {\"answer\": \"21,116\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe perturbation space size for AG-character at δ=3 is 1,436,026.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/140.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Maximum perturbation space size in the SST and AG News test set using word / character substitutions, which is the maximum number of forward passes per sentence to evaluate in the exhaustive verification." ], "reasoning": "The table shows the perturbation space size for AG-character at δ=1 and δ=2, but there is no value provided for δ=3.", "statement": "The perturbation space size for AG-character at δ=3 is 1,436,026.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Experimental results for changes up to δ=3 and δ=2 symbols on SST and AG dataset, respectively. We compare normal training, adversarial training, data augmentation and IBP-verifiable training, using three metrics on the test set: the nominal accuracy, adversarial accuracy, and exhaustively verified accuracy (Oracle) (%).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/141.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Experimental results for changes up to δ=3 and δ=2 symbols on SST and AG dataset, respectively. We compare normal training, adversarial training, data augmentation and IBP-verifiable training, using three metrics on the test set: the nominal accuracy, adversarial accuracy, and exhaustively verified accuracy (Oracle) (%)." ], "reasoning": "Adversarial training is effective against HotFlip attacks but shows a significant drop in oracle adversarial accuracy, especially in SST-character level perturbations.", "description": "Table 1 shows the results of IBP training and baseline models under δ = 3 and δ = 24 perturbations on SST and AG News, respectively.. In Table 1, comparing adversarial accuracy with exhaustive verification accuracy (oracle), we observe that although adversarial training is effective at defending against HotFlip attacks (74.9 / 76.8 / 85.5%), the oracle adversarial accuracy under exhaustive testing (25.8 / 74.6 / 81.6%) is much lower in SST-character / SST-word / AG-character level, respectively.. In Table 1, when the perturbation space is larger (SST character-level vs. SST word-level), (a) across models, there is a larger gap in adversarial accuracy and true robustness (oracle); (b) the difference in oracle robustness between IBP and adversarial training is even larger (73.1% vs. 25.8% and 76.5% vs. 74.6%).. The resulting models achieve the highest exhaustively verified accuracy at the cost of only moderate deterioration in nominal accuracy (Table 1)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Experimental results for changes up to δ=3 and δ=2 symbols on SST and AG dataset, respectively. We compare normal training, adversarial training, data augmentation and IBP-verifiable training, using three metrics on the test set: the nominal accuracy, adversarial accuracy, and exhaustively verified accuracy (Oracle) (%).'\nProblem: 'What is the oracle adversarial accuracy for the SST-character level under adversarial training?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/141.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Experimental results for changes up to δ=3 and δ=2 symbols on SST and AG dataset, respectively. We compare normal training, adversarial training, data augmentation and IBP-verifiable training, using three metrics on the test set: the nominal accuracy, adversarial accuracy, and exhaustively verified accuracy (Oracle) (%)." ], "reasoning": "From the table, under adversarial training for SST-character level, the oracle adversarial accuracy is listed as 25.8%.", "question": "What is the oracle adversarial accuracy for the SST-character level under adversarial training?", "answer": "So, the answer is {\"answer\": \"25.8%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe verifiable (IBP) training method achieves the highest oracle adversarial accuracy for AG-character level.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/141.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Experimental results for changes up to δ=3 and δ=2 symbols on SST and AG dataset, respectively. We compare normal training, adversarial training, data augmentation and IBP-verifiable training, using three metrics on the test set: the nominal accuracy, adversarial accuracy, and exhaustively verified accuracy (Oracle) (%)." ], "reasoning": "From the table, the oracle adversarial accuracy for AG-character level under verifiable (IBP) training is 87.1%, which is the highest among all training methods listed.", "statement": "The verifiable (IBP) training method achieves the highest oracle adversarial accuracy for AG-character level.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Macro precision (P), recall (R), F1 for the binary violation prediction task (± std. dev.).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/142.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Macro precision (P), recall (R), F1 for the binary violation prediction task (± std. dev.)." ], "reasoning": "HAN slightly improves over BIGRU-ATT in F1 score and is more robust across runs, while HIER-BERT performs worse with anonymized data.", "description": "Table 2 (upper part) shows the results for binary violation. We evaluate models using macroaveraged precision (P), recall (P), F1. The weak baselines (MAJORITY, COIN-TOSS) are widely outperformed by the rest of the methods. BIGRUATT outperforms in F1 (79.5 vs. 71.8) the previous best performing method (Aletras et al., 2016) in English judicial prediction.. HAN slightly improves over BIGRU-ATT (80.5 vs. 79.5), while being more robust across runs (0.2% vs. 2.7% std. dev.). the results in Table 2 indicate that performance is comparable even when this information is masked, with the exception of HIER-BERT that has quite worse results (2%) compared to using non-anonymized data, suggesting model bias." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Macro precision (P), recall (R), F1 for the binary violation prediction task (± std. dev.).'\nProblem: 'Which model shows the highest F1 score in the non-anonymized data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/142.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Macro precision (P), recall (R), F1 for the binary violation prediction task (± std. dev.)." ], "reasoning": "The table shows that HAN has the highest F1 score of 80.5 in the non-anonymized data.", "question": "Which model shows the highest F1 score in the non-anonymized data?", "answer": { "answer": "HAN" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBIGRU-ATT has a higher F1 score than HAN in anonymized data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/142.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Macro precision (P), recall (R), F1 for the binary violation prediction task (± std. dev.)." ], "reasoning": "The table shows that in anonymized data, HAN has an F1 score of 80.2, which is higher than BIGRU-ATT's F1 score of 78.9.", "statement": "BIGRU-ATT has a higher F1 score than HAN in anonymized data.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison of labeling strategies using many-to-one mapping for target languages with available test data, using 500 clusters or number of states. Accuracy is shown in percentage points.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/143.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of labeling strategies using many-to-one mapping for target languages with available test data, using 500 clusters or number of states. Accuracy is shown in percentage points." ], "reasoning": "Clustering by type with Brown-based algorithms consistently outperforms using a sophisticated tagger such as A-HMM across all languages.", "description": "we evaluate our four labeling strategies using the many-to-one approach, as presented in Table 1.. In all cases, clustering by type with Brown-based algorithms works better than using a sophisticated tagger such as A-HMM." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison of labeling strategies using many-to-one mapping for target languages with available test data, using 500 clusters or number of states. Accuracy is shown in percentage points.'\nProblem: 'Which tagger achieved the highest score for the French language?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/143.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of labeling strategies using many-to-one mapping for target languages with available test data, using 500 clusters or number of states. Accuracy is shown in percentage points." ], "reasoning": "To find the highest score for the French language, look at the 'fr' column and identify the highest value. The highest score is 85.4, achieved by the Marlin tagger.", "question": "Which tagger achieved the highest score for the French language?", "answer": "So, the answer is {\"answer\": \"marlin\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Brown tagger achieved the highest score for the Swahili language.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/143.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of labeling strategies using many-to-one mapping for target languages with available test data, using 500 clusters or number of states. Accuracy is shown in percentage points." ], "reasoning": "To verify this statement, look at the 'sw' column and compare the scores of all taggers. The highest score is 88.81, achieved by the Marlin tagger, not the Brown tagger.", "statement": "The Brown tagger achieved the highest score for the Swahili language.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Performance of cipher grounder using brown (|C|=500) as labeler. The best PL for each CL besides itself, is shown in bold. The artificial case where we have CL POS data (PL=CL) is shown for comparison, as is the ultimately used cipher-avg method.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/144.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of cipher grounder using brown (|C|=500) as labeler. The best PL for each CL besides itself, is shown in bold. The artificial case where we have CL POS data (PL=CL) is shown for comparison, as is the ultimately used cipher-avg method." ], "reasoning": "The best performing PL for each CL shows a significant difference in accuracy compared to the PL=CL upper-bound, with the smallest gap for French (es-fr) and the largest for English (fr-en).", "description": "Table 2 presents the intrinsic performance of the cipher grounder over all PL-CL pairs considered. The difference between the best and the worst performing PL for each CL ranges from 24.62 percentage points for Swahili to 48.34 points for French, and an average difference of 34.5 points among all languages.. The case when PL=CL is also presented in Table 2 as a reference and provides a reliable upper-bound under zero-resource conditions.. It is worth noting the difference in accuracy when comparing the best performing PL for each CL with its corresponding PL=CL upper-bound.. Among all CLs, the best cipher grounder for French (es-fr) gets the closest to its upper-bound with just 4.81 percentage points of difference, followed by the English grounder (fr-en) with 13.53 points of difference.. As shown in Table 2, this model, CIPHER-AVG, obtains accuracy scores of 56.4, 58.6, 37.4, and 37.8 % for en, fr, fa, and sw, respectively.. When compared to the best performing PL for each CL (see bold cells in Table 2), it can be noticed that the performance gap ranges from just 1.2 percentage points for Swahili to 13.3 points for French, with an average of 6.1 points among all target languages." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Performance of cipher grounder using brown (|C|=500) as labeler. The best PL for each CL besides itself, is shown in bold. The artificial case where we have CL POS data (PL=CL) is shown for comparison, as is the ultimately used cipher-avg method.'\nProblem: 'What is the difference in accuracy between the best performing PL for French and its PL=CL upper-bound?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/144.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of cipher grounder using brown (|C|=500) as labeler. The best PL for each CL besides itself, is shown in bold. The artificial case where we have CL POS data (PL=CL) is shown for comparison, as is the ultimately used cipher-avg method." ], "reasoning": "The best performing PL for French is Spanish (es-fr) with an accuracy of 71.9%. The PL=CL upper-bound for French is 76.7%. The difference is 76.7% - 71.9% = 4.8 percentage points.", "question": "What is the difference in accuracy between the best performing PL for French and its PL=CL upper-bound?", "answer": "So, the answer is {\"answer\": \"4.8\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe best performing PL for Swahili has a smaller accuracy gap compared to its PL=CL upper-bound than the best performing PL for French.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/144.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of cipher grounder using brown (|C|=500) as labeler. The best PL for each CL besides itself, is shown in bold. The artificial case where we have CL POS data (PL=CL) is shown for comparison, as is the ultimately used cipher-avg method." ], "reasoning": "The best performing PL for Swahili is Russian (ru-sw) with an accuracy of 38.96%, and the PL=CL upper-bound is 69.4%, resulting in a gap of 69.4% - 38.96% = 30.44 percentage points. For French, the best performing PL is Spanish (es-fr) with an accuracy of 71.9%, and the PL=CL upper-bound is 76.7%, resulting in a gap of 76.7% - 71.9% = 4.8 percentage points. Therefore, the statement is refuted.", "statement": "The best performing PL for Swahili has a smaller accuracy gap compared to its PL=CL upper-bound than the best performing PL for French.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison of performance over the NOUN tag, as measured by precision (P), recall (R), and F1 scores, between our combined cipher grounder (cipher-avg) and a supervised tagger.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/145.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of performance over the NOUN tag, as measured by precision (P), recall (R), and F1 scores, between our combined cipher grounder (cipher-avg) and a supervised tagger." ], "reasoning": "CIPHER-AVG shows mid to low accuracy but achieves mid-range F1-scores for all languages, with particularly optimistic recall for English and French, and precision for Farsi and Swahili.", "description": "Let us now compare the performance of CIPHER-AVG with that of a vanilla supervised neural model.11 Table 3 shows precision, recall, and F1 scores for the NOUN tag.. Even though CIPHER-AVG achieved mixed results (mid to low accuracy), the model robustly achieves mid-range performance according to F1-score for all CLs.. The results are even more optimistic in terms of recall for English and French, and in terms of precision for Farsi and Swahili." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Comparison of performance over the NOUN tag, as measured by precision (P), recall (R), and F1 scores, between our combined cipher grounder (cipher-avg) and a supervised tagger.'\nProblem: 'Which language has the highest precision score for the supervised model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/145.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of performance over the NOUN tag, as measured by precision (P), recall (R), and F1 scores, between our combined cipher grounder (cipher-avg) and a supervised tagger." ], "reasoning": "To find the highest precision score for the supervised model, compare the 'Supervised P' column for all languages. The highest value is 98.88 for sw.", "question": "Which language has the highest precision score for the supervised model?", "answer": "So, the answer is {\"answer\": \"sw\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe CIPHER-AVG model has a higher recall score for French than for Farsi.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/145.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of performance over the NOUN tag, as measured by precision (P), recall (R), and F1 scores, between our combined cipher grounder (cipher-avg) and a supervised tagger." ], "reasoning": "According to the table, the recall score for CIPHER-AVG in French is 78.82, while for Farsi it is 51.23. Therefore, the statement is supported.", "statement": "The CIPHER-AVG model has a higher recall score for French than for Farsi.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Impact of grounded unsupervised POS tagging on MaLOPa’s ‘zero-resource’ condition. Bold entries indicate an improvement over the baseline condition of having no POS tag information (beyond punctuation)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/146.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Impact of grounded unsupervised POS tagging on MaLOPa’s ‘zero-resource’ condition. Bold entries indicate an improvement over the baseline condition of having no POS tag information (beyond punctuation)" ], "reasoning": "Removing POS tags except for punctuation significantly drops scores across all languages. Using cipher tags improves LAS for all languages and UAS for all but one language (de).", "description": "Likewise, the utility of CIPHER-AVG tags for dependency parsing under zero-resource scenarios is summarized in Table 4 and Table 5.. We first analyze the effect of POS tag information at test time for the MALOPA setup in Table 4.. First we remove all POS signal except trivial punctuation information (NONE row), and, predictably, the scores drop significantly across all target languages. Then, we use our cipher tags (CIPHER row) and see improvements for all languages in LAS and for all but one language in UAS (de)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Impact of grounded unsupervised POS tagging on MaLOPa’s ‘zero-resource’ condition. Bold entries indicate an improvement over the baseline condition of having no POS tag information (beyond punctuation)'\nProblem: 'What is the UAS score for the 'none' row in the 'es' language, and how does it compare to the 'cipher' row?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/146.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Impact of grounded unsupervised POS tagging on MaLOPa’s ‘zero-resource’ condition. Bold entries indicate an improvement over the baseline condition of having no POS tag information (beyond punctuation)" ], "reasoning": "The UAS score for 'none' in 'es' is 43.82. The UAS score for 'cipher' in 'es' is 55.56, which is higher.", "question": "What is the UAS score for the 'none' row in the 'es' language, and how does it compare to the 'cipher' row?", "answer": "So, the answer is {\"answer\": \"55.56\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LAS score for 'cipher' tags in the 'fr' language is higher than the LAS score for 'none' tags in the 'fr' language.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/146.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Impact of grounded unsupervised POS tagging on MaLOPa’s ‘zero-resource’ condition. Bold entries indicate an improvement over the baseline condition of having no POS tag information (beyond punctuation)" ], "reasoning": "The LAS score for 'cipher' tags in 'fr' is 41.04, which is higher than the LAS score for 'none' tags in 'fr', which is 30.91.", "statement": "The LAS score for 'cipher' tags in the 'fr' language is higher than the LAS score for 'none' tags in the 'fr' language.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Changing to unsupervised muse embeddings boosts MaLOPa’s zero-resource performance significantly (bold entries), in many cases doing so even without any POS tag information (italic entries), however noisy decipherment-based POS tags are no longer helpful.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/147.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Changing to unsupervised muse embeddings boosts MaLOPa’s zero-resource performance significantly (bold entries), in many cases doing so even without any POS tag information (italic entries), however noisy decipherment-based POS tags are no longer helpful." ], "reasoning": "The utility of CIPHER-AVG tags for dependency parsing under zero-resource scenarios is summarized, showing that POS signal improves performance greatly for all languages when using MUSE embeddings, although some languages benefit more from POS information than others.", "description": "Likewise, the utility of CIPHER-AVG tags for dependency parsing under zero-resource scenarios is summarized in Table 4 and Table 5.. We then take the next logical step and remove the parallel data-grounded embeddings, replacing them with fully unsupervised MUSE embeddings. Table 5 summarizes these results.. It can be observed that POS signal improves performance greatly for all languages when using MUSE embeddings.. Here we note a mixed result: whilst de, sv, and it do benefit from POS information, the other languages do not, obtaining great improvements from MUSE embed. dings instead.. Finally, consider MUSE-CIPHER (gold POS tags during training, cipher tags during testing). When compared to MUSE-NONE setup, it can be observed that, unfortunately, the heuristic POS tagger is too noisy and gets in MUSE's way." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Changing to unsupervised muse embeddings boosts MaLOPa’s zero-resource performance significantly (bold entries), in many cases doing so even without any POS tag information (italic entries), however noisy decipherment-based POS tags are no longer helpful.'\nProblem: 'Which embedding and test tag combination resulted in the highest UAS score for the French language?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/147.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Changing to unsupervised muse embeddings boosts MaLOPa’s zero-resource performance significantly (bold entries), in many cases doing so even without any POS tag information (italic entries), however noisy decipherment-based POS tags are no longer helpful." ], "reasoning": "To find the highest UAS score for the French language, look at the 'fr UAS' column and identify the highest value. The highest UAS score for French is 80.86, which corresponds to the 'muse' embedding with 'gold' test tags.", "question": "Which embedding with gold test tags resulted in the highest UAS score for the French language?", "answer": "So, the answer is {\"answer\": \"muse\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe MUSE embedding with gold test tags resulted in the highest LAS score for the German language.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/147.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Changing to unsupervised muse embeddings boosts MaLOPa’s zero-resource performance significantly (bold entries), in many cases doing so even without any POS tag information (italic entries), however noisy decipherment-based POS tags are no longer helpful." ], "reasoning": "To verify this statement, look at the 'de LAS' column and identify the highest value. The highest LAS score for German is 56.28, which corresponds to the 'muse' embedding with 'gold' test tags.", "statement": "The MUSE embedding with gold test tags resulted in the highest LAS score for the German language.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Micro precision, recall, F1 in multi-label violation for all, frequent, and few training instances.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/148.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Micro precision, recall, F1 in multi-label violation for all, frequent, and few training instances." ], "reasoning": "HIER-BERT outperforms BIGRU-ATT and LWAN in overall micro-F1 scores, demonstrating superior performance in multi-label violation prediction.", "description": "Table 3 reports micro-averaged precision (P), recall (R), and F1 results for all methods, now including LWAN, in multi-label violation prediction. The results are also grouped by label frequency for all (OVERALL), FREQUENT, and FEW labels (articles), counting frequencies on the training subset.. We observe that predicting specific articles that have been violated is a much more difficult task than predicting if any article has been violated in a binary setup (cf. Table 2). Overall, HIER-BERT outperforms BIGRU-ATT and LWAN (60.0 vs. 57.. micro-F1), which is tailored for multi-labeling tasks, while being comparable with HAN (60.0 vs. 59.9 micro-F1). All models under-perform in labels with FEW training examples, demonstrating the difficulty of few-shot learning in ECHR legal judgment prediction." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Micro precision, recall, F1 in multi-label violation for all, frequent, and few training instances.'\nProblem: 'Which model has the highest overall precision (P) score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/148.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Micro precision, recall, F1 in multi-label violation for all, frequent, and few training instances." ], "reasoning": "To find the model with the highest overall precision score, we need to compare the precision (P) scores of all models listed under 'overall (all labels)'. HIER-BERT has the highest precision score of 65.9.", "question": "Which model has the highest overall precision (P) score?", "answer": "So, the answer is {\"answer\": \"HIER-BERT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nHAN has the highest recall (R) score for frequent labels.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/148.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Micro precision, recall, F1 in multi-label violation for all, frequent, and few training instances." ], "reasoning": "To verify this statement, we need to compare the recall (R) scores for frequent labels among all models. HAN has a recall score of 57.0, which is lower than HIER-BERT's recall score of 56.5.", "statement": "HAN has the highest recall (R) score for frequent labels.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Historical datasets used in the experiments'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/149.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Historical datasets used in the experiments" ], "reasoning": "Table 1 provides historical datasets covering eight languages from different families and genres across various time periods.", "description": "Table 1 gives an overview of the historical datasets.. covering eight languages from different language families—English, German, Hungarian, Icelandic, Spanish, Portuguese, Slovene, and Swedish—as well as different text genres and time periods." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Historical datasets used in the experiments'\nProblem: 'Which dataset has the highest number of tokens for training?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/149.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Historical datasets used in the experiments" ], "reasoning": "To find the dataset with the highest number of tokens for training, compare the 'Tokens Train' column for all datasets. The Portuguese dataset has the highest number of tokens for training with 222,525 tokens.", "question": "Which dataset has the highest number of tokens for training?", "answer": "So, the answer is {\"answer\": \"Portuguese\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe German (RIDGES) dataset has more tokens for training than the English dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/149.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Historical datasets used in the experiments" ], "reasoning": "The German (RIDGES) dataset has 41,857 tokens for training, while the English dataset has 147,826 tokens for training. Therefore, the statement is refuted.", "statement": "The German (RIDGES) dataset has more tokens for training than the English dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Word accuracy of different normalization methods on the test sets of the historical datasets, in percent; best result for each dataset in bold; results marked with an asterisk (*) are not significantly different from the best result using McNemar’s test at p<0.05. † indicates scores that were not (re)produced here, but reported in previous work; they might not be strictly comparable due to differences in data preprocessing (cf. Sec. 3). Additionally, Identity shows the accuracy when leaving all word forms unchanged, while Maximum gives the theoretical maximum accuracy with purely token-level methods.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/150.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Word accuracy of different normalization methods on the test sets of the historical datasets, in percent; best result for each dataset in bold; results marked with an asterisk (*) are not significantly different from the best result using McNemar’s test at p<0.05. † indicates scores that were not (re)produced here, but reported in previous work; they might not be strictly comparable due to differences in data preprocessing (cf. Sec. 3). Additionally, Identity shows the accuracy when leaving all word forms unchanged, while Maximum gives the theoretical maximum accuracy with purely token-level methods." ], "reasoning": "The extent of spelling variation varies greatly between datasets, with less than 15% of tokens requiring normalization (SLG) to more than 80% (HU). The deep NMT model by Tang et al. (2018) consistently outperforms the shallow one by Bollmann (2018).", "description": "Table 2 shows the results of this evaluation. The extent of spelling variation varies. greatly between datasets, with less than 15% of tokens requiring normalization (SLG) to more than 80% (HU). The maximum accuracy is above 97% for most datasets,. For the normalization systems, we observe significantly better word accuracy with SMT than NMT on four of the datasets, and non-significant differences on five others. There is only one dataset (DEA) where the NMT system by Tang et al. (2018) gets significantly better word accuracy than other systems.. Overall, the deep NMT model by Tang et al. (2018) consistently outperforms the shallow one by Bollmann (2018).. Finally, while Norma does produce competitive results on sev. eral datasets (particularly in the \"combined\" setting), it is generally significantly behind the SMT and NMT methods." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Word accuracy of different normalization methods on the test sets of the historical datasets, in percent; best result for each dataset in bold; results marked with an asterisk (*) are not significantly different from the best result using McNemar’s test at p<0.05. † indicates scores that were not (re)produced here, but reported in previous work; they might not be strictly comparable due to differences in data preprocessing (cf. Sec. 3). Additionally, Identity shows the accuracy when leaving all word forms unchanged, while Maximum gives the theoretical maximum accuracy with purely token-level methods.'\nProblem: 'Which dataset has the highest word accuracy for the NMT (Tang et al., 2018) method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/150.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Word accuracy of different normalization methods on the test sets of the historical datasets, in percent; best result for each dataset in bold; results marked with an asterisk (*) are not significantly different from the best result using McNemar’s test at p<0.05. † indicates scores that were not (re)produced here, but reported in previous work; they might not be strictly comparable due to differences in data preprocessing (cf. Sec. 3). Additionally, Identity shows the accuracy when leaving all word forms unchanged, while Maximum gives the theoretical maximum accuracy with purely token-level methods." ], "reasoning": "To find the dataset with the highest word accuracy for the NMT (Tang et al., 2018) method, look at the row corresponding to 'NMT (Tang et al., 2018)' and identify the highest value across all datasets.", "question": "Which dataset has the highest word accuracy for the NMT (Tang et al., 2018) method?", "answer": { "answer": "SLG" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe cSMTiser+LM method achieves higher accuracy than the NMT (Tang et al., 2018) method on Dataset EN.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/150.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Word accuracy of different normalization methods on the test sets of the historical datasets, in percent; best result for each dataset in bold; results marked with an asterisk (*) are not significantly different from the best result using McNemar’s test at p<0.05. † indicates scores that were not (re)produced here, but reported in previous work; they might not be strictly comparable due to differences in data preprocessing (cf. Sec. 3). Additionally, Identity shows the accuracy when leaving all word forms unchanged, while Maximum gives the theoretical maximum accuracy with purely token-level methods." ], "reasoning": "Compare the word accuracy values for the cSMTiser+LM method and the NMT (Tang et al., 2018) method on Dataset EN. The cSMTiser+LM method has an accuracy of 95.24, while the NMT (Tang et al., 2018) method has an accuracy of 95.24.", "statement": "The cSMTiser+LM method achieves higher accuracy than the NMT (Tang et al., 2018) method on Dataset EN.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Mean Absolute Error and Spearman’s ρ for case importance. Importance ranges from 1 (most important) to 4 (least). * Not Applicable.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/151.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Mean Absolute Error and Spearman’s ρ for case importance. Importance ranges from 1 (most important) to 4 (least). * Not Applicable." ], "reasoning": "HIER-BERT outperforms HAN by 17% and has the best ρ (.527), indicating a moderate positive correlation, which is not the case for the rest of the methods.", "description": "BOW-SVR performs worse than BIGRU-ATT, while HAN is 10% and 3% better, respectively. HIER-BERT further improves the results, outperforming HAN by 17%.. HIER-BERT has the best ρ (.527), indicating a moderate positive correlation (> 0.5), which is not the case for the rest of the methods." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Mean Absolute Error and Spearman’s ρ for case importance. Importance ranges from 1 (most important) to 4 (least). * Not Applicable.'\nProblem: 'Which method has the highest Spearman's ρ and by how much does it outperform HAN?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/151.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Mean Absolute Error and Spearman’s ρ for case importance. Importance ranges from 1 (most important) to 4 (least). * Not Applicable." ], "reasoning": "The table shows that HIER-BERT has the highest Spearman's ρ at .527. The description states that HIER-BERT outperforms HAN by 17%.", "question": "Which method has the highest Spearman's ρ?", "answer": "{\"answer\": \"HIER-BERT%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nHAN has a higher Spearman's ρ than BIGRU-ATT.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/151.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Mean Absolute Error and Spearman’s ρ for case importance. Importance ranges from 1 (most important) to 4 (least). * Not Applicable." ], "reasoning": "The table shows that HAN has a Spearman's ρ of .437, while BIGRU-ATT has a Spearman's ρ of .459. Therefore, HAN does not have a higher Spearman's ρ than BIGRU-ATT.", "statement": "HAN has a higher Spearman's ρ than BIGRU-ATT.", "answer": "{\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: 2015 & 2017 test set Smatch scores'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/152.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: 2015 & 2017 test set Smatch scores" ], "reasoning": "The fixed-tree decoder performs well with either edge model, while the projective decoder's performance drops with the K&G edge scores. The type-unaware baseline has low recall, and our models outperform the JAMR-style baseline significantly.", "description": "Table 1 shows the Smatch scores (Cai and Knight, 2013) of our models, compared to a selection of previously published results.. The fixed-tree decoder seems to work well with either edge model, but performance of the projective decoder drops with the K&G edge scores.. As expected, the type-unaware baseline has low recall,. The fact that our models outperform the JAMR-style baseline so clearly is an indication that" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: 2015 & 2017 test set Smatch scores'\nProblem: 'Which model had the highest Smatch score in 2017?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/152.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: 2015 & 2017 test set Smatch scores" ], "reasoning": "To find the highest Smatch score in 2017, compare the scores of all models listed under the 2017 column. The highest score is 71.0, achieved by the 'local edge + projective decoder' model.", "question": "Which model had the highest Smatch score in 2017?", "answer": { "answer": "local edge + projective decoder" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe fixed-tree decoder with the K&G edge model had a higher score in 2017 than in 2015.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/152.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: 2015 & 2017 test set Smatch scores" ], "reasoning": "The table shows that the fixed-tree decoder with the K&G edge model had a score of 69.6 in 2015 and 69.9 in 2017, indicating an increase.", "statement": "The fixed-tree decoder with the K&G edge model had a higher score in 2017 than in 2015.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Details for the LDC2015E86 and LDC2017T10 test sets'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/153.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Details for the LDC2015E86 and LDC2017T10 test sets" ], "reasoning": "Table 2 compares the performance of two systems (PD and FTD) with other AMR parsers on datasets from 2015 and 2017, highlighting good scores in reentrancy identification.", "description": "Table 2 analyzes the performance of our two best systems (PD = projective, FTD = fixed-tree) in more detail,. and compares them to Wang's, Flanigan's, and Damonte's AMR parsers on the 2015 set and , and van Noord and Bos (2017b) for the 2017 dataset.. The good scores we achieve on reentrancy identification," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Details for the LDC2015E86 and LDC2017T10 test sets'\nProblem: 'Which system achieved the highest score in the Named Ent. metric for the 2015 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/153.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Details for the LDC2015E86 and LDC2017T10 test sets" ], "reasoning": "To find the highest score in the Named Ent. metric for the 2015 dataset, compare the scores of all systems listed under the 2015 columns. The highest score is 83, achieved by the 2015 D'17 system.", "question": "Which system achieved the highest score in the Named Ent. metric for the 2015 dataset?", "answer": "So, the answer is {\"answer\": \"2015 D'17\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 2017 PD system achieved the highest score in the Concepts metric for the 2017 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/153.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Details for the LDC2015E86 and LDC2017T10 test sets" ], "reasoning": "To verify this statement, compare the Concepts scores for the 2017 dataset. The 2017 PD system scored 84, which is the highest among the listed systems.", "statement": "The 2017 PD system achieved the highest score in the Concepts metric for the 2017 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Main results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/154.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Main results." ], "reasoning": "The 'All' system has the highest scores in both Dev and Test sets.", "description": "Over the rules used on the 1-best result, more than 30% are non-terminal rules,. On the other hand, 30% are glue rules.. Finally, terminal rules take the largest percentage," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Main results.'\nProblem: 'Which system has the highest score in the Test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/154.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Main results." ], "reasoning": "By comparing the Test scores of all systems, the 'All' system has the highest score of 25.62.", "question": "Which system has the highest score in the Test set?", "answer": { "answer": "All" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe NoInducedRule system has the lowest score in the Dev set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/154.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Main results." ], "reasoning": "By comparing the Dev scores of all systems, the NoInducedRule system has the lowest score of 16.75.", "statement": "The NoInducedRule system has the lowest score in the Dev set.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Rules used for decoding.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/155.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Rules used for decoding." ], "reasoning": "More than 30% of the rules used on the 1-best result are non-terminal rules, 30% are glue rules, and terminal rules take the largest percentage at 39.9%.", "description": "Over the rules used on the 1-best result, more than 30% are non-terminal rules.. On the other hand, 30% are glue rules.. Finally, terminal rules take the largest percentage" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Rules used for decoding.'\nProblem: 'What percentage of the rules used on the 1-best result are terminal rules?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/155.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Rules used for decoding." ], "reasoning": "The table shows that terminal rules take the largest percentage at 39.9%.", "question": "What percentage of the rules used on the 1-best result are terminal rules?", "answer": { "answer": "39.9%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nNon-terminal rules make up the largest percentage of the rules used on the 1-best result.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/155.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Rules used for decoding." ], "reasoning": "The table shows that terminal rules take the largest percentage at 39.9%, while non-terminal rules are 30.1%.", "statement": "Non-terminal rules make up the largest percentage of the rules used on the 1-best result.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Label distribution of train, dev, and test set'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/156.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Label distribution of train, dev, and test set" ], "reasoning": "The description indicates that the dev set has roughly 5% for each emotion category, which is used as the empirical estimation to avoid overfitting, while the train set's sample distribution is used as Ptr(xtr i).", "description": "According to the description in (CodaLab, 2019), the label distribution for dev and test sets are roughly 4% for each of the emotions. However, from the dev set (Table 2) we know that the proportions of each of the emotion categories are better described as %5 each, thereby we use %5 as the empirical estimation of distribution Pte(xtr i ). We did not use the exact proportion of dev set as the estimation to prevent the overfitting towards dev set. The sample distribution of the train set is used as Ptr(xtr i )." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Label distribution of train, dev, and test set'\nProblem: 'What is the percentage of the 'others' category in the test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/156.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Label distribution of train, dev, and test set" ], "reasoning": "From the table, the 'others' category in the test set is listed as 85.70%.", "question": "What is the percentage of the 'others' category in the test set?", "answer": "So, the answer is {\"answer\": \"85.70%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe dev set has a higher percentage of 'happy' emotions compared to the test set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/156.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Label distribution of train, dev, and test set" ], "reasoning": "According to the table, the dev set has 5.15% 'happy' emotions, while the test set has 4.28% 'happy' emotions, indicating that the dev set has a higher percentage.", "statement": "The dev set has a higher percentage of 'happy' emotions compared to the test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Macro-F1 scores and its harmonic means of the four models'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/157.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Macro-F1 scores and its harmonic means of the four models" ], "reasoning": "The HRLCE model outperforms others, with the highest accuracy for the emotion Sad and the lowest for Happy. Performance on the dev set is generally better than on the test set.", "description": "The results are shown in Table 1. It shows that the proposed HRLCE model performs the best. The performance of SLD and SL are very close to each other, on the dev set, SLD performs better than SL but they have almost the same overall scores on the test set. The MacroF1 scores of each emotion category are very different from each other: the classification accuracy for emotion Sad is the highest in most of the cases, while the emotion Happy is the least accurately classified by all the models. We also noticed that the performance on the dev set is generally slightly better than that on the test set." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Macro-F1 scores and its harmonic means of the four models'\nProblem: 'Which model has the highest Harmonic Mean score on the test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/157.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Macro-F1 scores and its harmonic means of the four models" ], "reasoning": "By comparing the Harmonic Mean scores on the test set, HRLCE has the highest score of 0.7666.", "question": "Which model has the highest Harmonic Mean score on the test set?", "answer": "So, the answer is {\"answer\": \"HRLCE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe SLD model performs better than the SL model on the dev set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/157.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Macro-F1 scores and its harmonic means of the four models" ], "reasoning": "The table shows that the SLD model has a higher Harmonic Mean score (0.7112) compared to the SL model (0.7016) on the dev set.", "statement": "The SLD model performs better than the SL model on the dev set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: The intrinsic evaluation results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/158.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The intrinsic evaluation results." ], "reasoning": "Our alignment method outperforms the JAMR aligner by achieving higher scores in both Alignment F1 and Oracle's Smatch.", "description": "Table 3 shows the intrinsic evaluation results, in which our alignment intrinsically outperforms JAMR aligner by achieving better alignment F1 score and leading to a higher scored oracle parser." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: The intrinsic evaluation results.'\nProblem: 'What is the difference in Oracle's Smatch score between our aligner and the JAMR aligner?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/158.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The intrinsic evaluation results." ], "reasoning": "The Oracle's Smatch score for our aligner is 94.7, while for the JAMR aligner it is 91.7. The difference is 94.7 - 91.7 = 3.0.", "question": "What is the difference in Oracle's Smatch score between our aligner and the JAMR aligner?", "answer": { "answer": "3.0" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe JAMR aligner has a higher Alignment F1 score than our aligner.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/158.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The intrinsic evaluation results." ], "reasoning": "The table shows that the Alignment F1 score for the JAMR aligner is 90.6, while for our aligner it is 95.2. Therefore, the statement is refuted.", "statement": "The JAMR aligner has a higher Alignment F1 score than our aligner.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: The parsing results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/159.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The parsing results." ], "reasoning": "The alignment improves the performance of both JAMR and CAMR parsers, with improvements ranging from 0.5 to 1.7.", "description": "Table 4 shows the results. From this table, we can see that our alignment consistently improves all the parsers by a margin ranging from 0.5 to 1.7." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: The parsing results.'\nProblem: 'What is the improvement in the 'all' category for the JAMR parser when using 'Our aligner' compared to 'JAMR aligner'?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/159.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The parsing results." ], "reasoning": "The 'all' category score for the JAMR parser with 'JAMR aligner' is 65.9, and with 'Our aligner' it is 67.6. The improvement is 67.6 - 65.9 = 1.7.", "question": "What is the improvement in the 'all' category for the JAMR parser when using 'Our aligner' compared to 'JAMR aligner'?", "answer": "So, the answer is {\"answer\": \"1.7\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe CAMR parser with 'Our aligner' has a higher score in the 'newswire' category than with 'JAMR aligner'.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/159.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The parsing results." ], "reasoning": "The 'newswire' category score for the CAMR parser with 'JAMR aligner' is 68.4, and with 'Our aligner' it is 68.8. Since 68.8 is higher than 68.4, the statement is supported.", "statement": "The CAMR parser with 'Our aligner' has a higher score in the 'newswire' category than with 'JAMR aligner'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: The parsing results. xn denotes the ensemble of n differently initialized parsers. The difference in rounding is due to previous works report differently rounded results. † BA17 represents the result of Ballesteros and Al-Onaizan (2017), ‡ Damonte et al. (2017)’s result is drawn from Ballesteros and Al-Onaizan (2017).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/160.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The parsing results. xn denotes the ensemble of n differently initialized parsers. The difference in rounding is due to previous works report differently rounded results. † BA17 represents the result of Ballesteros and Al-Onaizan (2017), ‡ Damonte et al. (2017)’s result is drawn from Ballesteros and Al-Onaizan (2017)." ], "reasoning": "The ensemble parser significantly improves performance, with more parsers ensembled leading to greater improvements.", "description": "Table 6 shows the performance of our transitionbased parser along with comparison to the parsers in the previous works.. The second block in Table 6 shows the results of our ensemble parser, in which ensemble significantly improves the performance and more parsers ensembled, more improvements are achieved." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: The parsing results. xn denotes the ensemble of n differently initialized parsers. The difference in rounding is due to previous works report differently rounded results. † BA17 represents the result of Ballesteros and Al-Onaizan (2017), ‡ Damonte et al. (2017)’s result is drawn from Ballesteros and Al-Onaizan (2017).'\nProblem: 'What is the performance of the ensemble parser with Word, POS, and Our aligner when ensembled x10?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/160.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The parsing results. xn denotes the ensemble of n differently initialized parsers. The difference in rounding is due to previous works report differently rounded results. † BA17 represents the result of Ballesteros and Al-Onaizan (2017), ‡ Damonte et al. (2017)’s result is drawn from Ballesteros and Al-Onaizan (2017)." ], "reasoning": "The table shows that the performance of the ensemble parser with Word, POS, and Our aligner when ensembled x10 is 68.4.", "question": "What is the performance of the ensemble parser with Word, POS, and Our aligner when ensembled x10?", "answer": "So, the answer is {\"answer\": \"68.4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ensemble parser with Word only and Our aligner achieves a performance of 67.4 when ensembled x3.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/160.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The parsing results. xn denotes the ensemble of n differently initialized parsers. The difference in rounding is due to previous works report differently rounded results. † BA17 represents the result of Ballesteros and Al-Onaizan (2017), ‡ Damonte et al. (2017)’s result is drawn from Ballesteros and Al-Onaizan (2017)." ], "reasoning": "The table shows that the ensemble parser with Word only and Our aligner achieves a performance of 67.4 when ensembled x3.", "statement": "The ensemble parser with Word only and Our aligner achieves a performance of 67.4 when ensembled x3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: SQA test results. † marks contextual models using the previous question or the answer to the previous question. * marks the models that use the table content. RA denotes an oracle model that has access to the previous reference answer at test time. ALL is the average question accuracy, SEQ the sequence accuracy, and POS X, the accuracy of the X’th question in a sequence.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/161.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: SQA test results. † marks contextual models using the previous question or the answer to the previous question. * marks the models that use the table content. RA denotes an oracle model that has access to the previous reference answer at test time. ALL is the average question accuracy, SEQ the sequence accuracy, and POS X, the accuracy of the X’th question in a sequence." ], "reasoning": "Our model improves the SOTA question accuracy from 45.6% to 55.1%, outperforms FP by 20% on POS3, and shows a significant improvement when previous reference answers are provided.", "description": "We compare our model to Float Parser (FP) (Pasupat and Liang, 2015), Neural Programmer (NP) (Neelakantan et al., 2016b), DYNSP (Iyyer et al., 2017) and CAMP (Sun et al., 2018b) in Table 1.. We observe that our model improves the SOTA from 45.6% by CAMP to 55.1% in question accuracy (ALL), reducing the relative error rate by 18%. For the initial question (POS1), however, it is behind DYNSP by 3.7%. More interestingly, our model handles follow up questions especially well outperforming the previously best model FP by 20% on POS3, a 28% relative error reduction.. We observe that our model effectively leverages the context information by improving the average question accuracy from 45.1% to 55.1% in comparison to the use of context in DYNSP yielding 2.7% improvement.. If we provide the previous reference answers, the average question accuracy jumps to 61.7%, showing that 6.6% of the errors are due to error propagation." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: SQA test results. † marks contextual models using the previous question or the answer to the previous question. * marks the models that use the table content. RA denotes an oracle model that has access to the previous reference answer at test time. ALL is the average question accuracy, SEQ the sequence accuracy, and POS X, the accuracy of the X’th question in a sequence.'\nProblem: 'What is the improvement in question accuracy (ALL) when comparing our model to CAMP?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/161.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: SQA test results. † marks contextual models using the previous question or the answer to the previous question. * marks the models that use the table content. RA denotes an oracle model that has access to the previous reference answer at test time. ALL is the average question accuracy, SEQ the sequence accuracy, and POS X, the accuracy of the X’th question in a sequence." ], "reasoning": "CAMP has an ALL accuracy of 45.6%, while our model has an ALL accuracy of 55.1%. The improvement is calculated as 55.1% - 45.6%.", "question": "What is the improvement in question accuracy (ALL) when comparing our model to CAMP?", "answer": "So, the answer is {\"answer\": \"9.5%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur model outperforms FP by 20% on POS3.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/161.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: SQA test results. † marks contextual models using the previous question or the answer to the previous question. * marks the models that use the table content. RA denotes an oracle model that has access to the previous reference answer at test time. ALL is the average question accuracy, SEQ the sequence accuracy, and POS X, the accuracy of the X’th question in a sequence." ], "reasoning": "The table shows that FP has a POS3 accuracy of 25.9%, while our model has a POS3 accuracy of 46.8%. The difference is 46.8% - 25.9%, which is 20.9%, approximately 20%.", "statement": "Our model outperforms FP by 20% on POS3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: ESE performance (p@k). Best performing combination is bold faced.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/162.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: ESE performance (p@k). Best performing combination is bold faced." ], "reasoning": "The best results are achieved using TFIDF (Eq.5) for edge weighting and context-dependent similarity (Eq.8) for ranking NPs, showing the importance of feature overlap between noun phrases.", "description": "We designed ESE to output thirty candidate entities (NPs) ranked based on the similarity to the seed term. Therefore, we calculated precision at k (p@k) where k is always 30.. Table 2 shows the best results when using the feature ensemble method which is more stable than the non-ensemble one (due to lower standard deviation and non-zero precision). According to the results, the best combination in terms of the mean and standard deviation is obtained when using TFIDF (Eq.5) to weigh the edges and context-dependent similarity (Eq.8) to rank NPs. This shows that the uniqueness and the significant overlap of features between noun phrases were very important." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: ESE performance (p@k). Best performing combination is bold faced.'\nProblem: 'What is the highest precision score for Seed 1 using Eq. 9?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/162.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: ESE performance (p@k). Best performing combination is bold faced." ], "reasoning": "To find the highest precision score for Seed 1 using Eq. 9, look at the row corresponding to Seed 1 and Eq. 9. The highest value in this row is 0.73.", "question": "What is the highest precision score for Seed 1 using Eq. 9?", "answer": { "answer": "0.73" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe precision score for Seed 2 using Eq. 8 for the 'Person' category is higher than 0.5.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/162.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: ESE performance (p@k). Best performing combination is bold faced." ], "reasoning": "Check the precision score for Seed 2 using Eq. 8 in the 'Person' category. The value is 0.53, which is higher than 0.5.", "statement": "The precision score for Seed 2 using Eq. 8 for the 'Person' category is higher than 0.5.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Pipeline testing results of EAL and EAA annotation modes showing the model confidence (σ), F-Scores, and percentage cut from the pool of sentences.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/163.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Pipeline testing results of EAL and EAA annotation modes showing the model confidence (σ), F-Scores, and percentage cut from the pool of sentences." ], "reasoning": "The HFA auto-annotation mode shows a benefit in a realistic enterprise setting by requiring annotation of 33% of the data to increase the F-Score by 10%, compared to ESA.", "description": "Finally, for the last setting, we tested the system using the three auto-annotation modes (i.e., FA, HFA,. and UFA) as shown in Table 3. While the auto-annotation mode can allow us to reduce up to 87% of the data pool, this drastic saving also reduces the accuracy of the learned model, achieving, on average, around 81% F-Score. Overall, our framework presents a trade off between coverage and annotation cost. The HFA auto-annotation mode shows the benefit, especially in a realistic enterprise setting, when we need to annotate 33% of the data to increase F-Score by only 10% (when comparing the on average performance of HFA with ESA) is unreasonable. Table 3 appears to show FA being inferior to EAL in terms of the percentage cut for the Location class, for example. In reality FA reduced sentence annotation by 65% to reach 0.99 F-Score. But as our testing criteria demanded that we either reach 1.0 F-Score or finish all sentences from the pool, FA tried to finish the pool without any further performance improvement on the 0.99 F-Score." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Pipeline testing results of EAL and EAA annotation modes showing the model confidence (σ), F-Scores, and percentage cut from the pool of sentences.'\nProblem: 'What is the F-Score for the Location class in the EAA Annotation Mode FA?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/163.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Pipeline testing results of EAL and EAA annotation modes showing the model confidence (σ), F-Scores, and percentage cut from the pool of sentences." ], "reasoning": "The table shows the F-Score for the Location class under different annotation modes. For the EAA Annotation Mode FA, the F-Score for the Location class is 0.93.", "question": "What is the F-Score for the Location class in the EAA Annotation Mode FA?", "answer": "So, the answer is {\"answer\": \"0.93\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFA reduced sentence annotation by 65% to reach 0.99 F-Score for the Location class.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/163.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Pipeline testing results of EAL and EAA annotation modes showing the model confidence (σ), F-Scores, and percentage cut from the pool of sentences." ], "reasoning": "The table shows that for the Location class, FA reduced sentence annotation by 46% to reach a 0.99 F-Score, not 65%.", "statement": "FA reduced sentence annotation by 65% to reach 0.99 F-Score for the Location class.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Coarse text types in OntoNotes'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/164.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Coarse text types in OntoNotes" ], "reasoning": "The corpus contains 1.59 million tokens from various genres, with the majority being written data from newswire, web, and translations, and spoken data from television broadcasts and phone conversations.", "description": "The coreference annotated portion of the corpus contains 1.59 million tokens from multiple genres, presented in Table 1.. Written data constitutes the large bulk of material, primarily from newswire (Wall Street Journal data), as well as some data from the Web and the New Testament, and some translations of news and online discussions in Arabic and Chinese. The translated data has been placed in its own category: it behaves more conservatively in preferring strict agreement than non-translated language (see Section 4.2), perhaps due to translators' editorial practices. The spoken data comes primarily from television broadcasts, including dialogue data from MSNBC, Phoenix and other broadcast sources (bc.conv), or news, from CNN, ABC and others (bc.news), as well as phone conversations." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Coarse text types in OntoNotes'\nProblem: 'What is the total number of tokens in the written news category?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/164.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Coarse text types in OntoNotes" ], "reasoning": "To find the total number of tokens in the written news category, sum the tokens from the bible, translations, and web subcategories.", "question": "What is the total number of tokens in the written news category?", "answer": "So, the answer is {\"answer\": \"1,099,105\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe spoken data from phone conversations has more tokens than the spoken data from bc.news.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/164.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Coarse text types in OntoNotes" ], "reasoning": "The table shows that the spoken data from phone conversations has 110,132 tokens, while the spoken data from bc.news has 244,425 tokens.", "statement": "The spoken data from phone conversations has more tokens than the spoken data from bc.news.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Confusion matrix for test data classification'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/165.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Confusion matrix for test data classification" ], "reasoning": "The classifier is highly accurate for the majority class 'Sg', with minimal misclassifications, but about 1/3 of 'Pl' cases are incorrectly predicted as 'Sg'.", "description": "Looking at the actual classifications obtained by the classifier produces the confusion matrix in Table 2. The matrix makes it clear that the classifier is very good at avoiding errors against the majority class: it almost never guesses 'notional' when it shouldn't. Conversely, about 1/3 of actual notional cases are misclassified, predicted to be 'strict'. Among the erroneous cases, only 6 belong to Type III (about 15% of errors) , showing that the classifier largely handles this type quite well next to the other types, since Type III covers about 20% of plural-to-singular agreement cases." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Confusion matrix for test data classification'\nProblem: 'What is the total number of actual 'Pl' cases that were misclassified?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/165.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Confusion matrix for test data classification" ], "reasoning": "From the table, the number of actual 'Pl' cases misclassified as 'Sg' is 7.", "question": "What is the total number of actual 'Pl' cases that were misclassified?", "answer": "So, the answer is {\"answer\": \"7\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe classifier correctly predicted more than 80 'Pl' cases.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/165.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Confusion matrix for test data classification" ], "reasoning": "The table shows that the classifier correctly predicted 81 'Pl' cases, which is more than 80.", "statement": "The classifier correctly predicted more than 80 'Pl' cases.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Agreement patterns across genres'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/166.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Agreement patterns across genres" ], "reasoning": "Different genres exhibit varying tendencies towards notional agreement, with web data resembling spoken language and translations being the most restrictive.", "description": "Next we can consider the effect of genre, and expectations that speech promotes notional agreement. This is confirmed in Table 3. However we note that individual genres do behave differently: data from the Web is closer to spoken language. The most restrictive genre in avoiding notional agreement is translations. Both of these facts may reflect a combination of modality, genre and editorial practice effects. However the strong differences suggest that genre is likely crucial to any model attempting to predict this phenomenon." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Agreement patterns across genres'\nProblem: 'Which genre has the highest percentage of notional agreement?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/166.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Agreement patterns across genres" ], "reasoning": "To find the genre with the highest percentage of notional agreement, compare the 'agreement % notional' values for each genre.", "question": "Which genre has the highest percentage of notional agreement?", "answer": "So, the answer is {\"answer\": \"bc.conv\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTranslations have the lowest percentage of notional agreement among written genres.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/166.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Agreement patterns across genres" ], "reasoning": "The table shows that translations have an 'agreement % notional' of 20.75%, which is the lowest among the written genres listed.", "statement": "Translations have the lowest percentage of notional agreement among written genres.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Number of propositions per type in AMPERE.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/167.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Number of propositions per type in AMPERE." ], "reasoning": "The table shows that evaluation and fact are the most frequent types of propositions, making up 38.3% and 36.5% of the total respectively.", "description": "We also show the number of propositions in each category in Table 3. The most frequent types are evaluation (38.3%) and fact (36.5%)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Number of propositions per type in AMPERE.'\nProblem: 'What is the combined percentage of evaluation and fact propositions?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/167.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Number of propositions per type in AMPERE." ], "reasoning": "The table shows 3,982 evaluation and 3,786 fact propositions out of a total of 10,386. The combined percentage is calculated as (3,982 + 3,786) / 10,386 * 100.", "question": "What is the combined percentage of evaluation and fact propositions?", "answer": { "answer": "74.79%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe number of reference propositions is less than 200.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/167.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Number of propositions per type in AMPERE." ], "reasoning": "The table shows that there are 207 reference propositions, which is more than 200.", "statement": "The number of reference propositions is less than 200.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Proposition segmentation results. Result that is significantly better than all comparisons is marked with ∗ (p<10−6, McNemar test).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/168.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Proposition segmentation results. Result that is significantly better than all comparisons is marked with ∗ (p<10−6, McNemar test)." ], "reasoning": "BiLSTM-CRF achieves the highest F1 score among the methods, but the performance on reviews is lower than on existing datasets.", "description": "Table 4 shows that BiLSTM-CRF outperforms other methods in F1. More importantly, the perfor. mance on reviews is lower than those reached on existing datasets, e.g., an F1 of 86.7 is obtained by CRF for essays (Stab and Gurevych, 2017)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Proposition segmentation results. Result that is significantly better than all comparisons is marked with ∗ (p<10−6, McNemar test).'\nProblem: 'Which method has the highest F1 score and how does it compare to the F1 score obtained by CRF for essays?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/168.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Proposition segmentation results. Result that is significantly better than all comparisons is marked with ∗ (p<10−6, McNemar test)." ], "reasoning": "The table shows that BiLSTM-CRF has the highest F1 score of 81.09. The description mentions that CRF obtained an F1 score of 86.7 for essays, which is higher than the F1 score of BiLSTM-CRF.", "question": "Which method has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"BiLSTM-CRF\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBiLSTM-CRF has the highest F1 score among the methods listed in the table.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/168.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Proposition segmentation results. Result that is significantly better than all comparisons is marked with ∗ (p<10−6, McNemar test)." ], "reasoning": "The table shows that BiLSTM-CRF has the highest F1 score of 81.09, which is higher than the F1 scores of all other methods listed.", "statement": "BiLSTM-CRF has the highest F1 score among the methods listed in the table.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: All 2-layer systems, and 1-layer systems if best in column. State-of-the-art results in bold; † indicates best in column (corresponding row shaded in purple). Key: L=linear, T=tree, H=hybrid, (1,2)=# layers, S=single-task specific, G=single-task general, +lexfeats=with all lexical features, MultiSimp=multi-task simple, MultiBal=multi-task balanced, MultiFoc=multi-task focused, w/UDS-IH2=trained on all data including UDS-IH2. All-3.0 is a constant baseline, always predicting 3.0.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/169.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: All 2-layer systems, and 1-layer systems if best in column. State-of-the-art results in bold; † indicates best in column (corresponding row shaded in purple). Key: L=linear, T=tree, H=hybrid, (1,2)=# layers, S=single-task specific, G=single-task general, +lexfeats=with all lexical features, MultiSimp=multi-task simple, MultiBal=multi-task balanced, MultiFoc=multi-task focused, w/UDS-IH2=trained on all data including UDS-IH2. All-3.0 is a constant baseline, always predicting 3.0." ], "reasoning": "The L-biLSTM consistently outperforms the T-biLSTM, but the H-biLSTM is the top-performing system on UW for correlation.", "description": "Table 4 reports the results for all of the 2-layer L-, T-, and H-biLSTMs.7 The best-performing system for each dataset and metric are highlighted in purple, and when the best-performing system for a particular dataset was a 1-layer model, that system is included in Table 4.. The highest-performing system for each is reported in Table 4.. On its own, the biLSTM with linear topology (L-biLSTM) performs. consistently better than the biLSTM with tree topology (T-biLSTM). However, the hybrid topology (H-biLSTM), consisting of both a L- and TbiLSTM is the top-performing system on UW for correlation (Table 4).. Though our methods achieve state of the art in the single-task setting, the best performing systems are mostly multi-task (Table 4 and Supplementary Materials)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: All 2-layer systems, and 1-layer systems if best in column. State-of-the-art results in bold; † indicates best in column (corresponding row shaded in purple). Key: L=linear, T=tree, H=hybrid, (1,2)=# layers, S=single-task specific, G=single-task general, +lexfeats=with all lexical features, MultiSimp=multi-task simple, MultiBal=multi-task balanced, MultiFoc=multi-task focused, w/UDS-IH2=trained on all data including UDS-IH2. All-3.0 is a constant baseline, always predicting 3.0.'\nProblem: 'Which system has the highest correlation on the UW dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/169.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: All 2-layer systems, and 1-layer systems if best in column. State-of-the-art results in bold; † indicates best in column (corresponding row shaded in purple). Key: L=linear, T=tree, H=hybrid, (1,2)=# layers, S=single-task specific, G=single-task general, +lexfeats=with all lexical features, MultiSimp=multi-task simple, MultiBal=multi-task balanced, MultiFoc=multi-task focused, w/UDS-IH2=trained on all data including UDS-IH2. All-3.0 is a constant baseline, always predicting 3.0." ], "reasoning": "To find the system with the highest correlation on the UW dataset, look at the 'UW r' column and identify the highest value. The highest value is 0.749, achieved by H-biLSTM(2)-MultiSimp w/UDS-IH2.", "question": "Which system has the highest correlation on the UW dataset?", "answer": "So, the answer is {\"answer\": \"H-biLSTM(2)-MultiSimp w/UDS-IH2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe L-biLSTM(2)-S system has the highest correlation on the FactBank dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/169.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: All 2-layer systems, and 1-layer systems if best in column. State-of-the-art results in bold; † indicates best in column (corresponding row shaded in purple). Key: L=linear, T=tree, H=hybrid, (1,2)=# layers, S=single-task specific, G=single-task general, +lexfeats=with all lexical features, MultiSimp=multi-task simple, MultiBal=multi-task balanced, MultiFoc=multi-task focused, w/UDS-IH2=trained on all data including UDS-IH2. All-3.0 is a constant baseline, always predicting 3.0." ], "reasoning": "To verify this statement, check the 'FactBank r' column for the highest value. The highest value is 0.857, achieved by H-biLSTM(1)-MultiSimp, not L-biLSTM(2)-S.", "statement": "The L-biLSTM(2)-S system has the highest correlation on the FactBank dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Proposition classification F1 scores. Results that are significant better than other methods are marked with ∗ (p<10−6, McNemar test).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/170.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Proposition classification F1 scores. Results that are significant better than other methods are marked with ∗ (p<10−6, McNemar test)." ], "reasoning": "CNN performs better for evaluation and fact types due to more training samples, while joint models achieve the best F1 scores when gold-standard segmentation is unavailable.", "description": "F1 scores for all propositions and each type are reported in Table 5.. CNN performs better for types with significantly more training samples, i.e., evaluation and fact, indicating the effect of data size on neural model's performance. Joint models (CRF-joint and BiLSTM-CRF-joint) yield the best F1 scores for all categories when gold-standard segmentation is unavailable." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Proposition classification F1 scores. Results that are significant better than other methods are marked with ∗ (p<10−6, McNemar test).'\nProblem: 'Which model yields the highest F1 score for the 'Req' type with predicted segments?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/170.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Proposition classification F1 scores. Results that are significant better than other methods are marked with ∗ (p<10−6, McNemar test)." ], "reasoning": "To find the highest F1 score for the 'Req' type with predicted segments, look at the 'Req' column under 'With Predicted Segments'. The highest score is 67.31, achieved by the BiLSTM-CRF-joint model.", "question": "Which model yields the highest F1 score for the 'Req' type with predicted segments?", "answer": "So, the answer is {\"answer\": \"BiLSTM-CRF-joint\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe CNN model performs best for the 'Eval' type with gold-standard segments.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/170.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Proposition classification F1 scores. Results that are significant better than other methods are marked with ∗ (p<10−6, McNemar test)." ], "reasoning": "To verify this statement, check the 'Eval' column under 'With Gold-Standard Segments'. The highest F1 score is 69.02, achieved by the CNN model.", "statement": "The CNN model performs best for the 'Eval' type with gold-standard segments.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: The model and its hyper-parameters.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/171.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The model and its hyper-parameters." ], "reasoning": "The model uses two B-LSTM encoders and one LSTM decoder, each with two hidden layers and a hidden dimension of 500, along with a general attention mechanism.", "description": "Table 1 lists all hyper-parameters which have all been chosen using only training and validation data. The two encoders have been implemented using a Bidirectional Long Short-Term Memory (B-LSTM) (Hochreiter and Schmidhuber, 1997) while the decoder uses a unidirectional LSTM. Both the encoders and the decoder use two hidden layers. For the attention network, we have used the OpenNMT's general option (Luong et al., 2015)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: The model and its hyper-parameters.'\nProblem: 'What is the hidden dimension size used for both the encoders and the decoder?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/171.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The model and its hyper-parameters." ], "reasoning": "The table shows that both the encoder hidden dimension and the decoder hidden dimension are 500.", "question": "What is the hidden dimension size used for both the encoders and the decoder?", "answer": "So, the answer is {\"answer\": \"500\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model uses a beam size of 10.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/171.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The model and its hyper-parameters." ], "reasoning": "The table indicates that the beam size used in the model is 5, not 10.", "statement": "The model uses a beam size of 10.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results on the WMT17 IT domain English-German APE test set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/172.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on the WMT17 IT domain English-German APE test set." ], "reasoning": "Our model initially underperforms compared to baselines but significantly improves and outperforms them when trained with additional artificial data.", "description": "Table 2 compares the accuracy of our model on the test data with two baselines and two state-of-theart comparable systems. The MT baseline simply consists of the accuracy of the mt sentences with respect to the pe ground truth. The other baseline is given by a statistical PE (SPE) system (Simard et al., 2007) chosen by the WMT17 organizers. Table 2 shows that when our model is trained with only the 11K WMT17 official training sentences, it cannot even approach the baselines. Even when the 12K WMT16 sentences are added, its accuracy is still well below that of the baselines. However, when the 500K artificial data are added, it reports a major improvement and it outperforms them both significantly. In addition, we have compared our model with two recent systems that have used our. same training settings (500K artificial triplets + 23K manual triplets oversampled 10 times), reporting a slightly higher accuracy than both (1.43 TER and 1.93 BLEU p.p. over (Varis and Bojar, 2017) and 0.21 TER and 0.30 BLEU p.p. over (B´erard et al., 2017)). Since their models explicitly predicts edit operations rather than post-edited sentences, we speculate that these two tasks are of comparable intrinsic complexity." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results on the WMT17 IT domain English-German APE test set.'\nProblem: 'What is the BLEU score of our model when trained with 23K manual triplets and 500K artificial data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/172.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on the WMT17 IT domain English-German APE test set." ], "reasoning": "The table shows the BLEU score for different training configurations. For the 'train 23K + 500K' configuration, the BLEU score is 66.21.", "question": "What is the BLEU score of our model when trained with 23K manual triplets and 500K artificial data?", "answer": "So, the answer is {\"answer\": \"66.21\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur model trained with 23K manual triplets and 500K artificial data has a lower TER score than the SPE baseline.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/172.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on the WMT17 IT domain English-German APE test set." ], "reasoning": "The table shows that the TER score for 'train 23K + 500K' is 22.60, which is lower than the TER score of the SPE baseline, which is 24.69.", "statement": "Our model trained with 23K manual triplets and 500K artificial data has a lower TER score than the SPE baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: The performance of the best setting for each property.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/173.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The performance of the best setting for each property." ], "reasoning": "The table shows that word2vec with a higher embedding dimension is preferred for most properties.", "description": "Table 3 displays the best performing DSMfor each property. There is a preference to word2vec and to a higher embedding dimension." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: The performance of the best setting for each property.'\nProblem: 'Which property has the highest F1 score and what is its embedding method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/173.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The performance of the best setting for each property." ], "reasoning": "To find the property with the highest F1 score, compare the F1 scores of all properties. The highest F1 score is 78.9 for the property 'Is a weapon' with the embedding method 'word2vec CBOW'.", "question": "Which property has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"Is a weapon\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe property 'Has various colors' uses the GloVe embedding method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/173.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The performance of the best setting for each property." ], "reasoning": "The table shows that the property 'Has various colors' indeed uses the GloVe embedding method.", "statement": "The property 'Has various colors' uses the GloVe embedding method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Mean predictions for linear (L-biLSTM-S(2)) and tree models (T-biLSTM-S(2)) on UDS-IH2-dev, grouped by governing dependency relation. Only the 10 most frequent governing dependency relations in UDS-IH2-dev are shown.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/174.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Mean predictions for linear (L-biLSTM-S(2)) and tree models (T-biLSTM-S(2)) on UDS-IH2-dev, grouped by governing dependency relation. Only the 10 most frequent governing dependency relations in UDS-IH2-dev are shown." ], "reasoning": "The L-biLSTM model's mean prediction is generally closer to the true mean than the T-biLSTM model, except in cases like the xcomp governing relation.", "description": "Evidence of this complementarity can be seen in Table 6, which contains a breakdown of system performance by governing dependency relation, for both linear and tree models, on UDS-IH2-dev. In most cases, the L-biLSTM's mean prediction is closer to the true mean. This appears to arise in part because the T-biLSTM is less confident in its predictions – i.e. its mean prediction tends to be closer to 0. This results in the L-biLSTM being too confident in certain cases – e.g. in the case of the xcomp governing relation, where the T-biLSTM mean prediction is closer to the true mean." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Mean predictions for linear (L-biLSTM-S(2)) and tree models (T-biLSTM-S(2)) on UDS-IH2-dev, grouped by governing dependency relation. Only the 10 most frequent governing dependency relations in UDS-IH2-dev are shown.'\nProblem: 'Which governing relation has the L-biLSTM model being too confident compared to the T-biLSTM model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/174.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Mean predictions for linear (L-biLSTM-S(2)) and tree models (T-biLSTM-S(2)) on UDS-IH2-dev, grouped by governing dependency relation. Only the 10 most frequent governing dependency relations in UDS-IH2-dev are shown." ], "reasoning": "The description mentions that the L-biLSTM is too confident in certain cases, specifically in the xcomp governing relation, where the T-biLSTM mean prediction is closer to the true mean.", "question": "Which governing relation has the L-biLSTM model being too confident compared to the T-biLSTM model?", "answer": { "answer": "xcomp" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe T-biLSTM model's mean prediction is closer to the true mean for the xcomp governing relation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/174.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Mean predictions for linear (L-biLSTM-S(2)) and tree models (T-biLSTM-S(2)) on UDS-IH2-dev, grouped by governing dependency relation. Only the 10 most frequent governing dependency relations in UDS-IH2-dev are shown." ], "reasoning": "The description states that for the xcomp governing relation, the T-biLSTM mean prediction is closer to the true mean, which is supported by the table showing T-biLSTM's mean prediction (-0.49) being closer to the Mean Label (-0.42) than L-biLSTM's mean prediction (-0.57).", "statement": "The T-biLSTM model's mean prediction is closer to the true mean for the xcomp governing relation.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: The lexicons used as external knowledge.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/175.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The lexicons used as external knowledge." ], "reasoning": "We utilize various lexicons with psycho-linguistic, sentiment, and emotion annotations to augment our models with expert knowledge.", "description": "we augment our models with existing linguistic and affective knowledge from human experts. Specifically, we leverage lexica containing psycho-linguistic, sentiment and emotion annotations.. the word's annotations from the lexicons shown in Table 1.. As prior knowledge, we leverage the lexicons presented in Table 1. We selected widely-used lexicons that represent different facets of affective and psycho-linguistic features, namely;" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: The lexicons used as external knowledge.'\nProblem: 'Which lexicon has the highest number of dimensions and how many words does it contain?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/175.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The lexicons used as external knowledge." ], "reasoning": "By examining the table, LIWC has the highest number of dimensions (73) and contains 18,504 words.", "question": "Which lexicon has the highest number of dimensions and how many words does it contain?", "answer": "So, the answer is {\"answer\": \"18,504\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe AFINN lexicon has more words than the Emolex lexicon.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/175.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The lexicons used as external knowledge." ], "reasoning": "According to the table, AFINN has 6,786 words, while Emolex has 14,182 words, which means AFINN has fewer words than Emolex.", "statement": "The AFINN lexicon has more words than the Emolex lexicon.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison across benchmark datasets. Reported values are averaged across ten runs. All reported measures are F1 scores, apart from SST−5 which is evaluated with Accuracy.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/176.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison across benchmark datasets. Reported values are averaged across ten runs. All reported measures are F1 scores, apart from SST−5 which is evaluated with Accuracy." ], "reasoning": "Incorporating external knowledge in RNN-based architectures improves performance over the baseline for all datasets, with feature-based gating and combined methods showing significant improvements.", "description": "We present our results in Section 5 (Table 3). In Table 3 we use the abbreviations \"baseline\" and \"emb. conc.\" for the two baseline models respectively.. We compare the performance of the three proposed conditioning methods with the two baselines and the state-of-the-art in Table 3.. The results show that incorporating external knowledge in RNN-based architectures consistently improves performance over the baseline for all datasets. Furthermore, feature-based gating improves upon baseline concatenation in the embedding layer across benchmarks, with the exception of PsychExp dataset.. For the Sent17 dataset we achieve state-ofthe-art F1 score using the feature-based gating method; we further improve performance when combining gating with the emb. conc. method. For SST-5, we observe a significant performance boost with combined attentional gating and embedding conditioning (gate + emb. conc.). For PsychExp, we marginally outperform the state-ofthe-art also with the combined method, while for Irony18, feature-based gating yields the best results. Finally, concatenation based conditioning is the top method for SCv1, and the combination method for SCv2." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Comparison across benchmark datasets. Reported values are averaged across ten runs. All reported measures are F1 scores, apart from SST−5 which is evaluated with Accuracy.'\nProblem: 'Which model achieved the highest F1 score for the SCv1 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/176.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison across benchmark datasets. Reported values are averaged across ten runs. All reported measures are F1 scores, apart from SST−5 which is evaluated with Accuracy." ], "reasoning": "To find the highest F1 score for the SCv1 dataset, look at the SCv1 column and identify the highest value. The highest value is 65.1±0.6, achieved by the 'conc.' model.", "question": "Which model achieved the highest F1 score for the SCv1 dataset?", "answer": { "answer": "conc." } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe gate+emb.conc. model achieved the highest F1 score for the Sent17 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/176.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison across benchmark datasets. Reported values are averaged across ten runs. All reported measures are F1 scores, apart from SST−5 which is evaluated with Accuracy." ], "reasoning": "To verify this statement, look at the Sent17 column and compare the F1 scores of all models. The highest F1 score is 68.9±0.3, achieved by the gate+emb.conc. model.", "statement": "The gate+emb.conc. model achieved the highest F1 score for the Sent17 dataset.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Mean gold labels, counts, and MAE for L-biLSTM(2)-S and T-biLSTM(2)-S model predictions on UDS-IH2-dev, grouped by modals and negation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/177.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Mean gold labels, counts, and MAE for L-biLSTM(2)-S and T-biLSTM(2)-S model predictions on UDS-IH2-dev, grouped by modals and negation." ], "reasoning": "The context with no direct modal and no negation has the highest factuality, while 'will' has the most negative mean factuality score.", "description": "Table 5 illustrates the influence of modals and negation on the factuality of the events they have direct scope over. The context with the highest factuality on average is no direct modal and no negation (first row); all other modal contexts have varying degrees of negative mean factuality scores, with will as the most negative." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Mean gold labels, counts, and MAE for L-biLSTM(2)-S and T-biLSTM(2)-S model predictions on UDS-IH2-dev, grouped by modals and negation.'\nProblem: 'Which modal context has the second most negative mean factuality score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/177.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Mean gold labels, counts, and MAE for L-biLSTM(2)-S and T-biLSTM(2)-S model predictions on UDS-IH2-dev, grouped by modals and negation." ], "reasoning": "From the table, the modal context 'should' with no negation has the second most negative mean factuality score of -1.31.", "question": "Which modal context has the second most negative mean factuality score?", "answer": { "answer": "should" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe modal context 'may' with no negation has a positive mean factuality score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/177.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Mean gold labels, counts, and MAE for L-biLSTM(2)-S and T-biLSTM(2)-S model predictions on UDS-IH2-dev, grouped by modals and negation." ], "reasoning": "The table shows that the modal context 'may' with no negation has a mean factuality score of -0.38, which is negative.", "statement": "The modal context 'may' with no negation has a positive mean factuality score.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Coefficient of determination (r2) between global metrics and crowdsourced topic-word matching annotations.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/178.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Coefficient of determination (r2) between global metrics and crowdsourced topic-word matching annotations." ], "reasoning": "The coefficient of determination (r2) for global metrics shows moderate to poor correlation with human judgment of local topic quality, especially for coherence.", "description": "As seen in Table 2, we report the coefficient of determination (r2) for each global metric and dataset.. Note that global metrics do correlate somewhat with human judgment of local topic quality. However, the correlation is moderate to poor, especially in the case of coherence" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Coefficient of determination (r2) between global metrics and crowdsourced topic-word matching annotations.'\nProblem: 'Which global metric has the highest coefficient of determination (r2) for the Amazon dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/178.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Coefficient of determination (r2) between global metrics and crowdsourced topic-word matching annotations." ], "reasoning": "To find the highest coefficient of determination (r2) for the Amazon dataset, compare the values under the Amazon column. The highest value is 0.6960 for the SigVac metric.", "question": "Which global metric has the highest coefficient of determination (r2) for the Amazon dataset?", "answer": { "answer": "SigVac" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe coherence metric has the lowest coefficient of determination (r2) for the New York Times dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/178.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Coefficient of determination (r2) between global metrics and crowdsourced topic-word matching annotations." ], "reasoning": "To verify this statement, compare the values under the New York Times column. The coherence metric has a value of 0.3799, which is the lowest among the listed metrics.", "statement": "The coherence metric has the lowest coefficient of determination (r2) for the New York Times dataset.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Coefficient of determination (r2) between automated metrics and crowdsourced topic-word matching annotations. We include metrics measuring both local topic quality and global topic quality.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/179.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Coefficient of determination (r2) between automated metrics and crowdsourced topic-word matching annotations. We include metrics measuring both local topic quality and global topic quality." ], "reasoning": "Humans agree more often with models trained on Amazon reviews than on New York Times, as evidenced by higher r2 values across metrics.", "description": "Humans agree more often with models trained on Amazon reviews than on New York Times.. As seen in Table 3, we report the coefficient of determination (r2) for each metric and dataset.. SWITCHP most closely approximates human judgments of local topic quality, with an r2 which indicates a strong correlation.. As evidenced by the lower r2 for SWITCHVI, even switching between related topics does not seem to line up with human judgments of local topic quality." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Coefficient of determination (r2) between automated metrics and crowdsourced topic-word matching annotations. We include metrics measuring both local topic quality and global topic quality.'\nProblem: 'Which metric has the highest r2 value for Amazon reviews?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/179.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Coefficient of determination (r2) between automated metrics and crowdsourced topic-word matching annotations. We include metrics measuring both local topic quality and global topic quality." ], "reasoning": "To find the highest r2 value for Amazon reviews, we need to look at the 'Amazon' column and identify the highest value. The highest value is 0.9077 for the 'SwitchP' metric.", "question": "Which metric has the highest r2 value for Amazon reviews?", "answer": "So, the answer is {\"answer\": \"SwitchP\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe SwitchP metric has a higher r2 value for Newsgroups than for New York Times.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/179.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Coefficient of determination (r2) between automated metrics and crowdsourced topic-word matching annotations. We include metrics measuring both local topic quality and global topic quality." ], "reasoning": "To verify this statement, we compare the r2 values of the SwitchP metric for Newsgroups and New York Times. The r2 value for Newsgroups is 0.8737, and for New York Times, it is 0.7022. Since 0.8737 is greater than 0.7022, the statement is supported.", "statement": "The SwitchP metric has a higher r2 value for Newsgroups than for New York Times.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Event salience performance. (-E) and (-F) marks removing Entity information and Features from the full KCM model. The relative performance differences are computed against Frequency. W/T/L are the number of documents a method wins, ties, and loses compared to Frequency. † and ‡ mark the statistically significant improvements over Frequency†, LeToR‡ respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/180.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Event salience performance. (-E) and (-F) marks removing Entity information and Features from the full KCM model. The relative performance differences are computed against Frequency. W/T/L are the number of documents a method wins, ties, and loses compared to Frequency. † and ‡ mark the statistically significant improvements over Frequency†, LeToR‡ respectively." ], "reasoning": "KCE outperforms all other models significantly on all metrics, with the highest P@1 score of 50% and a large relative gain on recall metrics.", "description": "We summarize the main results in Table 3.. Frequency is the best performing baseline. Its precision at 1 and 5 are higher than 40%. PageRank performs worse than Frequency on all. the precision and recall metrics. Location performs the worst.. LeToR outperforms the baselines significantly on all metrics. Particularly, its P@1 value outperforms the Frequency baseline the most (4.64%), indicating a much better estimation on the most salient event. In terms of AUC, LeToR outperforms Frequency by a large margin (11.19% relative gain).. The KCE model further beats LeToR significantly on all metrics, by around 5% on AUC and precision values, and by around 10% on the recall values. Notably, the P@1 score is much higher, reaching 50%. The large relative gain on all the recall metrics and the high performance on precision show that KCE works really well on the top of the rank list.. To understand the source of performance gain of KCE, we conduct an ablation study by removing its components: -E removes of entity kernels; -EF removes the entity kernels and the features. We observe a performance drop in both cases." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Event salience performance. (-E) and (-F) marks removing Entity information and Features from the full KCM model. The relative performance differences are computed against Frequency. W/T/L are the number of documents a method wins, ties, and loses compared to Frequency. † and ‡ mark the statistically significant improvements over Frequency†, LeToR‡ respectively.'\nProblem: 'Which model has the highest P@1 score and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/180.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Event salience performance. (-E) and (-F) marks removing Entity information and Features from the full KCM model. The relative performance differences are computed against Frequency. W/T/L are the number of documents a method wins, ties, and loses compared to Frequency. † and ‡ mark the statistically significant improvements over Frequency†, LeToR‡ respectively." ], "reasoning": "The table shows that the KCE model has the highest P@1 score, which is 0.5049.", "question": "Which model has the highest P@1 score?", "answer": { "answer": "KCE" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nLeToR outperforms Frequency on all metrics.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/180.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Event salience performance. (-E) and (-F) marks removing Entity information and Features from the full KCM model. The relative performance differences are computed against Frequency. W/T/L are the number of documents a method wins, ties, and loses compared to Frequency. † and ‡ mark the statistically significant improvements over Frequency†, LeToR‡ respectively." ], "reasoning": "The table shows that LeToR outperforms Frequency on P@1, P@5, P@10, and AUC metrics, but not on R@01 and R@05.", "statement": "LeToR outperforms Frequency on all metrics.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Feature Ablation Results. + sign indicates the additional features to Frequency. Loc is the sentence location feature. Event is the event voting feature. Entity is the entity voting feature. Local is the local entity voting feature. † marks the statistically significant improvements over + Loc.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/181.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Feature Ablation Results. + sign indicates the additional features to Frequency. Loc is the sentence location feature. Event is the event voting feature. Entity is the entity voting feature. Local is the local entity voting feature. † marks the statistically significant improvements over + Loc." ], "reasoning": "Adding Location to Frequency significantly improves performance, and including all voting features further enhances all metrics with statistical significance in 4 out of 6 metrics.", "description": "We gradually add feature groups to the Frequency baseline. The combination of Location (sentence location) and Frequency almost sets the performance for the whole model. Adding each voting feature individually produces mixed results. However, adding all voting features improves all metrics. Though the margin is small, 4 of them are statistically significant over Frequency+Location.. To understand the contribution of individual features, we conduct an ablation study of various feature settings in Table 4." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Feature Ablation Results. + sign indicates the additional features to Frequency. Loc is the sentence location feature. Event is the event voting feature. Entity is the entity voting feature. Local is the local entity voting feature. † marks the statistically significant improvements over + Loc.'\nProblem: 'Which feature combination achieves the highest AUC score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/181.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Feature Ablation Results. + sign indicates the additional features to Frequency. Loc is the sentence location feature. Event is the event voting feature. Entity is the entity voting feature. Local is the local entity voting feature. † marks the statistically significant improvements over + Loc." ], "reasoning": "By examining the AUC column, the highest value is 0.6374, achieved by the combination of Loc, Entity, Event, and Local.", "question": "Which feature combination achieves the highest AUC score?", "answer": { "answer": "+ Loc + Entity + Event + Local" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAdding all voting features to Frequency and Location improves all metrics.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/181.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Feature Ablation Results. + sign indicates the additional features to Frequency. Loc is the sentence location feature. Event is the event voting feature. Entity is the entity voting feature. Local is the local entity voting feature. † marks the statistically significant improvements over + Loc." ], "reasoning": "The table shows that adding all voting features (Loc + Entity + Event + Local) to Frequency and Location improves all metrics compared to Frequency + Location alone.", "statement": "Adding all voting features to Frequency and Location improves all metrics.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Notable attributes of 50 instances from UDS-IH2-dev with highest absolute prediction error (using H-biLSTM(2)-MultiSim w/UDS-IH2).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/182.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Notable attributes of 50 instances from UDS-IH2-dev with highest absolute prediction error (using H-biLSTM(2)-MultiSim w/UDS-IH2)." ], "reasoning": "Table 7 shows that grammatical errors, including run-on sentences, are the most frequent issue in the UDS-IH2-dev dataset, posing a significant challenge for the H-biLSTM(2)MultiSim model.", "description": "Table 7 shows results from a manual error analysis on 50 events from UDS-IH2-dev with highest absolute prediction error (using H-biLSTM(2)MultiSim w/UDS-IH2). Grammatical errors (such as run-on sentences) in the underlying text of UDS-IH2 appear to pose a particular challenge for these models;" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Notable attributes of 50 instances from UDS-IH2-dev with highest absolute prediction error (using H-biLSTM(2)-MultiSim w/UDS-IH2).'\nProblem: 'What is the most common type of error found in the UDS-IH2-dev dataset according to Table 7?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/182.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Notable attributes of 50 instances from UDS-IH2-dev with highest absolute prediction error (using H-biLSTM(2)-MultiSim w/UDS-IH2)." ], "reasoning": "The table lists various types of errors and their frequencies. The most common error is the one with the highest frequency, which is 'Grammatical error present, incl. run-ons' with 16 occurrences.", "question": "What is the number for Grammatical error present, incl. run-ons in the UDS-IH2-dev dataset according to Table 7?", "answer": "So, the answer is {\"answer\": \"16\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe most common error in the UDS-IH2-dev dataset is the presence of auxiliary or light verbs.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/182.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Notable attributes of 50 instances from UDS-IH2-dev with highest absolute prediction error (using H-biLSTM(2)-MultiSim w/UDS-IH2)." ], "reasoning": "The table shows that 'Grammatical error present, incl. run-ons' is the most common error with 16 occurrences, while 'Is an auxiliary or light verb' has 14 occurrences.", "statement": "The most common error in the UDS-IH2-dev dataset is the presence of auxiliary or light verbs.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Similarities between event entity pairs. Word2vec shows the cosine similarity in pre-trained embeddings. KCE lists their closest kernel mean after training. (E) marks entities.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/183.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Similarities between event entity pairs. Word2vec shows the cosine similarity in pre-trained embeddings. KCE lists their closest kernel mean after training. (E) marks entities." ], "reasoning": "The table lists pairs of events and entities with their Word2Vec and KCE scores, showing various types of relations such as script-like chains and quasi-identity relations.", "description": "We inspect some pairs of events and entities in different kernels and list some examples in Table 5.. The pairs in Table 3 exhibit interesting types of relations: e.g.,\"arrest-charge\" and \"attack-kill\" form script-like chains; \"911 attack\" forms a quasiidentity relation (Recasens et al., 2010) with \"attack\"; \"business\" and \"increase\" are candidates as frame-argument structure." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Similarities between event entity pairs. Word2vec shows the cosine similarity in pre-trained embeddings. KCE lists their closest kernel mean after training. (E) marks entities.'\nProblem: 'Which pair has the highest Word2Vec score and what is the score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/183.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Similarities between event entity pairs. Word2vec shows the cosine similarity in pre-trained embeddings. KCE lists their closest kernel mean after training. (E) marks entities." ], "reasoning": "By examining the Word2Vec scores in the table, the pair '911 attack' and 'attack' has the highest score of 0.72.", "question": "What is the Word2Vec score for the pair has the highest Word2Vec score?", "answer": "So, the answer is {\"answer\": \"0.72\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe pair 'attack' and 'trade' has the highest KCE score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/183.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Similarities between event entity pairs. Word2vec shows the cosine similarity in pre-trained embeddings. KCE lists their closest kernel mean after training. (E) marks entities." ], "reasoning": "By examining the KCE scores in the table, the pair 'attack' and 'trade' has the highest KCE score of 0.9.", "statement": "The pair 'attack' and 'trade' has the highest KCE score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Self-BLEU and percent of generated n-grams that are unique relative to own generations (left) WikiText-103 test set (middle) a sample of 5000 sentences from Toronto Book Corpus (right). For the WT103 and TBC rows, we sample 1000 sentences from the respective datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/184.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Self-BLEU and percent of generated n-grams that are unique relative to own generations (left) WikiText-103 test set (middle) a sample of 5000 sentences from Toronto Book Corpus (right). For the WT103 and TBC rows, we sample 1000 sentences from the respective datasets." ], "reasoning": "GPT generations have higher corpus-BLEU with TBC and greater n-gram overlap with both TBC and WikiText-103, suggesting reliance on generic sentences, while BERT generations are more diverse.", "description": "We present sample generations, quality results, and diversity results respectively in Tables 1, 2, 3.. This observation is further bolstered by the fact that the GPT generations have a higher corpus-BLEU with TBC than TBC has with itself.. The corpusBLEU between BERT models and the datasets is low, particularly with WT103.. We find that BERT generations are more diverse than GPT generations. GPT has high n-gram overlap (smaller percent of unique n-grams) with TBC, but surprisingly also with WikiText-103, despite being trained on different data. Furthermore, GPT generations have greater n-gram overlap with these datasets than these datasets have with themselves, further suggesting that GPT is relying significantly on generic sentences. BERT has lower n-gram overlap with both corpora, with similar degrees of n-gram overlap as the samples of the data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Self-BLEU and percent of generated n-grams that are unique relative to own generations (left) WikiText-103 test set (middle) a sample of 5000 sentences from Toronto Book Corpus (right). For the WT103 and TBC rows, we sample 1000 sentences from the respective datasets.'\nProblem: 'Which model has the highest Self-BLEU score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/184.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Self-BLEU and percent of generated n-grams that are unique relative to own generations (left) WikiText-103 test set (middle) a sample of 5000 sentences from Toronto Book Corpus (right). For the WT103 and TBC rows, we sample 1000 sentences from the respective datasets." ], "reasoning": "By examining the Self-BLEU column in the table, we see that GPT has the highest Self-BLEU score of 40.02.", "question": "Which model has the highest Self-BLEU score?", "answer": { "answer": "GPT" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBERT (large) has a higher percentage of unique n-grams with WT103 than GPT for n=2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/184.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Self-BLEU and percent of generated n-grams that are unique relative to own generations (left) WikiText-103 test set (middle) a sample of 5000 sentences from Toronto Book Corpus (right). For the WT103 and TBC rows, we sample 1000 sentences from the respective datasets." ], "reasoning": "The table shows that BERT (large) has 59.91% unique n-grams with WT103 for n=2, while GPT has 33.71%, indicating that the statement is supported.", "statement": "BERT (large) has a higher percentage of unique n-grams with WT103 than GPT for n=2.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Quality metrics of model generations. Perplexity (PPL) is measured using an additional language model (Dauphin et al., 2016). For the WT103 and TBC rows, we sample 1000 sentences from the respective datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/185.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Quality metrics of model generations. Perplexity (PPL) is measured using an additional language model (Dauphin et al., 2016). For the WT103 and TBC rows, we sample 1000 sentences from the respective datasets." ], "reasoning": "Compared to GPT, BERT generations are of worse quality but more diverse, and the outside language model is less perplexed by GPT generations despite its training data.", "description": "We present sample generations, quality results, and diversity results respectively in Tables 1, 2, 3.. We find that, compared to GPT, the BERT generations are of worse quality, but are more diverse. Surprisingly, the outside language model, which was trained on Wikipedia, is less perplexed by the GPT generations than the BERT generations, even though GPT was only trained on romance novels and BERT was trained on romance novels and Wikipedia. On actual data from TBC, the outside language model is about as perplexed as on the BERT generations, which suggests that domain shift is an issue in using a trained language. model for evaluating generations and that the GPT generations might have collapsed to fairly generic and simple sentences.. The perplexity on BERT samples is not absurdly high, and in reading the samples, we find that many are fairly coherent." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Quality metrics of model generations. Perplexity (PPL) is measured using an additional language model (Dauphin et al., 2016). For the WT103 and TBC rows, we sample 1000 sentences from the respective datasets.'\nProblem: 'Which model has the highest perplexity (PPL) on the given data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/185.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Quality metrics of model generations. Perplexity (PPL) is measured using an additional language model (Dauphin et al., 2016). For the WT103 and TBC rows, we sample 1000 sentences from the respective datasets." ], "reasoning": "To find the model with the highest perplexity, we need to look at the PPL column and identify the highest value. The highest PPL value is 331.47, which corresponds to BERT (large).", "question": "Which model has the highest perplexity (PPL) on the given data?", "answer": { "answer": "BERT (large)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe outside language model is less perplexed by the BERT generations than the GPT generations.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/185.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Quality metrics of model generations. Perplexity (PPL) is measured using an additional language model (Dauphin et al., 2016). For the WT103 and TBC rows, we sample 1000 sentences from the respective datasets." ], "reasoning": "The table shows that the perplexity (PPL) for BERT (large) is 331.47 and for BERT (base) is 279.10, both of which are higher than the perplexity for GPT, which is 154.29. This indicates that the outside language model is more perplexed by BERT generations than GPT generations.", "statement": "The outside language model is less perplexed by the BERT generations than the GPT generations.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: F1 scores on six test sets. The last column, Avg, shows the average of F1 scores on MSNBC, AQUAINT, ACE2004, CWEB, and WIKI.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/186.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: F1 scores on six test sets. The last column, Avg, shows the average of F1 scores on MSNBC, AQUAINT, ACE2004, CWEB, and WIKI." ], "reasoning": "Our model outperformed all Wikipedia and Wikipedia + unlabeled data systems on all test sets, and while fully-supervised methods were more accurate on the in-domain AIDA-B test set, our model still performed competitively.", "description": "First, we compare to systems which relied on Wikipedia and those which used Wikipedia along with unlabeled data ('Wikipedia + unlab'), i.e. the top half of Table 1. These methods are comparable to ours, as they use the same type of information as supervision. Our model outperformed all of them on all test sets.. When evaluated on AIDA-B, their scores are still lower than ours, though significantly higher that those of the previous systems suggesting that web links are indeed valuable.. Second, we compare to fully-supervised systems, which were estimated on AIDA-CoNLL documents.. We distinguish results on a test set taken from AIDA-CoNLL (AIDA-B) and the other standard test sets not directly corresponding to the AIDA-CoNLL domain. When tested on the latter, our approach is very effective, on average outperforming fully-supervised techniques.. As expected, on the in-domain test set (AIDA-B), the majority of recent fully-supervised methods are more accurate than our model. However, even on this test set our model is not as far behind, for example, outperforming the system of Guo and Barbosa (2016)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: F1 scores on six test sets. The last column, Avg, shows the average of F1 scores on MSNBC, AQUAINT, ACE2004, CWEB, and WIKI.'\nProblem: 'Which model had the highest average score across all test sets?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/186.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: F1 scores on six test sets. The last column, Avg, shows the average of F1 scores on MSNBC, AQUAINT, ACE2004, CWEB, and WIKI." ], "reasoning": "To find the model with the highest average score, we need to compare the 'Avg' column for all models. The model with the highest value in this column is the one with the highest average score.", "question": "Which model had the highest average score across all test sets?", "answer": { "answer": "Our model" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model by Ganea and Hofmann (2017) outperformed all other models on the AIDA-B test set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/186.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: F1 scores on six test sets. The last column, Avg, shows the average of F1 scores on MSNBC, AQUAINT, ACE2004, CWEB, and WIKI." ], "reasoning": "To verify this statement, we need to compare the AIDA-B scores of all models. The model by Ganea and Hofmann (2017) has a score of 92.22, which is higher than the scores of all other models listed.", "statement": "The model by Ganea and Hofmann (2017) outperformed all other models on the AIDA-B test set.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: F1 scores of our model when it is weakly-supervised and when it is fully-supervised on Wikipedia and on AIDA CoNLL. AIDA-A is our development set. Avg is the average of F1 scores on MSNBC, AQUAINT, ACE2004, CWEB, and WIKI. Each F1 is the mean of five runs.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/187.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F1 scores of our model when it is weakly-supervised and when it is fully-supervised on Wikipedia and on AIDA CoNLL. AIDA-A is our development set. Avg is the average of F1 scores on MSNBC, AQUAINT, ACE2004, CWEB, and WIKI. Each F1 is the mean of five runs." ], "reasoning": "The fully-supervised model trained on AIDA-CoNLL performs better on the AIDA test set but overfits and performs worse on the five out-of-domain test sets.", "description": "The results are shown in Table 2 ('Wikipedia'). The resulting model is significantly less accurate than the one which used unlabeled documents. The score difference is larger. for AIDA-CoNLL test set than for the other 5 test sets.. Additionally we train our model on AIDA-CoNLL, producing its fully-supervised version ('AIDA CoNLL' row in Table 2). Though, as expected, this version is more accurate on AIDA test set, similarly to other fully-supervised methods, it overfits and does not perform that well on the 5 out-of-domain test sets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: F1 scores of our model when it is weakly-supervised and when it is fully-supervised on Wikipedia and on AIDA CoNLL. AIDA-A is our development set. Avg is the average of F1 scores on MSNBC, AQUAINT, ACE2004, CWEB, and WIKI. Each F1 is the mean of five runs.'\nProblem: 'Which model has the highest average score on the AIDA-CoNLL test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/187.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F1 scores of our model when it is weakly-supervised and when it is fully-supervised on Wikipedia and on AIDA CoNLL. AIDA-A is our development set. Avg is the average of F1 scores on MSNBC, AQUAINT, ACE2004, CWEB, and WIKI. Each F1 is the mean of five runs." ], "reasoning": "By comparing the average scores of the models on the AIDA-CoNLL test set, the 'weakly-supervised' model has an average score of 86.18, the 'fully-supervised on Wikipedia' model has an average score of 85.84, and the 'fully-supervised on AIDA-CoNLL' model has an average score of 84.55. The 'weakly-supervised' model has the highest average score.", "question": "Which model has the highest average score on the AIDA-CoNLL test set?", "answer": { "answer": "weakly-supervised" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe fully-supervised model trained on Wikipedia has a higher average score than the weakly-supervised model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/187.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F1 scores of our model when it is weakly-supervised and when it is fully-supervised on Wikipedia and on AIDA CoNLL. AIDA-A is our development set. Avg is the average of F1 scores on MSNBC, AQUAINT, ACE2004, CWEB, and WIKI. Each F1 is the mean of five runs." ], "reasoning": "The table shows that the fully-supervised model trained on Wikipedia has an average score of 85.84, while the weakly-supervised model has an average score of 86.18. Therefore, the statement is refuted.", "statement": "The fully-supervised model trained on Wikipedia has a higher average score than the weakly-supervised model.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Ablation study on AIDA CoNLL development set. Each F1 score is the mean of five runs.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/188.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation study on AIDA CoNLL development set. Each F1 score is the mean of five runs." ], "reasoning": "Our model outperforms the baseline by 1.6% and drops to 82.4% F1 without local context modeling.", "description": "As we do not want to test multiple systems on the final test set, we report the remaining ablations on the development set (AIDA-A), Table 3.. we constructed a baseline which only relies on link statistics in Wikipedia as well as string similarity (we refereed to its scoring function as sc). It appears surprisingly strong, however, we still outperform it by 1.6% (see Table 3).. When we use only global coherence (i.e. only second term in expression (1)) and drop any modeling of local context on the disambiguation stage, the performance drops very substantially (to 82.4% F1, see Table 3).. Without using local scores the disambiguation model appears to be even less accurate than our 'no-statisticaldisambiguation' baseline. It is also important to have an accurate global model: not using global attention results in a 1.2% drop in performance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Ablation study on AIDA CoNLL development set. Each F1 score is the mean of five runs.'\nProblem: 'What is the F1 score of the model when global attention is not used?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/188.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation study on AIDA CoNLL development set. Each F1 score is the mean of five runs." ], "reasoning": "The table shows that the F1 score of the model without attention is 86.82%.", "question": "What is the F1 score of the model when global attention is not used?", "answer": { "answer": "86.82" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model without local context modeling has an F1 score higher than 85%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/188.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation study on AIDA CoNLL development set. Each F1 score is the mean of five runs." ], "reasoning": "The table shows that the F1 score of the model without local context modeling is 82.41%, which is lower than 85%.", "statement": "The model without local context modeling has an F1 score higher than 85%.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Accuracy (%) by NER type on AIDA-A.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/189.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy (%) by NER type on AIDA-A." ], "reasoning": "Our model achieves 97.20% accuracy for PER, which is only 0.53% lower than the fully-supervised model's 97.73% accuracy.", "description": "Figure 4 shows the accuracy of two systems for different NER (named entity recognition) types. We consider four types: location (LOC), organization (ORG), person (PER), and miscellany (MICS). These types are given in CoNLL 2003 dataset, which was used as a basis for AIDA CoNLL. Our model is accurate for PER, achieving accuracy of about 97%, only 0.53% lower than the supervised model." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Accuracy (%) by NER type on AIDA-A.'\nProblem: 'What is the accuracy difference between our model and the fully-supervised model for the ORG type?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/189.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy (%) by NER type on AIDA-A." ], "reasoning": "The accuracy of our model for ORG is 89.51%, and the accuracy of the fully-supervised model for ORG is 92.70%. The difference is 92.70% - 89.51% = 3.19%.", "question": "What is the accuracy difference between our model and the fully-supervised model for the ORG type?", "answer": "So, the answer is {\"answer\": \"3.19%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur model has higher accuracy for the LOC type compared to the fully-supervised model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/189.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy (%) by NER type on AIDA-A." ], "reasoning": "The table shows that our model has an accuracy of 85.53% for LOC, while the fully-supervised model has an accuracy of 89.41% for LOC. Therefore, our model does not have higher accuracy for the LOC type.", "statement": "Our model has higher accuracy for the LOC type compared to the fully-supervised model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Model performance (P, R and F) for development sets for all MWE and only discontinuous ones (%: proportion of discontinuous MWES)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/190.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Model performance (P, R and F) for development sets for all MWE and only discontinuous ones (%: proportion of discontinuous MWES)" ], "reasoning": "GCN-based outperforms Att-based in MWE-based F-score for three out of four languages, and combining GCN with attention using highway networks results in further improvements for EN, FR, and FA.", "description": "We perform hyperparameter optimisation and make comparisons among our systems, including GCN + Bi-LSTM (GCN-based), CNN + attention + Bi-LSTM (Attbased), and their combination using a highway layer (H-combined) in Table 1.. Systems are evaluated using two types of precision, recall and F-score measures: strict MWEbased scores (every component of an MWE should be correctly tagged to be considered as true positive), and token-based scores (a partial match between a predicted and a gold MWE would be considered as true positive). We report results for all MWEs as well as discontinuous ones specifically.. GCN-based outperforms Att-based and they both outperform the strong baseline in terms of MWE-based F-score in three out of four languages. Combining GCN with attention using highway networks results in further improvements for EN, FR and FA. The Hcombined model consistently exceeds the baseline for all languages.. GCN and H-combined models each show significant improvement with regard to discontinuous MWEs, regardless of the proportion of such expressions.. The overall results confirm our assumption that a hybrid architecture can mitigate errors of individual models and bolster their strengths." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Model performance (P, R and F) for development sets for all MWE and only discontinuous ones (%: proportion of discontinuous MWES)'\nProblem: 'Which model has the highest MWE-based F-score for French?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/190.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Model performance (P, R and F) for development sets for all MWE and only discontinuous ones (%: proportion of discontinuous MWES)" ], "reasoning": "To find the model with the highest MWE-based F-score for French, look at the 'All MWE-based' F column for FR. The highest value is 76.56, which corresponds to the H-combined model.", "question": "Which model has the highest MWE-based F-score for French?", "answer": "So, the answer is {\"answer\": \"H-combined\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe GCN-based model has the highest discontinuous MWE-based F-score for German.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/190.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Model performance (P, R and F) for development sets for all MWE and only discontinuous ones (%: proportion of discontinuous MWES)" ], "reasoning": "To verify this statement, check the 'Discontinuous MWE-based F' column for DE. The highest value is 57.53, which corresponds to the H-combined model, not the GCN-based model.", "statement": "The GCN-based model has the highest discontinuous MWE-based F-score for German.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 9: MAE of L-biLSTM(2)-S and L-biLSTM(2)-S+lexfeats, for predictions on events in UDS-IH2-dev that are xcomp-governed by an infinitival-taking verb.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/191.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: MAE of L-biLSTM(2)-S and L-biLSTM(2)-S+lexfeats, for predictions on events in UDS-IH2-dev that are xcomp-governed by an infinitival-taking verb." ], "reasoning": "Despite the overall underperformance, Table 9 indicates that these features can still enhance performance in specific instances where they are present.", "description": "Despite the underperformance of these features overall, Table 9 shows that they may still improve performance in the subset of instances where they appear." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 9: MAE of L-biLSTM(2)-S and L-biLSTM(2)-S+lexfeats, for predictions on events in UDS-IH2-dev that are xcomp-governed by an infinitival-taking verb.'\nProblem: 'Which verb has the highest performance improvement when using +lexfeats compared to L-biLSTM(2)-S?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/191.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: MAE of L-biLSTM(2)-S and L-biLSTM(2)-S+lexfeats, for predictions on events in UDS-IH2-dev that are xcomp-governed by an infinitival-taking verb." ], "reasoning": "To find the verb with the highest performance improvement, compare the values in the +lexfeats column to the L-biLSTM(2)-S column and identify the verb with the largest positive difference.", "question": "Which verb has the highest performance improvement when using +lexfeats compared to L-biLSTM(2)-S?", "answer": "So, the answer is {\"answer\": \"decide to\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe verb 'get to' has a higher performance score with L-biLSTM(2)-S than with +lexfeats.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/191.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: MAE of L-biLSTM(2)-S and L-biLSTM(2)-S+lexfeats, for predictions on events in UDS-IH2-dev that are xcomp-governed by an infinitival-taking verb." ], "reasoning": "The table shows that 'get to' has a score of 1.55 with L-biLSTM(2)-S and 1.43 with +lexfeats, indicating that the performance is indeed higher with L-biLSTM(2)-S.", "statement": "The verb 'get to' has a higher performance score with L-biLSTM(2)-S than with +lexfeats.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparing the performance of the systems on test data in terms of MWE-based F-score'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/192.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparing the performance of the systems on test data in terms of MWE-based F-score" ], "reasoning": "The hybrid architecture (H-combined) outperforms other models in most cases, except for FA where GCN-based performs best for discontinuous MWEs.", "description": "we show the superior performance (in terms of MWE-based F-score) of our top systems on the test data compared to the baseline and stateof-the-art systems, namely, ATILF-LLF (Al Saied et al., 2017) and SHOMA (Taslimipoor and Rohanian, 2018). GCN works the best for discontinuous MWEs in EN and FA, while H-combined outperforms based on results for all MWEs except for FA.. The overall results confirm our assumption that a hybrid architecture can mitigate errors of individual models and bolster their strengths." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparing the performance of the systems on test data in terms of MWE-based F-score'\nProblem: 'Which system performs best for all MWEs in EN and FA?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/192.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparing the performance of the systems on test data in terms of MWE-based F-score" ], "reasoning": "To determine the best system for all MWEs in EN and FA, compare the 'All' scores for each system in the EN and FA columns. H-combined has the highest scores in both EN (41.91) and FA (80.04).", "question": "Which system performs best for all MWEs in EN and FA?", "answer": "So, the answer is {\"answer\": \"H-combined\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGCN-based system outperforms the baseline for discontinuous MWEs in DE.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/192.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparing the performance of the systems on test data in terms of MWE-based F-score" ], "reasoning": "Compare the 'Discontinuous' scores for GCN-based and baseline in the DE column. GCN-based has a score of 54.87, which is higher than the baseline score of 53.94.", "statement": "GCN-based system outperforms the baseline for discontinuous MWEs in DE.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results for pretraining experiments on development sets except where noted. Bold denotes best result overall. Underlining denotes an average score surpassing the Random baseline. See Section 6 for discussion of WNLI results (*).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/193.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results for pretraining experiments on development sets except where noted. Bold denotes best result overall. Underlining denotes an average score surpassing the Random baseline. See Section 6 for discussion of WNLI results (*)." ], "reasoning": "The CoLA task benefits significantly from LM pretraining, while the STS task does not benefit substantially from LM pretraining. Multitask models do not reliably combine knowledge from each task but generalize better on test data for tasks like STS and MNLI.", "description": "Tables 2 and 3 respectively show results for our pretraining and intermediate training experiments.. From Table 2, among target tasks, we find the grammar-related CoLA task benefits dramatically from LM pretraining: The results achieved with LM pretraining are significantly better than the results achieved without. In contrast, the meaningoriented STS sees good results with several kinds of pretraining, but does not benefit substantially from LM pretraining. Among pretraining tasks, language modeling performs best, followed by MNLI. The remaining pretraining tasks yield performance near that of the random baseline. Even our single-task baseline gets less than a one point gain over this simple baseline. The multitask models are tied or outperformed by models trained on one of their constituent tasks, suggesting that our approach to multitask learning does not reliably produce models that productively combine the knowledge taught by each task. However, of the two models that perform best on the development data, the multitask model generalizes better than the single-task model on test data for tasks like STS and MNLI where the test set contains out-of-domain data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results for pretraining experiments on development sets except where noted. Bold denotes best result overall. Underlining denotes an average score surpassing the Random baseline. See Section 6 for discussion of WNLI results (*).'\nProblem: 'Which pretraining task performs best on the CoLA task, and how does it compare to the random baseline?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/193.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results for pretraining experiments on development sets except where noted. Bold denotes best result overall. Underlining denotes an average score surpassing the Random baseline. See Section 6 for discussion of WNLI results (*)." ], "reasoning": "The table shows that the LM BWB pretraining task achieves the highest score of 30.7 on the CoLA task, which is significantly better than the random baseline score of 16.9.", "question": "Which pretraining task performs best on the CoLA task, and how does it compare to the random baseline?", "answer": "So, the answer is {\"answer\": \"LM BWB\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe multitask model performs better than the single-task model on the STS task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/193.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results for pretraining experiments on development sets except where noted. Bold denotes best result overall. Underlining denotes an average score surpassing the Random baseline. See Section 6 for discussion of WNLI results (*)." ], "reasoning": "The table shows that the multitask model (MTL GLUE) achieves a score of 82.6 on the STS task, which is lower than the single-task model score of 81.7. Therefore, the statement is refuted.", "statement": "The multitask model performs better than the single-task model on the STS task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results for intermediate training experiments on development sets except where noted. E and B respectively denote ELMo and BERT experiments. Bold denotes best scores by section. Underlining denotes average scores better than the single-task baseline. See Section 6 for discussion of WNLI results (*). BERT Base numbers are from Devlin et al. (2019).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/194.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for intermediate training experiments on development sets except where noted. E and B respectively denote ELMo and BERT experiments. Bold denotes best scores by section. Underlining denotes average scores better than the single-task baseline. See Section 6 for discussion of WNLI results (*). BERT Base numbers are from Devlin et al. (2019)." ], "reasoning": "Using ELMo improves over training the encoder from scratch, with MNLI and English–German translation performing best. BERT consistently outperforms ELMo and pretraining from scratch.", "description": "Tables 2 and 3 respectively show results for our pretraining and intermediate training experiments.. Looking to Table 3, using ELMo uniformly improves over training the encoder from scratch. The ELMo-augmented random baseline is strong, lagging behind the single-task baseline by less than a point. Most intermediate tasks beat the random baseline, but several fail to significantly outperform the single-task baseline. MNLI and English–German translation perform best with ELMo, with SkipThought and DisSent also beating the single-task baseline. Intermediate multitask training on all the non-GLUE tasks produces our best-performing ELMo model. Using BERT consistently outperforms ELMo and pretraining from scratch. We find that intermediate training on each of MNLI, QQP, and STS leads to improvements over no intermediate training, while intermediate training on the other tasks harms transfer performance. The improve. ments gained via STS, a small-data task, versus the negative impact of fairly large-data tasks (e.g. QNLI), suggests that the benefit of intermediate training is not solely due to additional training, but that the signal provided by the intermediate task complements the original language modeling objective. Intermediate training on generation tasks such as MT and SkipThought significantly impairs BERT's transfer ability. We speculate that this degradation may be due to catastrophic forgetting in fine-tuning for a task substantially different from the tasks BERT was originally trained on. This phenomenon might be mitigated in our ELMo models via the frozen encoder and skip connection. On the test set, we lag slightly behind the BERT base results from Devlin et al. (2019), likely due in part to our limited hyperparameter tuning." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Results for intermediate training experiments on development sets except where noted. E and B respectively denote ELMo and BERT experiments. Bold denotes best scores by section. Underlining denotes average scores better than the single-task baseline. See Section 6 for discussion of WNLI results (*). BERT Base numbers are from Devlin et al. (2019).'\nProblem: 'Which intermediate task leads to the best performance with ELMo?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/194.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for intermediate training experiments on development sets except where noted. E and B respectively denote ELMo and BERT experiments. Bold denotes best scores by section. Underlining denotes average scores better than the single-task baseline. See Section 6 for discussion of WNLI results (*). BERT Base numbers are from Devlin et al. (2019)." ], "reasoning": "By examining the table, we see that intermediate multitask training on all the non-GLUE tasks produces the best-performing ELMo model.", "question": "Which intermediate task leads to the best performance with ELMo?", "answer": { "answer": "MTL Non-GLUE E" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIntermediate training on generation tasks such as MT and SkipThought significantly improves BERT's transfer ability.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/194.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for intermediate training experiments on development sets except where noted. E and B respectively denote ELMo and BERT experiments. Bold denotes best scores by section. Underlining denotes average scores better than the single-task baseline. See Section 6 for discussion of WNLI results (*). BERT Base numbers are from Devlin et al. (2019)." ], "reasoning": "The description states that intermediate training on generation tasks such as MT and SkipThought significantly impairs BERT's transfer ability, which refutes the statement.", "statement": "Intermediate training on generation tasks such as MT and SkipThought significantly improves BERT's transfer ability.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Pearson correlations between performances on a subset of all pairs of target tasks, measured over all runs reported in Table 2. The Avg column shows the correlation between performance on a target task and the overall GLUE score. For QQP and STS, the correlations are computed respectively using F1 and Pearson correlation. Negative correlations are underlined.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/195.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Pearson correlations between performances on a subset of all pairs of target tasks, measured over all runs reported in Table 2. The Avg column shows the correlation between performance on a target task and the overall GLUE score. For QQP and STS, the correlations are computed respectively using F1 and Pearson correlation. Negative correlations are underlined." ], "reasoning": "The table shows that different target tasks benefit from different forms of pretraining, with CoLA showing a strong correlation with overall GLUE scores, while WNLI and STS have negative correlations.", "description": "Table 4 presents an alternative view of the results of the pretraining experiment (Table 2): The table shows correlations between pairs of target tasks over the space of pretrained encoders. The correlations reflect the degree to which the performance on one target task with some encoder predicts performance on another target task with the same encoder. See Appendix D for the full table and similar tables for intermediate ELMo and BERT experiments. Many correlations are low, suggesting that different tasks benefit from different forms of pretraining to a substantial degree, and bolstering the observation that no single pretraining task yields good performance on all target tasks. For reasons noted earlier, the models that tended to perform best overall also tended to overfit the WNLI training set most, leading to a negative correlation between WNLI and overall GLUE score. STS also shows a negative correlation, likely due to the observation that it does not benefit from LM pretraining. In contrast, CoLA shows a strong correlation with the overall GLUE scores, but has weak or negative correlations with many tasks: The use of LM pretraining dramatically improves CoLA performance, but most other forms of pretraining have little effect." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Pearson correlations between performances on a subset of all pairs of target tasks, measured over all runs reported in Table 2. The Avg column shows the correlation between performance on a target task and the overall GLUE score. For QQP and STS, the correlations are computed respectively using F1 and Pearson correlation. Negative correlations are underlined.'\nProblem: 'Which task has the highest correlation with the overall GLUE score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/195.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Pearson correlations between performances on a subset of all pairs of target tasks, measured over all runs reported in Table 2. The Avg column shows the correlation between performance on a target task and the overall GLUE score. For QQP and STS, the correlations are computed respectively using F1 and Pearson correlation. Negative correlations are underlined." ], "reasoning": "The table shows the correlation values between different tasks and the overall GLUE score (Avg). The highest correlation value with Avg is 0.86 for CoLA.", "question": "Which task has the highest correlation with the overall GLUE score?", "answer": { "answer": "CoLA" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSTS has a positive correlation with the overall GLUE score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/195.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Pearson correlations between performances on a subset of all pairs of target tasks, measured over all runs reported in Table 2. The Avg column shows the correlation between performance on a target task and the overall GLUE score. For QQP and STS, the correlations are computed respectively using F1 and Pearson correlation. Negative correlations are underlined." ], "reasoning": "The table shows that the correlation between STS and the overall GLUE score (Avg) is -0.36, which is a negative correlation.", "statement": "STS has a positive correlation with the overall GLUE score.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: NE-tags of numbers in Wikipedia.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/196.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: NE-tags of numbers in Wikipedia." ], "reasoning": "Temporal-related numbers are the most frequent NE tags at 54.28%, while 40.13% are classified as unspecific NUMBER.", "description": "The distribution of their named-entity (NE) tags, according to Stanford NE-tagger, is shown in Table 1. While temporal-related numbers are the most frequent, around 40% are classified only as unspecific NUMBER. By manually checking 100 random NUMBERs, we observed that 47 are relation cardinalities,1 i.e., approximately 18.86% of all numbers in Wikipedia are relation cardinalities." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: NE-tags of numbers in Wikipedia.'\nProblem: 'What percentage of NE tags are classified as temporal-related and unspecific NUMBER combined?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/196.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: NE-tags of numbers in Wikipedia." ], "reasoning": "The table shows that temporal-related NE tags are 54.28% and unspecific NUMBER tags are 40.13%. Adding these percentages gives the combined percentage.", "question": "What percentage of NE tags are classified as temporal-related and unspecific NUMBER combined?", "answer": "So, the answer is {\"answer\": \"94.41%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRelation cardinalities make up 18.86% of all NE tags.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/196.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: NE-tags of numbers in Wikipedia." ], "reasoning": "The table shows that Relation cardinality NE tags have a frequency of 18.86%, which matches the statement.", "statement": "Relation cardinalities make up 18.86% of all NE tags.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Number of Wikidata entities as subjects (#s) of each predicate (p), and evaluation results on manually annotated randomly selected subjects that have at least an object.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/197.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Number of Wikidata entities as subjects (#s) of each predicate (p), and evaluation results on manually annotated randomly selected subjects that have at least an object." ], "reasoning": "The CRF-based method significantly outperforms the random-number baseline in precision and F1-score for 'has part', 'admin. territ. entity', and 'child' relations, especially when using only-nummod.", "description": "Table 2 shows the performance of our CRF-based method in finding the correct relation cardinality, evaluated on manually annotated 20 (has part), 100 (admin. terr. entity) and 200 (child and spouse) randomly selected subjects that have at least one object.. The random-number baseline achieves a precision of 5% (has part), 3.5% (admin. territ. entity), 0% (spouse) and 11.2% (child). Compared to that, especially using only-nummod, our method gives encouraging results for has part, admin. territ. entity and child, with 30-50% precision and around 30% F1-score.. As shown by the last row of Table 2, higher quality of training data can considerably boost the performance of cardinality extraction." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Number of Wikidata entities as subjects (#s) of each predicate (p), and evaluation results on manually annotated randomly selected subjects that have at least an object.'\nProblem: 'What is the precision of the CRF-based method using only-nummod for the 'contains admin. territ. entity' relation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/197.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Number of Wikidata entities as subjects (#s) of each predicate (p), and evaluation results on manually annotated randomly selected subjects that have at least an object." ], "reasoning": "The table shows the precision of the CRF-based method using only-nummod for the 'contains admin. territ. entity' relation as 0.548.", "question": "What is the precision of the CRF-based method using only-nummod for the 'contains admin. territ. entity' relation?", "answer": { "answer": "0.548" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe CRF-based method using only-nummod achieves a higher precision for 'child' relations compared to 'has part' relations.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/197.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Number of Wikidata entities as subjects (#s) of each predicate (p), and evaluation results on manually annotated randomly selected subjects that have at least an object." ], "reasoning": "The table shows that the precision of the CRF-based method using only-nummod for 'child' relations is 0.320, which is lower than the precision for 'has part' relations, which is 0.353.", "statement": "The CRF-based method using only-nummod achieves a higher precision for 'child' relations compared to 'has part' relations.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: LAS on the test sets, the best LAS in each group is marked in bold face.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/198.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: LAS on the test sets, the best LAS in each group is marked in bold face." ], "reasoning": "The CNN model performs better than the WORD and W2V models on average, and the LSTM model improves significantly when combined with the WORD model.", "description": "The experimental results are shown in Table 1, with Int denoting internal comparisons (with three groups) and Ext denoting external comparisons, the highest LAS in each group is marked in bold face.. In the first group, we compare the LAS of the four single models WORD, W2V, LSTM, and CNN. In macro average of all languages, the CNN model performs 2.17% higher than the WORD model, and 1.24% higher than the W2V model. The LSTM model, however, performs only 0.9% higher than the WORD model and 1.27% lower than the CNN model.. In the second group, we observe that the additional word-lookup model does not significantly improve the CNN moodel (from 82.75% in CNN to 82.90% in CNN+WORD on average) while the LSTM model is improved by a much larger margin (from 81.48% in LSTM to 82.56% in LSTM+WORD on average). This suggests that the CNN model has already learned the most important information from the the word forms, while the LSTM model has not. Also, the combined CNN+WORD model is still better than the LSTM+WORD model, despite the large improvement in the latter.. While comparing to the best published results (Björkelund et al., 2013, 2014), we have to note that their approach uses explicit morphological features, ensemble, ranking, etc., which all can boost parsing performance. We only use a greedy parser with much fewer features, but bridge the 6 points gap between the previous best greedy parser and the best published result by more than one half.. On average, the B15-LSTM model improves their own baseline by 1.1%, similar to the 0.9% improvement of our LSTM model, which is much smaller than the 2.17% improvement of the CNN model. Furthermore, the CNN model is improved from a strong baseline: our WORD model performs already 2.22% higher than the B15-WORD model.. Comparing the individual performances on each language, we observe that the CNN model almost always outperforms the WORD model except for Hebrew. However, both LSTM and B15-LSTM perform higher than baseline only on the three agglutinative languages (Basque, Hungarian, and Korean), and lower than baseline on the other six." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: LAS on the test sets, the best LAS in each group is marked in bold face.'\nProblem: 'Which model has the highest average LAS in the internal comparisons?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/198.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: LAS on the test sets, the best LAS in each group is marked in bold face." ], "reasoning": "By examining the 'Avg' column for internal comparisons (Int), the CNN+WORD model has the highest average LAS of 82.90%.", "question": "Which model has the highest average LAS in the internal comparisons?", "answer": { "answer": "CNN+WORD" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LSTM model performs better than the CNN model in the internal comparisons.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/198.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: LAS on the test sets, the best LAS in each group is marked in bold face." ], "reasoning": "In the internal comparisons, the CNN model has an average LAS of 82.75%, while the LSTM model has an average LAS of 81.48%, indicating that the CNN model performs better.", "statement": "The LSTM model performs better than the CNN model in the internal comparisons.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: LAS improvements by CNN and LSTM in the IV and OOV cases on the development sets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/199.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: LAS improvements by CNN and LSTM in the IV and OOV cases on the development sets." ], "reasoning": "The improvements of both models in the OOV case are larger than in the IV case, indicating that character composition models alleviate the OOV problem.", "description": "Table 2 shows the results, where the two cases are denoted as ΔIV and ΔOOV. The general trend in the results is that the improvements of both models in the OOV case are larger than in the IV case, which means that the character composition models indeed alleviates the OOV problem. Also, CNN improves on seven languages in the IV case and eight languages in the OOV case, and it performs consistently better than LSTM in both cases." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: LAS improvements by CNN and LSTM in the IV and OOV cases on the development sets.'\nProblem: 'How many languages did CNN improve on in the IV and OOV cases combined?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/199.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: LAS improvements by CNN and LSTM in the IV and OOV cases on the development sets." ], "reasoning": "CNN improved on seven languages in the IV case and eight languages in the OOV case. Adding these together gives 15 languages.", "question": "How many languages did CNN improve on in the IV and OOV cases combined?", "answer": { "answer": "15" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nLSTM performs better than CNN in the IV case.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/199.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: LAS improvements by CNN and LSTM in the IV and OOV cases on the development sets." ], "reasoning": "The average improvement for CNN in the IV case is 0.81, while for LSTM it is 0.22. Since 0.81 is greater than 0.22, CNN performs better than LSTM in the IV case.", "statement": "LSTM performs better than CNN in the IV case.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Macro-averaged P/R/F1 on the dev set for the entity typing task of Choi et al. (2018) comparing various systems. ELMo gives a substantial improvement over baselines. Over an ELMo-equipped model, data augmentation using the method of Choi et al. (2018) gives no benefit. However, our denoising technique allow us to effectively incorporate distant data, matching the results of a BERT model on this task Devlin et al. (2018).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/100.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Macro-averaged P/R/F1 on the dev set for the entity typing task of Choi et al. (2018) comparing various systems. ELMo gives a substantial improvement over baselines. Over an ELMo-equipped model, data augmentation using the method of Choi et al. (2018) gives no benefit. However, our denoising technique allow us to effectively incorporate distant data, matching the results of a BERT model on this task Devlin et al. (2018)." ], "reasoning": "Our model with no augmentation matches Choi et al. (2018) with augmentation, and incorporating ELMo improves both precision and recall. Denoising distant data improves recall significantly, especially in the general class, while BERT performs similarly to ELMo with denoised distant data.", "description": "Table 1 compares the performance of these systems on the development set. Our model with no augmentation already matches the system of Choi et al. (2018) with augmentation, and incorporating ELMo gives further gains on both precision and recall. On top of this model, adding the distantly-annotated data lowers the performance; the loss function-based approach of (Choi et al., 2018) does not sufficiently mitigate the noise in this data. However, denoising makes the distantlyannotated data useful, improving recall by a substantial margin especially in the general class.. BERT performs similarly to ELMo with denoised distant data. As can be seen in the performance breakdown, BERT gains from improvements in recall in the fine class." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Macro-averaged P/R/F1 on the dev set for the entity typing task of Choi et al. (2018) comparing various systems. ELMo gives a substantial improvement over baselines. Over an ELMo-equipped model, data augmentation using the method of Choi et al. (2018) gives no benefit. However, our denoising technique allow us to effectively incorporate distant data, matching the results of a BERT model on this task Devlin et al. (2018).'\nProblem: 'Which model has the highest recall in the general class?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/100.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Macro-averaged P/R/F1 on the dev set for the entity typing task of Choi et al. (2018) comparing various systems. ELMo gives a substantial improvement over baselines. Over an ELMo-equipped model, data augmentation using the method of Choi et al. (2018) gives no benefit. However, our denoising technique allow us to effectively incorporate distant data, matching the results of a BERT model on this task Devlin et al. (2018)." ], "reasoning": "To find the model with the highest recall in the general class, we need to compare the 'General R' column for all models. The highest value in this column is 80.7, which corresponds to 'Ours + ELMo w augmentation + filter & relabel'.", "question": "Which model has the highest recall in the general class?", "answer": "So, the answer is {\"answer\": \"Ours + ELMo w augmentation + filter & relabel\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model 'Ours + ELMo w/o augmentation' has the highest precision in the general class.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/100.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Macro-averaged P/R/F1 on the dev set for the entity typing task of Choi et al. (2018) comparing various systems. ELMo gives a substantial improvement over baselines. Over an ELMo-equipped model, data augmentation using the method of Choi et al. (2018) gives no benefit. However, our denoising technique allow us to effectively incorporate distant data, matching the results of a BERT model on this task Devlin et al. (2018)." ], "reasoning": "To verify this statement, we need to check the 'General P' column. The highest value in this column is 69.3, which corresponds to 'Ours + ELMo w/o augmentation'. Therefore, the statement is supported.", "statement": "The model 'Ours + ELMo w/o augmentation' has the highest precision in the general class.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Macro-averaged P/R/F1 on the test set for the entity typing task of Choi et al. (2018). Our denoising approach gives substantial gains over naive augmentation and matches the performance of a BERT model.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/101.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Macro-averaged P/R/F1 on the test set for the entity typing task of Choi et al. (2018). Our denoising approach gives substantial gains over naive augmentation and matches the performance of a BERT model." ], "reasoning": "Our approach, particularly with ELMo augmentation and additional filtering and relabeling, achieves the highest F1 score of 40.2, outperforming other models including the concurrently-published Xiong et al. (2019).", "description": "Table 2 shows the performance of all settings on the test set, with the same trend as the performance on the development set. Our approach outperforms the concurrently-published Xiong et al. (2019);" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Macro-averaged P/R/F1 on the test set for the entity typing task of Choi et al. (2018). Our denoising approach gives substantial gains over naive augmentation and matches the performance of a BERT model.'\nProblem: 'Which model achieved the highest F1 score and what was the score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/101.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Macro-averaged P/R/F1 on the test set for the entity typing task of Choi et al. (2018). Our denoising approach gives substantial gains over naive augmentation and matches the performance of a BERT model." ], "reasoning": "By examining the F1 column, the highest score is 40.2, achieved by both 'Ours + ELMo w augmentation + filter & relabel' and 'BERT-Base, Uncased'.", "question": "Which model achieved the highest P score?", "answer": "So, the answer is {\"answer\": \"Ours + ELMo w augmentation\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model 'Ours + ELMo w/o augmentation' achieved the highest precision score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/101.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Macro-averaged P/R/F1 on the test set for the entity typing task of Choi et al. (2018). Our denoising approach gives substantial gains over naive augmentation and matches the performance of a BERT model." ], "reasoning": "By examining the Precision (P) column, the highest precision score is 55.8, achieved by 'Ours + ELMo w/o augmentation'.", "statement": "The model 'Ours + ELMo w/o augmentation' achieved the highest precision score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Macro-averaged P/R/F1 on the dev set for the entity typing task of Choi et al. (2018) with various types of augmentation added. The customized loss from Choi et al. (2018) actually causes a decrease in performance from adding any of the datasets. Heuristics can improve incorporation of this data: a relabeling heuristic (Pair) helps on HEAD and a filtering heuristic (Overlap) is helpful in both settings. However, our trainable filtering and relabeling models outperform both of these techniques.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/102.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Macro-averaged P/R/F1 on the dev set for the entity typing task of Choi et al. (2018) with various types of augmentation added. The customized loss from Choi et al. (2018) actually causes a decrease in performance from adding any of the datasets. Heuristics can improve incorporation of this data: a relabeling heuristic (Pair) helps on HEAD and a filtering heuristic (Overlap) is helpful in both settings. However, our trainable filtering and relabeling models outperform both of these techniques." ], "reasoning": "FILTER & RELABEL outperforms all baselines, with OVERLAP being the most effective heuristic technique.", "description": "Table 3 compares the results on the development set.. On top of the baseline ORIGINAL, adding synonyms and hypernyms by consulting external knowledge does not improve the performance.. the PAIR technique results in small gains over ORIGINAL. OVERLAP is the most ef. fective heuristic technique. This simple. heuristic improves recall on EL.. FILTER,. gives similar improvements to PAIR and OVERLAP on the HEAD setting,. RELABEL and OVERLAP both improve performance on both EL and HEAD while other methods do poorly on EL.. FILTER & RELABEL outperforms all the baselines." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Macro-averaged P/R/F1 on the dev set for the entity typing task of Choi et al. (2018) with various types of augmentation added. The customized loss from Choi et al. (2018) actually causes a decrease in performance from adding any of the datasets. Heuristics can improve incorporation of this data: a relabeling heuristic (Pair) helps on HEAD and a filtering heuristic (Overlap) is helpful in both settings. However, our trainable filtering and relabeling models outperform both of these techniques.'\nProblem: 'Which denoising method has the highest EL&HEAD F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/102.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Macro-averaged P/R/F1 on the dev set for the entity typing task of Choi et al. (2018) with various types of augmentation added. The customized loss from Choi et al. (2018) actually causes a decrease in performance from adding any of the datasets. Heuristics can improve incorporation of this data: a relabeling heuristic (Pair) helps on HEAD and a filtering heuristic (Overlap) is helpful in both settings. However, our trainable filtering and relabeling models outperform both of these techniques." ], "reasoning": "By examining the EL&HEAD F1 column, the highest score is 40.1, achieved by the Filter&Relabel method.", "question": "Which denoising method has the highest EL&HEAD F1 score?", "answer": "So, the answer is {\"answer\": \"Filter&Relabel\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe PAIR technique results in the highest EL&HEAD F1 score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/102.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Macro-averaged P/R/F1 on the dev set for the entity typing task of Choi et al. (2018) with various types of augmentation added. The customized loss from Choi et al. (2018) actually causes a decrease in performance from adding any of the datasets. Heuristics can improve incorporation of this data: a relabeling heuristic (Pair) helps on HEAD and a filtering heuristic (Overlap) is helpful in both settings. However, our trainable filtering and relabeling models outperform both of these techniques." ], "reasoning": "The table shows that the highest EL&HEAD F1 score is 40.1, achieved by Filter&Relabel, not PAIR.", "statement": "The PAIR technique results in the highest EL&HEAD F1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Test results on OntoNotes. Denoising helps substantially even in this reduced setting. Using fewer distant examples, we nearly match the performance using the data from Choi et al. (2018) (see text).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/103.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Test results on OntoNotes. Denoising helps substantially even in this reduced setting. Using fewer distant examples, we nearly match the performance using the data from Choi et al. (2018) (see text)." ], "reasoning": "Denoising and augmentation significantly improve model performance, with the best results achieved by our model with augmented training data.", "description": "Table 4 lists the results on the OntoNotes test set following the adaptation setting of Choi et al. (2018).. denoising significantly improves over naive incorporation of distant data,. BERT still performs well but not as well as our model with augmented training data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Test results on OntoNotes. Denoising helps substantially even in this reduced setting. Using fewer distant examples, we nearly match the performance using the data from Choi et al. (2018) (see text).'\nProblem: 'Which model achieved the highest Ma-F1 score and what was the score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/103.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Test results on OntoNotes. Denoising helps substantially even in this reduced setting. Using fewer distant examples, we nearly match the performance using the data from Choi et al. (2018) (see text)." ], "reasoning": "By examining the table, the model 'Ours + ELMo w augmentation + filter & relabel' achieved the highest Ma-F1 score of 84.5.", "question": "Which model achieved the highest Ma-F1 score?", "answer": "So, the answer is {\"answer\": \"Ours + ELMo w augmentation + filter & relabel\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model by Choi et al. (2018) achieved the highest accuracy.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/103.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Test results on OntoNotes. Denoising helps substantially even in this reduced setting. Using fewer distant examples, we nearly match the performance using the data from Choi et al. (2018) (see text)." ], "reasoning": "The table shows that the model 'Ours + ELMo w augmentation' achieved the highest accuracy of 64.9, not the model by Choi et al. (2018).", "statement": "The model by Choi et al. (2018) achieved the highest accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: The average number of types added or deleted by the relabeling function per example. The right-most column shows that the rate of examples discarded by the filtering function.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/104.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The average number of types added or deleted by the relabeling function per example. The right-most column shows that the rate of examples discarded by the filtering function." ], "reasoning": "HEAD examples have more general types added than EL examples, while ultra-fine types are added more frequently to EL examples. The filtering function discards similar numbers of examples for both datasets.", "description": "Table 5 reports the average numbers of types added/deleted by the relabeling function and the ratio of examples discarded by the filtering function.. The HEAD examples have more general types added than the EL examples since the noisy HEAD labels are typically finer. Fine-grained types are added to both EL and HEAD examples less frequently. Ultra-fine examples are frequently added to both datasets, with more added to EL;. The filtering function discards similar numbers of examples for the EL and HEAD data: 9.4% and 10% respectively." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: The average number of types added or deleted by the relabeling function per example. The right-most column shows that the rate of examples discarded by the filtering function.'\nProblem: 'Which dataset has more ultra-fine types added, and what is the percentage of examples discarded by the filtering function for that dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/104.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The average number of types added or deleted by the relabeling function per example. The right-most column shows that the rate of examples discarded by the filtering function." ], "reasoning": "The table shows that EL has more ultra-fine types added (2.03) compared to HEAD (1.15). The filtering function discards 9.4% of EL examples.", "question": "Which dataset has more ultra-fine types added?", "answer": "So, the answer is {\"answer\": \"EL\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe filtering function discards a higher percentage of examples for the HEAD dataset compared to the EL dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/104.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The average number of types added or deleted by the relabeling function per example. The right-most column shows that the rate of examples discarded by the filtering function." ], "reasoning": "The table shows that the filtering function discards 10% of HEAD examples and 9.4% of EL examples. Therefore, the statement is correct.", "statement": "The filtering function discards a higher percentage of examples for the HEAD dataset compared to the EL dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Properties of the Ubuntu and Samsung QA dataset. The message and response are {context}, {response} in Ubuntu and {question}, {answer} in the Samsung QA dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/105.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Properties of the Ubuntu and Samsung QA dataset. The message and response are {context}, {response} in Ubuntu and {question}, {answer} in the Samsung QA dataset." ], "reasoning": "The Ubuntu Dialogue Corpus dataset has a large sequential length of 162 steps on average, which poses a limitation for RNN gradient calculation.", "description": "we found that it still showed a limitation when we consider very large sequential length data such as 162 steps average in the Ubuntu Dialogue Corpus dataset (see Table 1).. Table 1 shows properties of the Ubuntu dataset.. we generated ({question}, {answer}, flag) triples (see Table 1).. The maximum time step for calculating gradient of the RNN is determined according to the input data statistics in Table 1." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Properties of the Ubuntu and Samsung QA dataset. The message and response are {context}, {response} in Ubuntu and {question}, {answer} in the Samsung QA dataset.'\nProblem: 'What is the average number of tokens per message in the Ubuntu-v1 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/105.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Properties of the Ubuntu and Samsung QA dataset. The message and response are {context}, {response} in Ubuntu and {question}, {answer} in the Samsung QA dataset." ], "reasoning": "To find the average number of tokens per message in the Ubuntu-v1 dataset, look at the 'Message (Avg.) # tokens' column for the Ubuntu-v1 row.", "question": "What is the average number of tokens per message in the Ubuntu-v1 dataset?", "answer": "So, the answer is {\"answer\": \"162.47\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Samsung QA dataset has more training samples than the Ubuntu-v2 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/105.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Properties of the Ubuntu and Samsung QA dataset. The message and response are {context}, {response} in Ubuntu and {question}, {answer} in the Samsung QA dataset." ], "reasoning": "The table shows that the Samsung QA dataset has 163,616 training samples, while the Ubuntu-v2 dataset has 1M training samples. Therefore, the statement is incorrect.", "statement": "The Samsung QA dataset has more training samples than the Ubuntu-v2 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Model performance results for the Ubuntu-v1 dataset. Models [1-4] are from Lowe et al. (2015); Kadlec et al. (2015); Wang and Jiang (2016); Wang et al. (2017), respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/106.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Model performance results for the Ubuntu-v1 dataset. Models [1-4] are from Lowe et al. (2015); Kadlec et al. (2015); Wang and Jiang (2016); Wang et al. (2017), respectively." ], "reasoning": "The HRDE and HRDE-LTC models achieve the best performance for the Ubuntu-v1 dataset, with HRDE-LTC showing the highest scores across all metrics.", "description": "As Table 3 shows, our proposed HRDE and HRDE-LTC models achieve the best performance for the Ubuntu-v1 dataset. We also find that the RDE-LTC model shows improvements from the baseline model, RDE." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Model performance results for the Ubuntu-v1 dataset. Models [1-4] are from Lowe et al. (2015); Kadlec et al. (2015); Wang and Jiang (2016); Wang et al. (2017), respectively.'\nProblem: 'Which model shows the highest performance for the Ubuntu-v1 1 in 10R@5 metric?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/106.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Model performance results for the Ubuntu-v1 dataset. Models [1-4] are from Lowe et al. (2015); Kadlec et al. (2015); Wang and Jiang (2016); Wang et al. (2017), respectively." ], "reasoning": "By examining the table, the HRDE-LTC model has the highest score for the Ubuntu-v1 1 in 10R@5 metric, with a value of 0.960.", "question": "Which model shows the highest performance for the Ubuntu-v1 1 in 10R@5 metric?", "answer": "So, the answer is {\"answer\": \"HRDE-LTC\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe RDE-LTC model outperforms the RDE model in the Ubuntu-v1 1 in 10R@1 metric.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/106.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Model performance results for the Ubuntu-v1 dataset. Models [1-4] are from Lowe et al. (2015); Kadlec et al. (2015); Wang and Jiang (2016); Wang et al. (2017), respectively." ], "reasoning": "The table shows that the RDE-LTC model has a score of 0.794 for the Ubuntu-v1 1 in 10R@1 metric, while the RDE model has a score of 0.784. Therefore, the statement is supported.", "statement": "The RDE-LTC model outperforms the RDE model in the Ubuntu-v1 1 in 10R@1 metric.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Model performance results for the Ubuntu-v2 dataset. Models [1,3-6] are from Lowe et al. (2015); Wang and Jiang (2016); Wang et al. (2017); Baudiš et al. (2016); Tan et al. (2015), respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/107.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Model performance results for the Ubuntu-v2 dataset. Models [1,3-6] are from Lowe et al. (2015); Wang and Jiang (2016); Wang et al. (2017); Baudiš et al. (2016); Tan et al. (2015), respectively." ], "reasoning": "The HRDE-LTC model outperforms other models in three specific cases, showing improvements over the RDE and HRDE models.", "description": "Table 4 reveals that the HRDE-LTC model is best for three cases (1 in 2 R@1, 1 in 10 R@2 and 1 in 10 R@5).. we see improvements from the RDE model to the HRDE model and additional improvements with the LTC module in all test cases (the Ubuntuv1/v2 and the Samsung QA)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Model performance results for the Ubuntu-v2 dataset. Models [1,3-6] are from Lowe et al. (2015); Wang and Jiang (2016); Wang et al. (2017); Baudiš et al. (2016); Tan et al. (2015), respectively.'\nProblem: 'Which model performs best in the Ubuntu v2 1 in 10 R@5 case?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/107.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Model performance results for the Ubuntu-v2 dataset. Models [1,3-6] are from Lowe et al. (2015); Wang and Jiang (2016); Wang et al. (2017); Baudiš et al. (2016); Tan et al. (2015), respectively." ], "reasoning": "By examining the table, the HRDE-LTC model has the highest score of 0.966 in the Ubuntu v2 1 in 10 R@5 case.", "question": "Which model performs best in the Ubuntu v2 1 in 10 R@5 case?", "answer": "So, the answer is {\"answer\": \"HRDE-LTC\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe HRDE-LTC model shows the best performance in all test cases.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/107.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Model performance results for the Ubuntu-v2 dataset. Models [1,3-6] are from Lowe et al. (2015); Wang and Jiang (2016); Wang et al. (2017); Baudiš et al. (2016); Tan et al. (2015), respectively." ], "reasoning": "The table shows that the HRDE-LTC model is the best in three specific cases, but not in all test cases.", "statement": "The HRDE-LTC model shows the best performance in all test cases.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Model performance results for the Samsung QA dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/108.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Model performance results for the Samsung QA dataset." ], "reasoning": "The HRDE-LTC model shows the highest performance improvements compared to the baseline model, TFIDF, and RDE.", "description": "Table 5 indicates the proposed RDE-LTC, HRDE, and the that HRDE-LTC model show performance improvements when compared to the baseline model, TFIDF and RDE." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Model performance results for the Samsung QA dataset.'\nProblem: 'Which model shows the highest performance in Samsung QA 1 in 10R@5?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/108.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Model performance results for the Samsung QA dataset." ], "reasoning": "By comparing the values in the Samsung QA 1 in 10R@5 column, the HRDE-LTC model has the highest value of 0.998.", "question": "Which model shows the highest performance in Samsung QA 1 in 10R@5?", "answer": { "answer": "HRDE-LTC" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe RDE-LTC model outperforms the HRDE model in Samsung QA 1 in 10R@1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/108.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Model performance results for the Samsung QA dataset." ], "reasoning": "In the Samsung QA 1 in 10R@1 column, the RDE-LTC model has a value of 0.880, while the HRDE model has a value of 0.885. Therefore, the statement is refuted.", "statement": "The RDE-LTC model outperforms the HRDE model in Samsung QA 1 in 10R@1.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparison of our model with the baselines. “*” is our QG module.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/109.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of our model with the baselines. “*” is our QG module." ], "reasoning": "The IWAQG model consistently outperforms all other models across all metrics by around 2%.", "description": "Our model outperforms all other models in all the metrics. This improvement is consistent, around 2%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparison of our model with the baselines. “*” is our QG module.'\nProblem: 'Which model has the highest BLEU-1 score and by how much does it outperform the second highest model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/109.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of our model with the baselines. “*” is our QG module." ], "reasoning": "The IWAQG model has the highest BLEU-1 score of 47.69. The second highest BLEU-1 score is 46.58 by Liu et al. (2019). The difference is 47.69 - 46.58 = 1.11.", "question": "Which model has the highest BLEU-1 score and by how much does it outperform the second highest model?", "answer": "So, the answer is {\"answer\": \"1.11\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe IWAQG model has the highest METEOR score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/109.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of our model with the baselines. “*” is our QG module." ], "reasoning": "The table shows that the IWAQG model has a METEOR score of 22.33, which is higher than the METEOR scores of the other models listed.", "statement": "The IWAQG model has the highest METEOR score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Performance of the QG model with respect to the accuracy of the interrogative-word classifier. “*” is our implementation of the QG module without our interrogative-word classifier Zhao et al. (2018).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/110.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of the QG model with respect to the accuracy of the interrogative-word classifier. “*” is our implementation of the QG module without our interrogative-word classifier Zhao et al. (2018)." ], "reasoning": "Table 3 and Figure 3 illustrate that the classifier's accuracy improves with higher IWAQG values, validating the pipelined approach's effectiveness.", "description": "Table 3 and Figure 3 show a linear relationship between the accuracy of the classifier and the IWAQG. This demonstrates the effectiveness of our pipelined approach regardless of the interrogative-word classifier model." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Performance of the QG model with respect to the accuracy of the interrogative-word classifier. “*” is our implementation of the QG module without our interrogative-word classifier Zhao et al. (2018).'\nProblem: 'What is the BLEU-4 score when the accuracy is 80%?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/110.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of the QG model with respect to the accuracy of the interrogative-word classifier. “*” is our implementation of the QG module without our interrogative-word classifier Zhao et al. (2018)." ], "reasoning": "To find the BLEU-4 score at 80% accuracy, locate the row with 80% accuracy and then find the corresponding BLEU-4 score in that row.", "question": "What is the BLEU-4 score when the accuracy is 80%?", "answer": "So, the answer is {\"answer\": \"18.42\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe METEOR score is highest when the accuracy is 100%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/110.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of the QG model with respect to the accuracy of the interrogative-word classifier. “*” is our implementation of the QG module without our interrogative-word classifier Zhao et al. (2018)." ], "reasoning": "To verify this statement, compare the METEOR scores across all accuracy levels. The highest METEOR score is 23.45, which corresponds to the 100% accuracy row.", "statement": "The METEOR score is highest when the accuracy is 100%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Recall of interrogative words of the QG model. “*” is our implementation of the QG module without our interrogative-word classifier Zhao et al. (2018).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/111.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Recall of interrogative words of the QG model. “*” is our implementation of the QG module without our interrogative-word classifier Zhao et al. (2018)." ], "reasoning": "The recall of the proposed IWAQG system is 74.10%, which is an improvement of almost 6% over the QG module's recall of 68.29%.", "description": "we analyze the recall of the interrogative words generated by our pipelined system. As shown in the Table 4, the total recall of using only the QG module is 68.29%, while the recall of our proposed system, IWAQG, is 74.10%, an improvement of almost 6%. Furthermore, if we assume a perfect interrogative-word classifier, the recall would be 99.72%, a dramatic improvement which proves the validity of our hypothesis." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Recall of interrogative words of the QG model. “*” is our implementation of the QG module without our interrogative-word classifier Zhao et al. (2018).'\nProblem: 'What is the recall percentage for the 'Why' interrogative word using the IWAQG system?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/111.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Recall of interrogative words of the QG model. “*” is our implementation of the QG module without our interrogative-word classifier Zhao et al. (2018)." ], "reasoning": "The table shows the recall percentages for different interrogative words using various models. For the 'Why' interrogative word, the recall percentage using the IWAQG system is 50.63%.", "question": "What is the recall percentage for the 'Why' interrogative word using the IWAQG system?", "answer": { "answer": "50.63%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe recall percentage for the 'Which' interrogative word using the Upper Bound model is 99.71%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/111.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Recall of interrogative words of the QG model. “*” is our implementation of the QG module without our interrogative-word classifier Zhao et al. (2018)." ], "reasoning": "The table indicates that the recall percentage for the 'Which' interrogative word using the Upper Bound model is indeed 99.71%.", "statement": "The recall percentage for the 'Which' interrogative word using the Upper Bound model is 99.71%.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Ablation Study of our interrogative-word classifier.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/112.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Ablation Study of our interrogative-word classifier." ], "reasoning": "The combination of answer, answer entity type, and passage features yields the best performance for the interrogative-word classifier.", "description": "We tried to combine different features shown in Table 6 for the interrogative-word classifier.. The first model is only using the [CLS] BERT token embedding. The second model is the previous one with the entity type of the answer as an additional feature. The performance of this model is a bit better than the first one but it is not enough to be utilized effectively for our pipeline.. As we can see, the performance noticeably increased, which indicates that answer information is the key to predict the interrogative word needed.. the fourth model,. clearly outperforms the previous one,. The fifth model is the same as the previous one but with the addition of the entitytype embedding of the answer. The combination of the three features (answer, answer entity type, and passage) yields to the best performance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Ablation Study of our interrogative-word classifier.'\nProblem: 'Which classifier model achieved the highest accuracy?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/112.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Ablation Study of our interrogative-word classifier." ], "reasoning": "By comparing the accuracy values in the table, the model 'CLS + AT + NER' achieved the highest accuracy of 73.8%.", "question": "Which classifier model achieved the highest accuracy?", "answer": { "answer": "CLS + AT + NER" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model using only the CLS BERT token embedding achieved the lowest accuracy.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/112.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Ablation Study of our interrogative-word classifier." ], "reasoning": "The table shows that the 'CLS' model has the lowest accuracy of 56.0%, which is lower than all other models listed.", "statement": "The model using only the CLS BERT token embedding achieved the lowest accuracy.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Recall and precision of interrogative words of our interrogative-word classifier.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/113.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Recall and precision of interrogative words of our interrogative-word classifier." ], "reasoning": "The classifier has very low recall for 'which' and 'when', and also struggles with 'why'.", "description": "In addition, we provide the recall and precision per class for our final interrogative-word classifier (CLS + AT + NER in Table 7).. However, the recall of which is very low.. Our model has also problem with why. Lastly, the recall of 'when is also low" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Recall and precision of interrogative words of our interrogative-word classifier.'\nProblem: 'Which class has the lowest recall and what is its value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/113.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Recall and precision of interrogative words of our interrogative-word classifier." ], "reasoning": "By examining the recall values in the table, the class 'Which' has the lowest recall value of 1.4%.", "question": "Which class has the lowest recall?", "answer": "So, the answer is {\"answer\": \"Which\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe classifier has the highest precision for the class 'How'.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/113.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Recall and precision of interrogative words of our interrogative-word classifier." ], "reasoning": "By examining the precision values in the table, the class 'How' has the highest precision value of 79.0%.", "statement": "The classifier has the highest precision for the class 'How'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Statistics on forests generated with various γ (upper half) and K (lower half) on the development set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/114.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Statistics on forests generated with various γ (upper half) and K (lower half) on the development set." ], "reasoning": "Forests produced by EDGEWISE generally have higher forest density than those from KBESTEISNER, but their connectivity ratio drops with increasing γ.", "description": "Table 1 demonstrates several characteristics of the generated forests of both the EDGEWISE and KBESTEISNER algorithms in Section 5.1, where \"#Edge/#Sent\" measures the forest density with the number of edges divided by the sentence length, \"LAS\" represents the oracle LAS score on 100 biomedical sentences with manually annotated dependency trees, and \"Conn. Ratio (%)\" shows the percentage of forests where both related entity mentions are connected.. Regarding the forest density, forests produced by EDGEWISE generally contain more edges than those from KBESTEISNER.. For connectivity, KBESTEISNER guarantees to generate spanning forests. On the other hand, the connectivity ratio for the forests produced by EDGEWISE drops when increasing the threshold γ. We can have more than 94% being connected with γ ≤ 0.2." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Statistics on forests generated with various γ (upper half) and K (lower half) on the development set.'\nProblem: 'What is the LAS score for EDGEWISE when γ is 0.1, and how does it compare to the LAS score for KBESTEISNER when K is 5?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/114.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Statistics on forests generated with various γ (upper half) and K (lower half) on the development set." ], "reasoning": "The LAS score for EDGEWISE when γ is 0.1 is 91.2. The LAS score for KBESTEISNER when K is 5 is 89.1. Comparing these, the LAS score for EDGEWISE is higher.", "question": "What is the LAS score for EDGEWISE when γ is 0.1, and how does it compare to the LAS score for KBESTEISNER when K is 5?", "answer": { "answer": "91.2" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe connectivity ratio for EDGEWISE is always 100%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/114.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Statistics on forests generated with various γ (upper half) and K (lower half) on the development set." ], "reasoning": "The table shows that the connectivity ratio for EDGEWISE drops to 77.6% when γ is 0.3, indicating that it is not always 100%.", "statement": "The connectivity ratio for EDGEWISE is always 100%.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Test results of Biocreative VI CPR. † indicates previously reported numbers. ** means significant over DepTree at p<0.01 with 1000 bootstrap tests (Efron and Tibshirani, 1994).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/115.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Test results of Biocreative VI CPR. † indicates previously reported numbers. ** means significant over DepTree at p<0.01 with 1000 bootstrap tests (Efron and Tibshirani, 1994)." ], "reasoning": "TEXTONLY performs comparably to Bran, while DEPTREE outperforms the previous state-of-the-art. KBESTEISNERPS and EDGEWISEPS achieve significantly higher scores than DEPTREE.", "description": "Table 2 shows the main comparison results on the BioCreative CPR testset, with comparisons to the previous state-of-the-art and our baselines.. TEXTONLY gives a performance comparable with Bran. With 1-best dependency trees, our DEPTREE baseline gives better performances than the previous state of the art.. both KBESTEISNERPS and EDGEWISEPS obtain significantly higher numbers than DEPTREE." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Test results of Biocreative VI CPR. † indicates previously reported numbers. ** means significant over DepTree at p<0.01 with 1000 bootstrap tests (Efron and Tibshirani, 1994).'\nProblem: 'Which model achieved the highest F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/115.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Test results of Biocreative VI CPR. † indicates previously reported numbers. ** means significant over DepTree at p<0.01 with 1000 bootstrap tests (Efron and Tibshirani, 1994)." ], "reasoning": "By examining the F1 scores in the table, we see that EDGEWISEPS has the highest score of 53.4.", "question": "Which model achieved the highest F1 score?", "answer": { "answer": "EDGEWISEPS" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDepTree outperforms TextOnly.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/115.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Test results of Biocreative VI CPR. † indicates previously reported numbers. ** means significant over DepTree at p<0.01 with 1000 bootstrap tests (Efron and Tibshirani, 1994)." ], "reasoning": "The table shows that DepTree has an F1 score of 51.4, while TextOnly has an F1 score of 50.6. Therefore, DepTree outperforms TextOnly.", "statement": "DepTree outperforms TextOnly.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Main results on PGR testest. † denotes previous numbers rounded into 3 significant digits. * and ** indicate significance over DepTree at p<0.05 and p<0.01 with 1000 bootstrap tests.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/116.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Main results on PGR testest. † denotes previous numbers rounded into 3 significant digits. * and ** indicate significance over DepTree at p<0.05 and p<0.01 with 1000 bootstrap tests." ], "reasoning": "Our models, KBESTEISNERPS and EDGEWISEPS, significantly outperform DEPTREE on the PGR testset, with EDGEWISEPS achieving the highest F1 score.", "description": "Table 3 shows the comparison with previous work on the PGR testset, where our models are significantly better than the existing models.. With 1-best trees, DEPTREE is 2.9 points better than TEXTONLY,. both KBESTEISNERPS and EDGEWISEPS significantly outperform DEPTR" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Main results on PGR testest. † denotes previous numbers rounded into 3 significant digits. * and ** indicate significance over DepTree at p<0.05 and p<0.01 with 1000 bootstrap tests.'\nProblem: 'Which model has the highest F1 score and how much better is it compared to DEPTREE?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/116.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Main results on PGR testest. † denotes previous numbers rounded into 3 significant digits. * and ** indicate significance over DepTree at p<0.05 and p<0.01 with 1000 bootstrap tests." ], "reasoning": "The table shows that EDGEWISEPS has the highest F1 score of 85.7. DEPTREE has an F1 score of 78.9. The difference between EDGEWISEPS and DEPTREE is 85.7 - 78.9 = 6.8 points.", "question": "Which model has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"EDGEWISEPS\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTEXTONLY has a higher F1 score than BIOBERT.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/116.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Main results on PGR testest. † denotes previous numbers rounded into 3 significant digits. * and ** indicate significance over DepTree at p<0.05 and p<0.01 with 1000 bootstrap tests." ], "reasoning": "The table shows that TEXTONLY has an F1 score of 76.0, while BIOBERT has an F1 score of 67.2. Therefore, TEXTONLY has a higher F1 score than BIOBERT.", "statement": "TEXTONLY has a higher F1 score than BIOBERT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Main results on SemEval-2010 task 8 testest. † denotes previous numbers.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/117.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Main results on SemEval-2010 task 8 testest. † denotes previous numbers." ], "reasoning": "EDGEWISEPS outperforms all other models, while DEPTREE performs similarly to CGCN but worse than C-AGGCN.", "description": "As shown in Table 4, we conduct a preliminary study on SemEval-2010 task 8. DEPTREE achieves similar performance as CGCN and is slightly worse than C-AGGCN,. both KBESTEISNERPS and EDGEWISEPS outperform DEPTREE. they show comparable and slightly better performances than C-AGGCN.. EDGEWISEPS is better than KBESTEISNERPS," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Main results on SemEval-2010 task 8 testest. † denotes previous numbers.'\nProblem: 'Which model has the highest F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/117.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Main results on SemEval-2010 task 8 testest. † denotes previous numbers." ], "reasoning": "By comparing the F1 scores of all models listed in the table, EDGEWISEPS has the highest F1 score of 86.3.", "question": "Which model has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"EDGEWISEPS\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDepTree has a higher F1 score than C-GCN.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/117.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Main results on SemEval-2010 task 8 testest. † denotes previous numbers." ], "reasoning": "The table shows that DepTree has an F1 score of 84.6, while C-GCN has an F1 score of 84.8. Therefore, DepTree does not have a higher F1 score than C-GCN.", "statement": "DepTree has a higher F1 score than C-GCN.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Accuracy (%) of Hits@K on relations with training instances fewer than 100/200.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/118.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy (%) of Hits@K on relations with training instances fewer than 100/200." ], "reasoning": "The KATT model outperforms both the plain attention model and the HATT model for both CNN and PCNN architectures.", "description": "From the results shown in Table 1, we observe that for both CNN and PCNN models, our model outperforms the plain attention model and the HATT model." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Accuracy (%) of Hits@K on relations with training instances fewer than 100/200.'\nProblem: 'Which model achieves the highest Hits@K score for PCNN with less than 200 training instances at K=20?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/118.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy (%) of Hits@K on relations with training instances fewer than 100/200." ], "reasoning": "For PCNN with less than 200 training instances at K=20, the KATT model achieves the highest score of 69.2.", "question": "Which model achieves the highest Hits@K score for PCNN with less than 200 training instances at K=20?", "answer": "So, the answer is {\"answer\": \"KATT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe HATT model achieves the highest Hits@K score for CNN with less than 100 training instances at K=15.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/118.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy (%) of Hits@K on relations with training instances fewer than 100/200." ], "reasoning": "For CNN with less than 100 training instances at K=15, the KATT model achieves the highest score of 41.3, not the HATT model.", "statement": "The HATT model achieves the highest Hits@K score for CNN with less than 100 training instances at K=15.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results of ablation study with PCNN.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/119.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of ablation study with PCNN." ], "reasoning": "Performance slightly degraded without coarse-to-fine attention, KG, or word embeddings, and significantly degraded without GCNs.", "description": "From the evaluation results in Table 2, we observe that the performance slightly degraded without coarse-tofine attention,. We also noticed that the performance slightly degraded without KG or using word embeddings, and the performance significantly degraded when we removed GCNs." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results of ablation study with PCNN.'\nProblem: 'What was the performance impact on Hits@K (Macro) when GCNs were removed?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/119.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of ablation study with PCNN." ], "reasoning": "The table shows that removing GCNs resulted in a significant performance degradation across all Hits@K metrics compared to the baseline (+KATT). For example, Hits@K <100 10 dropped from 35.3 to 30.5.", "question": "Which feature has the highest average values?", "answer": "So, the answer is {\"answer\": \"KATT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe performance slightly degraded without using word embeddings.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/119.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of ablation study with PCNN." ], "reasoning": "The table shows that the performance metrics for Word2vec are slightly lower than the baseline (+KATT), supporting the statement.", "statement": "The performance slightly degraded without using word embeddings.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Spearman correlations with WordNet similarities (left) and human judgments (right) ×100.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/120.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Spearman correlations with WordNet similarities (left) and human judgments (right) ×100." ], "reasoning": "Path2vec outperforms other graph embeddings in both WordNet similarity scores and correlations with human judgments, demonstrating its efficiency in approximating different graph measures.", "description": "Discussion of Results The left part of Table 1 shows results with the WordNet similarity scores used as gold standard. Path2vec outperforms other graph embeddings, achieving high correlations with WordNet similarities. This shows that our model efficiently approximates different graph measures. The right part of Table 1 shows results. for the correlations with human judgments (SimLex999). We report the results for the best models for each method, all of them (except FSE) using vector size 300 for comparability." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Spearman correlations with WordNet similarities (left) and human judgments (right) ×100.'\nProblem: 'Which model achieves the highest correlation with human judgments (SimLex999) in the ShP measure?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/120.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Spearman correlations with WordNet similarities (left) and human judgments (right) ×100." ], "reasoning": "To find the model with the highest correlation with human judgments in the ShP measure, look at the right part of the table under the ShP column. The highest value is 55.6, achieved by FSE.", "question": "Which model achieves the highest correlation with human judgments (SimLex999) in the ShP measure?", "answer": "So, the answer is {\"answer\": \"FSE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPath2vec achieves the highest LCH score in WordNet similarity.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/120.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Spearman correlations with WordNet similarities (left) and human judgments (right) ×100." ], "reasoning": "To verify this statement, look at the left part of the table under the LCH column. Path2vec has a score of 93.5, which is higher than the scores of other models.", "statement": "Path2vec achieves the highest LCH score in WordNet similarity.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: F1 scores of a graph-based WSD algorithm on WordNet versus its vectorized counterparts.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/121.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F1 scores of a graph-based WSD algorithm on WordNet versus its vectorized counterparts." ], "reasoning": "The raw WordNet similarities generally outperform their vector approximations, with path2vec models closely following and outperforming other graph embedding baselines.", "description": "Discussion of Results Table 2 presents the WSD micro-F1 scores using raw WordNet similarities, 300D path2vec, DeepWalk and node2vec models, and the 128D FSE model. We evaluate on the following all-words English WSD test sets:. Senseval-2 (Palmer et al., 2001), Senseval-3 (Mihalcea et al., 2004), and SemEval-15 Task 13 (Moro and Navigli, 2015). The raw WordNet similarities have a small edge over their vector approximations in the majority of the cases yet the path2vec models consistently closely follow them while outperforming other graph embedding baselines: We indicate the differences with respect to the original with a subscript number." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: F1 scores of a graph-based WSD algorithm on WordNet versus its vectorized counterparts.'\nProblem: 'Which model has the highest micro-F1 score on the SemEval-15 test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/121.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F1 scores of a graph-based WSD algorithm on WordNet versus its vectorized counterparts." ], "reasoning": "To find the highest micro-F1 score on the SemEval-15 test set, look at the SemEval-15 column and identify the highest value. The highest score is 0.563 by ShP (path2vec).", "question": "Which model has the highest micro-F1 score on the SemEval-15 test set?", "answer": { "answer": "ShP (path2vec)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe DeepWalk model has a higher micro-F1 score than the node2vec model on the Senseval2 test set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/121.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F1 scores of a graph-based WSD algorithm on WordNet versus its vectorized counterparts." ], "reasoning": "Compare the micro-F1 scores of DeepWalk and node2vec on the Senseval2 test set. DeepWalk has a score of 0.528, while node2vec has a score of 0.503. Therefore, DeepWalk has a higher score.", "statement": "The DeepWalk model has a higher micro-F1 score than the node2vec model on the Senseval2 test set.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Similarity results on the RareWord set, measured as Spearman’s ρ×100. VarEmbed was trained on a 20-million token dataset, Polyglot on a 1.7B-token dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/122.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Similarity results on the RareWord set, measured as Spearman’s ρ×100. VarEmbed was trained on a 20-million token dataset, Polyglot on a 1.7B-token dataset." ], "reasoning": "MIMICK RNN recovers about half of the performance loss due to out-of-vocabulary words and outperforms VarEmbed, while FastText has the highest performance with a much larger vocabulary.", "description": "The results, shown in Table 3, demonstrate that the MIMICK RNN recovers about half of the loss in performance incurred by the original Polyglot training model due to out-of-vocabulary words in the \"All pairs\" condition. MIMICK also outperforms VarEmbed. FastText can be considered an upper bound: with a vocabulary that is 25 times larger than the other models, it was missing words from only 44 pairs on this data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Similarity results on the RareWord set, measured as Spearman’s ρ×100. VarEmbed was trained on a 20-million token dataset, Polyglot on a 1.7B-token dataset.'\nProblem: 'Which model has the highest performance in the 'All pairs' condition?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/122.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Similarity results on the RareWord set, measured as Spearman’s ρ×100. VarEmbed was trained on a 20-million token dataset, Polyglot on a 1.7B-token dataset." ], "reasoning": "By comparing the 'All pairs' column, FastText has the highest value of 47.3, indicating the highest performance.", "question": "Which model has the highest performance in the 'All pairs' condition?", "answer": "So, the answer is {\"answer\": \"FastText\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMIMICK RNN has a higher performance than VarEmbed in the 'All pairs' condition.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/122.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Similarity results on the RareWord set, measured as Spearman’s ρ×100. VarEmbed was trained on a 20-million token dataset, Polyglot on a 1.7B-token dataset." ], "reasoning": "The 'All pairs' column shows that MIMICK RNN has a value of 17.5, which is lower than VarEmbed's value of 25.5.", "statement": "MIMICK RNN has a higher performance than VarEmbed in the 'All pairs' condition.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results on argument generation by BLEU and METEOR (MTR), with system retrieved evidence and oracle retrieval. The best performing model is highlighted in bold per metric. Our separate decoder models, with and without keyphrase attention, statistically significantly outperform all seq2seq-based models based on approximation randomization testing Noreen (1989), p<0.0001.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/123.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on argument generation by BLEU and METEOR (MTR), with system retrieved evidence and oracle retrieval. The best performing model is highlighted in bold per metric. Our separate decoder models, with and without keyphrase attention, statistically significantly outperform all seq2seq-based models based on approximation randomization testing Noreen (1989), p<0.0001." ], "reasoning": "Our models, especially with separate decoders, achieve significantly higher BLEU and METEOR scores than seq2seq-based models, and attention over both input and generated keyphrases further boosts performance.", "description": "As can be seen from Table 3, our models produce better BLEU scores than almost all the comparisons. Especially, our models with separate decoder yield significantly higher BLEU and METEOR scores than all seq2seq-based models (approximation randomization testing, p < 0.0001) do. Better METEOR scores are achieved by the RETRIEVAL baseline, mainly due to its significantly longer arguments.. Moreover, utilizing attention over both input and the generated keyphrases further boosts our models' performance. Interestingly, utilizing system retrieved evidence yields better BLEU scores than using oracle retrieval for testing." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Results on argument generation by BLEU and METEOR (MTR), with system retrieved evidence and oracle retrieval. The best performing model is highlighted in bold per metric. Our separate decoder models, with and without keyphrase attention, statistically significantly outperform all seq2seq-based models based on approximation randomization testing Noreen (1989), p<0.0001.'\nProblem: 'Which model configuration achieves the highest BLEU score with system retrieval?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/123.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on argument generation by BLEU and METEOR (MTR), with system retrieved evidence and oracle retrieval. The best performing model is highlighted in bold per metric. Our separate decoder models, with and without keyphrase attention, statistically significantly outperform all seq2seq-based models based on approximation randomization testing Noreen (1989), p<0.0001." ], "reasoning": "To find the highest BLEU score with system retrieval, look at the 'w/ System Retrieval BLEU' column. The highest value is 24.71, achieved by the '+ attend KP' model configuration.", "question": "Which model configuration achieves the highest BLEU score with system retrieval?", "answer": "So, the answer is {\"answer\": \"+ attend KP\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe RETRIEVAL baseline achieves the highest METEOR score with system retrieval.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/123.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on argument generation by BLEU and METEOR (MTR), with system retrieved evidence and oracle retrieval. The best performing model is highlighted in bold per metric. Our separate decoder models, with and without keyphrase attention, statistically significantly outperform all seq2seq-based models based on approximation randomization testing Noreen (1989), p<0.0001." ], "reasoning": "The METEOR score for the RETRIEVAL baseline with system retrieval is 12.19, which is lower than the METEOR scores achieved by our models, such as 11.27 by '+ attend KP'.", "statement": "The RETRIEVAL baseline achieves the highest METEOR score with system retrieval.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: POS tagging accuracy (UD 1.4 Test). Bold (Italic) indicates significant improvement (degradation) by McNemar’s test, p'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/124.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: POS tagging accuracy (UD 1.4 Test). Bold (Italic) indicates significant improvement (degradation) by McNemar’s test, p" ], "reasoning": "The table shows part-of-speech tagging accuracy for various languages, with notable improvements in Slavic languages and Chinese due to their linguistic characteristics and the use of rich word representations.", "description": "We report the results on the full sets and on. = 5000 tokens in Table 5 (partof-speech tagging accuracy). For POS, the largest margins are in the Slavic languages (Russian, Czech, Bulgarian), where word order is relatively free and thus rich word representations are imperative. Chinese also exhibits impressive improvement across all settings, perhaps due to the large character inventory (> 12,000), for which a model such as MIMICK can learn well-informed embeddings using the large Polyglot vocabulary dataset, overcoming both word- and characterlevel sparsity in the UD corpus." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: POS tagging accuracy (UD 1.4 Test). Bold (Italic) indicates significant improvement (degradation) by McNemar’s test, p'\nProblem: 'Which language shows the highest part-of-speech tagging accuracy using the Full data mimick method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/124.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: POS tagging accuracy (UD 1.4 Test). Bold (Italic) indicates significant improvement (degradation) by McNemar’s test, p" ], "reasoning": "To find the highest part-of-speech tagging accuracy using the Full data mimick method, look at the 'Full data mimick' column and identify the highest value.", "question": "Which language shows the highest part-of-speech tagging accuracy using the Full data mimick method?", "answer": "So, the answer is {\"answer\": \"el\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe part-of-speech tagging accuracy for Russian using the Full data char method is higher than 95%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/124.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: POS tagging accuracy (UD 1.4 Test). Bold (Italic) indicates significant improvement (degradation) by McNemar’s test, p" ], "reasoning": "Check the 'Full data char' column for the value corresponding to Russian (ru). The value is 95.21%, which is higher than 95%.", "statement": "The part-of-speech tagging accuracy for Russian using the Full data char method is higher than 95%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Micro-F1 for morphosyntactic attributes (UD 1.4 Test). Bold (Italic) type indicates significant improvement (degradation) by a bootstrapped Z-test, p'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/125.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Micro-F1 for morphosyntactic attributes (UD 1.4 Test). Bold (Italic) type indicates significant improvement (degradation) by a bootstrapped Z-test, p" ], "reasoning": "Morphosyntactic tagging shows gains for Slavic languages, Chinese, and agglutinative languages like Tamil and Turkish due to stable morpheme representation aiding subword modeling.", "description": "Table 6 (morphosyntactic attribute tagging micro-F1).. In morphosyntactic tagging, gains are apparent for Slavic languages and Chinese, but also for agglutinative languages — especially Tamil and Turkish — where the stable morpheme representation makes it easy for subword modeling to provide a type-level signal." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Micro-F1 for morphosyntactic attributes (UD 1.4 Test). Bold (Italic) type indicates significant improvement (degradation) by a bootstrapped Z-test, p'\nProblem: 'Which language has the highest micro-F1 score in the 'Full data Both' column?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/125.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Micro-F1 for morphosyntactic attributes (UD 1.4 Test). Bold (Italic) type indicates significant improvement (degradation) by a bootstrapped Z-test, p" ], "reasoning": "To find the highest micro-F1 score in the 'Full data Both' column, we need to look at the values in that column and identify the maximum value.", "question": "Which language has the highest micro-F1 score in the 'Full data Both' column?", "answer": "So, the answer is {\"answer\": \"es\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe micro-F1 score for Tamil in the 'Ntrain=5000 Both' column is higher than in the 'Full data No-Char' column.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/125.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Micro-F1 for morphosyntactic attributes (UD 1.4 Test). Bold (Italic) type indicates significant improvement (degradation) by a bootstrapped Z-test, p" ], "reasoning": "The table shows that the micro-F1 score for Tamil in the 'Ntrain=5000 Both' column is 85.63, while in the 'Full data No-Char' column it is 79.90. Therefore, the statement is supported.", "statement": "The micro-F1 score for Tamil in the 'Ntrain=5000 Both' column is higher than in the 'Full data No-Char' column.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Absolute gain in POS tagging accuracy from using mimick for 10,000-token datasets (all tokens for Tamil and Kazakh). Bold denotes statistical significance (McNemar’s test,p<0.01).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/126.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Absolute gain in POS tagging accuracy from using mimick for 10,000-token datasets (all tokens for Tamil and Kazakh). Bold denotes statistical significance (McNemar’s test,p<0.01)." ], "reasoning": "MIMICK achieves statistically significant POS tagging improvements over Polyglot models in most languages, especially for test-set tokens outside the UD training set.", "description": "Table 7 presents the POS tagging improvements that MIMICK achieves over the pre-trained Polyglot models, with and without CHAR→TAG concatenation, with 10,000 tokens of training data. We obtain statistically significant improvements in most languages, even when CHAR→TAG is included. These improvements are particularly substantial for test-set tokens outside the UD training set, as shown in the right two columns. While test set OOVs are a strength of the CHAR→TAG model (Plank et al., 2016), in many languages there are still considerable improvements to be obtained from the application of MIMICK initialization." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Absolute gain in POS tagging accuracy from using mimick for 10,000-token datasets (all tokens for Tamil and Kazakh). Bold denotes statistical significance (McNemar’s test,p<0.01).'\nProblem: 'Which language shows the highest improvement in OOV (UD) with CHAR→TAG concatenation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/126.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Absolute gain in POS tagging accuracy from using mimick for 10,000-token datasets (all tokens for Tamil and Kazakh). Bold denotes statistical significance (McNemar’s test,p<0.01)." ], "reasoning": "To find the language with the highest improvement in OOV (UD) with CHAR→TAG concatenation, look at the 'OOV (UD) with' column and identify the highest value.", "question": "Which language shows the highest improvement in OOV (UD) with CHAR→TAG concatenation?", "answer": "So, the answer is {\"answer\": \"Kazakh\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMIMICK achieves the highest improvement in full vocabulary with CHAR→TAG concatenation for the Czech language.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/126.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Absolute gain in POS tagging accuracy from using mimick for 10,000-token datasets (all tokens for Tamil and Kazakh). Bold denotes statistical significance (McNemar’s test,p<0.01)." ], "reasoning": "To verify this statement, check the 'Full vocabulary with' column for the highest value and see if it corresponds to the Czech language.", "statement": "MIMICK achieves the highest improvement in full vocabulary with CHAR→TAG concatenation for the Czech language.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: pretrained vs non-pretrained comparison'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/300.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: pretrained vs non-pretrained comparison" ], "reasoning": "Pre-trained character embeddings consistently improve accuracy across different datasets and author counts.", "description": "Various subsets of authors were chosen and the dataset was truncated to each author having the same number of samples.. with and without pre-training character level embedding and comparing the proposed architectures on the held-out dataset.. To illustrate the need of pre-trained character embeddings, we see from III that using a pre-trained embedding increases the accuracy across datasets and the different number of authors, regardless of the amount of data for each author.. increase the performance a few degrees.. we analyzed the importance of pretrained character embedding for author attribution and showed that pre-training can result in better performances." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE III: pretrained vs non-pretrained comparison'\nProblem: 'What is the accuracy difference between pre-trained and not pre-trained embeddings for 6 authors with 1100 samples per class?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/300.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: pretrained vs non-pretrained comparison" ], "reasoning": "For 6 authors with 1100 samples per class, the accuracy with pre-trained embeddings is 96, while without pre-trained embeddings it is 95. The difference is 96 - 95 = 1.", "question": "What is the accuracy difference between pre-trained and not pre-trained embeddings for 6 authors with 1100 samples per class?", "answer": { "answer": "1" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPre-trained embeddings always result in higher accuracy compared to not pre-trained embeddings.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/300.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: pretrained vs non-pretrained comparison" ], "reasoning": "The table shows that for all given author counts and sample sizes, the accuracy with pre-trained embeddings is higher than without pre-trained embeddings.", "statement": "Pre-trained embeddings always result in higher accuracy compared to not pre-trained embeddings.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Accuracy for cross-lingual Wikipedia title linking, with the transfer HRL shown in parentheses. The best accuracy among input representations with graphemes, phonemes or articulatory features for Encode and PBEL is presented here. Complete results for each representation are in the supplementary material.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/301.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy for cross-lingual Wikipedia title linking, with the transfer HRL shown in parentheses. The best accuracy among input representations with graphemes, phonemes or articulatory features for Encode and PBEL is presented here. Complete results for each representation are in the supplementary material." ], "reasoning": "PBEL method outperforms other baselines in entity linking accuracy across all nine test languages, with significant gains, especially when using multiple pivot HRLs.", "description": "The entity linking accuracy on the Wikipedia test set are summarized in Table 1. On average, our proposed PBEL method performs significantly better than the baselines, with significant accuracy gains in all nine test languages.. The EXACT baseline, which is most often used for monolingual EL (Sil et al. 2017), performs reasonably only when the test language is in the same script as English (i.e., Javanese).. Similarly TRANS, the current state-of-the-art retrieval method in cross-lingual EL (Pan et al. 2017), fails when zero data is available in the test language, unless the HRL is very closely-related to the LRL (as with jv, mr and am). On the other hand, ENCODE presents relatively strong zero-shot transfer results.. PBEL offers stronger performance than ENCODE. As seen in the BEST-53 results, the HRL that performs best is closely-related to the respective test LRL. We also observe that using multiple pivot HRLs leads to better average accuracy, with considerable improvement for some languages.. In most cases, the MANUAL HRL is also the best performing in BEST-53. However, we see that the Dravidian Telugu (te) seems to obtain higher accuracy with Indo-Aryan HRLs – Punjabi (pa) or Hindi (hi).. We also see that the Ukrainian (uk) test set has better performance with another Cyrillic script language, Kazakh (kk), rather than Russian (ru)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Accuracy for cross-lingual Wikipedia title linking, with the transfer HRL shown in parentheses. The best accuracy among input representations with graphemes, phonemes or articulatory features for Encode and PBEL is presented here. Complete results for each representation are in the supplementary material.'\nProblem: 'Which model has the highest average accuracy across all test languages?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/301.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy for cross-lingual Wikipedia title linking, with the transfer HRL shown in parentheses. The best accuracy among input representations with graphemes, phonemes or articulatory features for Encode and PBEL is presented here. Complete results for each representation are in the supplementary material." ], "reasoning": "By comparing the average accuracy values in the 'Avg.' column for each model, we see that the PBEL model with multiple pivot HRLs has the highest average accuracy of 0.54.", "question": "Which model in Pbel has the highest average accuracy across all test languages?", "answer": "So, the answer is {\"answer\": \"Multi\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe TRANS model performs best when the test language is in the same script as English.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/301.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy for cross-lingual Wikipedia title linking, with the transfer HRL shown in parentheses. The best accuracy among input representations with graphemes, phonemes or articulatory features for Encode and PBEL is presented here. Complete results for each representation are in the supplementary material." ], "reasoning": "The description states that the EXACT baseline performs reasonably only when the test language is in the same script as English, not the TRANS model.", "statement": "The TRANS model performs best when the test language is in the same script as English.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Entity linking accuracy on non-Wikipedia data'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/302.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Entity linking accuracy on non-Wikipedia data" ], "reasoning": "PBEL achieves the highest accuracy among all methods on the LORELEI dataset, with a notable improvement in Tigrinya compared to Oromo.", "description": "Entity linking accuracies on the LORELEI dataset are shown in Table 2. PBEL has considerably higher accuracy than the other methods. However, we see relatively lower improvement in accuracy with Somali-Oromo than Amharic-Tigrinya.. the supervised TRANS model, which uses Wikipedia parallel data in the LRL itself as a lexicon, does not perform better than the zero-shot PBEL.. The PBEL model results for our test set with each input representation are presented in Table 3." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Entity linking accuracy on non-Wikipedia data'\nProblem: 'Which method has the highest accuracy for Tigrinya and what is the value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/302.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Entity linking accuracy on non-Wikipedia data" ], "reasoning": "By examining the table, we see that PBEL has the highest accuracy for Tigrinya with a value of 0.33.", "question": "Which method has the highest accuracy for Tigrinya?", "answer": "So, the answer is {\"answer\": \"PBEL\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe supervised TRANS model performs better than the zero-shot PBEL model for Oromo.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/302.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Entity linking accuracy on non-Wikipedia data" ], "reasoning": "The table shows that the supervised TRANS model has an accuracy of 0.05 for Oromo, while the PBEL model has an accuracy of 0.11, indicating that the PBEL model performs better.", "statement": "The supervised TRANS model performs better than the zero-shot PBEL model for Oromo.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Entity linking accuracy with PBEL, using Graphemes, Phonemes or Articulatory features as input. The HRL used for training and pivoting is shown in parentheses in the first row. The pairs with the different scripts are marked with a “*”.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/303.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Entity linking accuracy with PBEL, using Graphemes, Phonemes or Articulatory features as input. The HRL used for training and pivoting is shown in parentheses in the first row. The pairs with the different scripts are marked with a “*”." ], "reasoning": "Phonological representations, such as phonemes and articulatory features, enable effective mapping between languages with different orthographies, leading to improved accuracy over graphemes for HRL-LRL pairs in different scripts.", "description": "see We that using phonological representations (phonemes and articulatory features) offers the ability to map between languages that use different orthographies, explaining the convincing improvement over graphemes for HRL-LRL pairs that are written in different scripts (Table 3). With graphemes, the experiments on these languages achieve ≈ 0 accuracy because the character vocabulary of the HRL encoder simply does not contain the low-resource test language characters. This is the case with Lao-Thai (lo-th), Telugu-Tamil (te-ta) and Bengali-Hindi (bn-hi). In contrast, we observe that the grapheme representation offers strong transfer performance when the LRL and HRL share orthography, notably Javanese-Indonesian (jv-id), Marathi-Hindi (mr-hi) and Ukrainian-Russian (uk-ru)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Entity linking accuracy with PBEL, using Graphemes, Phonemes or Articulatory features as input. The HRL used for training and pivoting is shown in parentheses in the first row. The pairs with the different scripts are marked with a “*”.'\nProblem: 'Which representation achieves the highest accuracy for the Javanese-Indonesian (jv-id) language pair?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/303.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Entity linking accuracy with PBEL, using Graphemes, Phonemes or Articulatory features as input. The HRL used for training and pivoting is shown in parentheses in the first row. The pairs with the different scripts are marked with a “*”." ], "reasoning": "By examining the table, we see that the highest accuracy for the Javanese-Indonesian (jv-id) language pair is achieved using the Phoneme representation with an accuracy of 0.84.", "question": "Which representation achieves the highest accuracy for the Javanese-Indonesian (jv-id) language pair?", "answer": "So, the answer is {\"answer\": \"Phoneme\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe grapheme representation achieves 0 accuracy for the Lao-Thai (lo-th) language pair.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/303.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Entity linking accuracy with PBEL, using Graphemes, Phonemes or Articulatory features as input. The HRL used for training and pivoting is shown in parentheses in the first row. The pairs with the different scripts are marked with a “*”." ], "reasoning": "The table shows that the grapheme representation achieves an accuracy of 0.02 for the Lao-Thai (lo-th) language pair, not 0.", "statement": "The grapheme representation achieves 0 accuracy for the Lao-Thai (lo-th) language pair.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Proportions of correctly answered questions.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/304.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Proportions of correctly answered questions." ], "reasoning": "Most methods performed similarly to random guessing, indicating unique challenges in SQA on this dataset.", "description": "The results are summarized in Table. Almost all the methods performed similar to random guess, showing that SQA on our dataset has its unique challenges." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Proportions of correctly answered questions.'\nProblem: 'Which method had the highest score for Textbooks and what was the score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/304.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Proportions of correctly answered questions." ], "reasoning": "To find the highest score for Textbooks, compare all the values in the Textbooks column. The highest score is 26.22, achieved by PMI.", "question": "Which method had the highest score for Textbooks?", "answer": "So, the answer is {\"answer\": \"PMI\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBERT NLI w/o scenario performed better on Textbooks than on Wikipedia.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/304.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Proportions of correctly answered questions." ], "reasoning": "Compare the scores of BERT NLI w/o scenario for Textbooks (24.29) and Wikipedia (24.17). The score for Textbooks is higher.", "statement": "BERT NLI w/o scenario performed better on Textbooks than on Wikipedia.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Experiment 1: Caption retrieval on the coco dataset. We compare the different reminders of the different methods first in English and then by adding new languages. We also evaluate variations of dsve method with different word embedding.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/305.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Experiment 1: Caption retrieval on the coco dataset. We compare the different reminders of the different methods first in English and then by adding new languages. We also evaluate variations of dsve method with different word embedding." ], "reasoning": "Training with BIVEC embeddings on both English and French captions improves recall performance on the COCO dataset, while using MUSE embeddings decreases performance.", "description": "The table 1 shows the caption retrieval recall on COCO dataset. The first two lines show the state-of-the-art results. The second pair of lines present the results of our model, with W2V and FastText embeddings used as the baseline.. We can see that our model is close to the Deep SemanticVisual Embedding (DSVE) method while the W2V method is slightly worst, as the representation power of the word embedding is reduced.. The BIVEC English-French method is used in English and on both languages simultaneously. If trained only on English, i.e. only on the COCO dataset like the two previous methods, it shows performance similar to the one of the state-of-the-art. This means training using BIVEC does not weaken the English representation. When trained on English and French together, the recall is increased by 3.35 %, going from 65.58 % to 67.78 %. We can also see an improvement for recall@5 and recall@10, with respectively 1.17 % and 0.85 % of increase. This implies that the similarity learning with French captions increases English recognition when using BIVEC.. First of all, when training with MUSE for English only, we can see a sharp decrease in performance, with a recall going from 66.08 % to 63.10 %. By comparing the model trained with W2V, we obtain similar results. This could come from the fact that both MUSE and W2V embeddings do not have representation for out of vocabulary words like the FastText ones. Moreover, rare words have much more chance to be wrongly projected because of space transformation. When we train the model with additional languages, we can see a slight decrease in performance in English. The maximum decrease is 1.01 % for recall@10, but it is counterbalanced by an increase of 0.29 % for the recall@1.. We evaluated our method on the COCO dataset for English-only results and shown that using BIVEC embeddings enables the use of another language in order to improve the performance. The obtained improvement is a 3.35 % increase in performance on the COCO dataset, and a 15.15 % increase on the Multi30K dataset." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Experiment 1: Caption retrieval on the coco dataset. We compare the different reminders of the different methods first in English and then by adding new languages. We also evaluate variations of dsve method with different word embedding.'\nProblem: 'What is the recall@1 performance when using BIVEC embeddings for English and French together?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/305.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Experiment 1: Caption retrieval on the coco dataset. We compare the different reminders of the different methods first in English and then by adding new languages. We also evaluate variations of dsve method with different word embedding." ], "reasoning": "The table shows that the recall@1 performance for BIVEC embeddings when trained on English and French together is 67.78%.", "question": "What is the recall@1 performance when using BIVEC embeddings for English and French together?", "answer": { "answer": "67.78%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUsing MUSE embeddings for English only results in a recall@1 performance of 63.1%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/305.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Experiment 1: Caption retrieval on the coco dataset. We compare the different reminders of the different methods first in English and then by adding new languages. We also evaluate variations of dsve method with different word embedding." ], "reasoning": "The table indicates that the recall@1 performance for MUSE embeddings when trained on English only is indeed 63.1%.", "statement": "Using MUSE embeddings for English only results in a recall@1 performance of 63.1%.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Experiment 2: Image retrieval on the coco dataset. The methods are the same as in table 1.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/306.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experiment 2: Image retrieval on the coco dataset. The methods are the same as in table 1." ], "reasoning": "Training with multiple languages improves recall@1, but adding more than two languages can decrease performance.", "description": "Given a sentence, in any language, we evaluate the rank of the corresponding image. The evaluation is again made by batches of 1000. The results are presented in table 2.. The first two lines of the table present the state-of-the-art results, with W2V and FastText embeddings. We can see similar results as in the previous experiment. With BIVEC, we have results close to the FastText embeddings when training only in English. This time, the recall is better with an increase of 2.68 % for recall@1. When trained with English and French, the recall@1 is increased by 3.65 %. This implies, again, that we can improve performance by learning on an additional language.. Our model is able to use the multi-language representing the effectiveness of MUSE embeddings. We train the model with English, and different combinations of French, German and Czech. On English only, we have similar results to the W2V approach. When adding new languages, we can see a decrease in performance for English. We obtain a maximum decrease of 2.62 % for recall@1 when the models saw English, French, German and Czech.. For image retrieval from a caption in 4 languages, we obtain a 49.38 % recall@10 on the Multi30K dataset." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Experiment 2: Image retrieval on the coco dataset. The methods are the same as in table 1.'\nProblem: 'What is the recall@1 for the model trained with English, French, German, and Czech using MUSE embeddings?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/306.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experiment 2: Image retrieval on the coco dataset. The methods are the same as in table 1." ], "reasoning": "The table shows that the recall@1 for the model trained with English, French, German, and Czech using MUSE embeddings is 51.17%.", "question": "What is the recall@1 for the model trained with English, French, German, and Czech using MUSE embeddings?", "answer": { "answer": "51.17%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe recall@10 for the model trained with English and French using MUSE embeddings is higher than 92%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/306.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experiment 2: Image retrieval on the coco dataset. The methods are the same as in table 1." ], "reasoning": "The table shows that the recall@10 for the model trained with English and French using MUSE embeddings is 92.74%, which is higher than 92%.", "statement": "The recall@10 for the model trained with English and French using MUSE embeddings is higher than 92%.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Image Recall@10 on the Multi30k dataset with different languages with muse.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/307.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Image Recall@10 on the Multi30k dataset with different languages with muse." ], "reasoning": "Adding French decreases performance for English, but adding more languages increases recall in a multilingual environment, with the best performance achieved with English, French, German, and Czech.", "description": "The model is trained with English only, then with English and French (en+fr), with English, French, and German (en+fr+de) and with English, French, German and Czech (all). We can see a decrease in performance when adding French that is not present with other languages. Otherwise, every time we add a new language the recall for this language logically increase. The best performance is achieved with English+French+German+Czech, with an increase of 6.42 % for multilingual retrieval.. By using MUSE embeddings, we are able to embed more languages in the same model. We showed that adding other languages decrease performance for English, but increase the recall in a multilingual environment." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Image Recall@10 on the Multi30k dataset with different languages with muse.'\nProblem: 'What is the recall for English when trained with English, French, and German?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/307.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Image Recall@10 on the Multi30k dataset with different languages with muse." ], "reasoning": "To find the recall for English when trained with English, French, and German, look at the 'en' column in the 'en+fr+de' row.", "question": "What is the recall for English when trained with English, French, and German?", "answer": "So, the answer is {\"answer\": \"54.63\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTraining with English, French, German, and Czech achieves the highest recall for Czech.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/307.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Image Recall@10 on the Multi30k dataset with different languages with muse." ], "reasoning": "To verify this statement, compare the recall values for Czech (cs) across all training language combinations. The highest recall for Czech is found in the 'all' row.", "statement": "Training with English, French, German, and Czech achieves the highest recall for Czech.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Image Recall@10 on Multi30k dataset with different languages with bv Embeddings.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/308.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Image Recall@10 on Multi30k dataset with different languages with bv Embeddings." ], "reasoning": "Training with BIVEC embeddings on multiple languages improves recall performance compared to training on a single language, as shown by higher recall percentages in the table.", "description": "With BIVEC embeddings, we learn two languages at the same time, and test retrieval on one or two of these languages. Results are shown in table 4. Trained in English alone, the model gives worse performance than. MUSE for languages not seen previously. For example, with English-German BIVEC and a model trained only in English, and test on German, we obtain only 22.96 % recall@10, where MUSE embeddings obtain 44.18 %. But when train on English and French, we obtain 55.22 % recall, an increase of 26.39 % compared to MUSE. With German and English training, we have an increase of 15.16 % on English only recall, with a recall of 61.44 %. Meaning that, once again, learning a new language with BIVEC enables better results in English, as the same kind of results is visible with French as well." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Image Recall@10 on Multi30k dataset with different languages with bv Embeddings.'\nProblem: 'What is the recall@10 for English when trained on both English and German?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/308.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Image Recall@10 on Multi30k dataset with different languages with bv Embeddings." ], "reasoning": "The table shows that when trained on both English and German, the recall@10 for English is 61.44%.", "question": "What is the recall@10 for English when trained on both English and German?", "answer": "So, the answer is {\"answer\": \"61.44\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTraining on both English and French results in a recall@10 of 55.22% for French.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/308.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Image Recall@10 on Multi30k dataset with different languages with bv Embeddings." ], "reasoning": "The table indicates that when trained on both English and French, the recall@10 for French is indeed 55.22%.", "statement": "Training on both English and French results in a recall@10 of 55.22% for French.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Non-expert human performance results for a randomly-selected validator per question.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/309.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Non-expert human performance results for a randomly-selected validator per question." ], "reasoning": "The table shows that as the model used to construct the dataset becomes stronger, the resulting questions become harder for humans, as indicated by the decreasing EM and F1 scores.", "description": "We select a randomly chosen validator's answer to each question and compute Exact Match (EM) and word overlap F1 scores with the original to calculate non-expert human performance; Table 1 shows the result. We observe a clear trend: the stronger the model in the loop used to construct the dataset, the harder the resulting questions become for humans." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Non-expert human performance results for a randomly-selected validator per question.'\nProblem: 'Which model has the highest Test F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/309.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Non-expert human performance results for a randomly-selected validator per question." ], "reasoning": "To find the model with the highest Test F1 score, compare the Test F1 scores of all models listed in the table. DBiDAF has the highest Test F1 score of 78.5.", "question": "Which model has the highest Test F1 score?", "answer": "So, the answer is {\"answer\": \"DBiDAF\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDBERT has the highest Dev EM score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/309.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Non-expert human performance results for a randomly-selected validator per question." ], "reasoning": "To verify this statement, compare the Dev EM scores of all models listed in the table. DBERT has a Dev EM score of 59.2, which is lower than DBiDAF's Dev EM score of 63.0.", "statement": "DBERT has the highest Dev EM score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Abstractive summarization results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/310.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Abstractive summarization results." ], "reasoning": "Incorporating discourse features improves recall and F1 scores for both baselines (PG and PG+Cov), but generally at the expense of precision.", "description": "We assess unigram (R-1), bigram (R-2), and longest-commonsubsequence (R-L) overlap, and present F1, recall and precision scores in Table 1.. For the first baseline (PG), we see that incorporating discourse features consistently improves recall and F1.. We see similar observations for the second baseline (PG+Cov): recall is generally improved at the expense of precision.. Observing that our model generally has better recall (Table 1)" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Abstractive summarization results.'\nProblem: 'Which method has the highest recall for R-1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/310.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Abstractive summarization results." ], "reasoning": "To find the method with the highest recall for R-1, we need to compare the recall R-1 values across all methods. The highest recall R-1 value is 44.44, which corresponds to the method PG+Cov +M1-latent.", "question": "Which method has the highest recall for R-1?", "answer": "So, the answer is {\"answer\": \"PG+Cov +M1-latent\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe method PG+Cov +M1-latent has the highest precision for R-1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/310.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Abstractive summarization results." ], "reasoning": "To verify this statement, we need to compare the precision R-1 values across all methods. The highest precision R-1 value is 40.82, which corresponds to the method PG+Cov, not PG+Cov +M1-latent.", "statement": "The method PG+Cov +M1-latent has the highest precision for R-1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Average petition regression performance over 3 runs (noting that lower is better for both MAE and MAPE). One-sided t-tests show that both (1) and (2) are significantly better than the baseline (p<0.05 and p<0.005, resp.).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/311.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average petition regression performance over 3 runs (noting that lower is better for both MAE and MAPE). One-sided t-tests show that both (1) and (2) are significantly better than the baseline (p<0.05 and p<0.005, resp.)." ], "reasoning": "Adding shallow discourse features to the baseline model improves results, while replacing CNN with LSTM improves MAE but worsens MAPE. Using EDUs with latent features outperforms shallow features.", "description": "We present the test results in Table 2.. We are able to reproduce the performance of the baseline model (\"CNN w/ GloVe\"), and find that once again, adding the shallow discourse features improves results.. Interestingly, we found that replacing the CNN with an LSTM results in improved MAE, but worse MAPE. Adding discourse features to this model generally has marginal improvement in all cases.. When we replace the word sequence with EDUs (\"Bi-LSTM w/ latent\" and \"Bi-LSTM w/ shallow\"), we see that the latent features outperform the shallow features." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Average petition regression performance over 3 runs (noting that lower is better for both MAE and MAPE). One-sided t-tests show that both (1) and (2) are significantly better than the baseline (p<0.05 and p<0.005, resp.).'\nProblem: 'Which model configuration achieved the lowest MAE?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/311.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average petition regression performance over 3 runs (noting that lower is better for both MAE and MAPE). One-sided t-tests show that both (1) and (2) are significantly better than the baseline (p<0.05 and p<0.005, resp.)." ], "reasoning": "By examining the MAE column, the lowest value is 1.11, which corresponds to the 'Bi-LSTM w/ latent' model.", "question": "Which model configuration achieved the lowest MAE?", "answer": "So, the answer is {\"answer\": \"Bi-LSTM w/ latent\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'CNN w/ GloVe' model has the highest MAPE.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/311.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average petition regression performance over 3 runs (noting that lower is better for both MAE and MAPE). One-sided t-tests show that both (1) and (2) are significantly better than the baseline (p<0.05 and p<0.005, resp.)." ], "reasoning": "By examining the MAPE column, the highest value is 14.67, which corresponds to the 'Bi-LSTM w/ shallow' model, not 'CNN w/ GloVe'.", "statement": "The 'CNN w/ GloVe' model has the highest MAPE.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results on IMDb dataset over different teacher sizes. BERTSDV(K=1) is same as BERTSDA(K=1). For IMDb and AG’s News, we report test error rate (%). For SNLI, we report accuracy (%). T denotes the total number of iterations.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/312.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on IMDb dataset over different teacher sizes. BERTSDV(K=1) is same as BERTSDA(K=1). For IMDb and AG’s News, we report test error rate (%). For SNLI, we report accuracy (%). T denotes the total number of iterations." ], "reasoning": "The table shows that the best teacher size varies across different datasets, indicating sensitivity to dataset characteristics.", "description": "We choose different teacher size K and evaluate our models in three datasets. Table 2 shows that teacher size is sensitive to datasets. Therefore, we select the best teacher size for each dataset in the following experiment." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results on IMDb dataset over different teacher sizes. BERTSDV(K=1) is same as BERTSDA(K=1). For IMDb and AG’s News, we report test error rate (%). For SNLI, we report accuracy (%). T denotes the total number of iterations.'\nProblem: 'What is the best teacher size for the AG's News dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/312.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on IMDb dataset over different teacher sizes. BERTSDV(K=1) is same as BERTSDA(K=1). For IMDb and AG’s News, we report test error rate (%). For SNLI, we report accuracy (%). T denotes the total number of iterations." ], "reasoning": "To find the best teacher size for the AG's News dataset, look for the highest score in the AG's News column. The highest score is 5.55, which corresponds to BERTSDV with K=4.", "question": "What is the best teacher size for the AG's News dataset?", "answer": "So, the answer is {\"answer\": \"4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe best teacher size for the IMDb dataset is K=5.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/312.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on IMDb dataset over different teacher sizes. BERTSDV(K=1) is same as BERTSDA(K=1). For IMDb and AG’s News, we report test error rate (%). For SNLI, we report accuracy (%). T denotes the total number of iterations." ], "reasoning": "To verify this statement, look for the highest score in the IMDb column. The highest score is 5.48, which corresponds to BERTSDA with K=3, not K=5.", "statement": "The best teacher size for the IMDb dataset is K=5.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Effects on fine-tuning the BERT-base model (BERTBASE). ‘*’ indicates using extra fine-tuning strategies and data preprocessing. ‘/’ means no available reported result. We implemented a “BERTBASE” without any extra fine-tuning strategy as our baseline. “BERTVOTE” and “BERTAVG” means ensemble BERT (See section 3.1). “BERTSE” means self-ensemble BERT (See section 3.2). “BERTSDV” and “BERTSDA” means self-distillation BERT (See section 3.3). ‘Avg. Δ’ means the average of relative change, respectively. We bold the better self-distillation results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/313.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effects on fine-tuning the BERT-base model (BERTBASE). ‘*’ indicates using extra fine-tuning strategies and data preprocessing. ‘/’ means no available reported result. We implemented a “BERTBASE” without any extra fine-tuning strategy as our baseline. “BERTVOTE” and “BERTAVG” means ensemble BERT (See section 3.1). “BERTSE” means self-ensemble BERT (See section 3.2). “BERTSDV” and “BERTSDA” means self-distillation BERT (See section 3.3). ‘Avg. Δ’ means the average of relative change, respectively. We bold the better self-distillation results." ], "reasoning": "BERTVOTE and BERTAVG outperform BERTBASE on both text classification and NLI tasks, with BERTVOTE showing the highest improvement. BERTSDA and BERTSDV also outperform BERTBASE, with BERTSDA excelling in text classification and BERTSDV in NLI tasks.", "description": "Table 3 shows the results of fine-tuning the BERT-base model on five text classification datasets and two NLI datasets. For ensemble BERT, both the voted BERT (BERTVOTE) and averaged BERT (BERTAVG) outperform the single BERT (BERTBASE). The average improvement of BERTVOTE is 5.44% (for text clas. sification) and 5.50% (for NLI), while BERTAVG follows closely with 4.07% and 3.24%. BERTVOTE outperforms BERTAVG on all tasks, which adheres to our intuition since BERTVOTE is more complicated. The self-ensemble BERT (BERTSE) has a slight improvement in classification tasks of 2.50%, but it does not work on NLI tasks. This is also a reason why we need self-distillation to improve the base models. Overall, self-distillation model has significant improvement on both classification and NLI tasks. Table 3 shows that BERTSDA and BERTSDV outperform BERTBASE on all datasets. Generally speaking, BERTSDA performs better than BERTSDV on text classification tasks with the improvement of 6.26% vs. 5.65%, but the latter performs better on NLI tasks (BERTSDA vs. BERTSDV is 4.65% vs. 5.30%). Our proposed fine-tuning strategies also outperform the previous method in [Sun et al., 2019] on text classification tasks, which makes extensive efforts to find sophisticated hyperparameters." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Effects on fine-tuning the BERT-base model (BERTBASE). ‘*’ indicates using extra fine-tuning strategies and data preprocessing. ‘/’ means no available reported result. We implemented a “BERTBASE” without any extra fine-tuning strategy as our baseline. “BERTVOTE” and “BERTAVG” means ensemble BERT (See section 3.1). “BERTSE” means self-ensemble BERT (See section 3.2). “BERTSDV” and “BERTSDA” means self-distillation BERT (See section 3.3). ‘Avg. Δ’ means the average of relative change, respectively. We bold the better self-distillation results.'\nProblem: 'Which model has the highest average improvement in text classification tasks?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/313.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effects on fine-tuning the BERT-base model (BERTBASE). ‘*’ indicates using extra fine-tuning strategies and data preprocessing. ‘/’ means no available reported result. We implemented a “BERTBASE” without any extra fine-tuning strategy as our baseline. “BERTVOTE” and “BERTAVG” means ensemble BERT (See section 3.1). “BERTSE” means self-ensemble BERT (See section 3.2). “BERTSDV” and “BERTSDA” means self-distillation BERT (See section 3.3). ‘Avg. Δ’ means the average of relative change, respectively. We bold the better self-distillation results." ], "reasoning": "The table shows the average improvement (Avg. Δ) for each model in text classification tasks. BERTSDA has the highest average improvement of 6.26%.", "question": "Which model has the highest average improvement in text classification tasks?", "answer": { "answer": "BERTSDA" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBERTSE has the highest average improvement in NLI tasks.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/313.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effects on fine-tuning the BERT-base model (BERTBASE). ‘*’ indicates using extra fine-tuning strategies and data preprocessing. ‘/’ means no available reported result. We implemented a “BERTBASE” without any extra fine-tuning strategy as our baseline. “BERTVOTE” and “BERTAVG” means ensemble BERT (See section 3.1). “BERTSE” means self-ensemble BERT (See section 3.2). “BERTSDV” and “BERTSDA” means self-distillation BERT (See section 3.3). ‘Avg. Δ’ means the average of relative change, respectively. We bold the better self-distillation results." ], "reasoning": "The table shows the average improvement (Avg. Δ) for each model in NLI tasks. BERTSE has an average improvement of 0.51%, which is lower than BERTVOTE, BERTAVG, BERTSDV, and BERTSDA.", "statement": "BERTSE has the highest average improvement in NLI tasks.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Effects on fine-tuning the BERT-large model (BERT-L). For IMDb and AG’s News, we report test error rate (%). For SNLI, we report accuracy (%). MT-DNN fine-tunes BERT with multi-task learning.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/314.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Effects on fine-tuning the BERT-large model (BERT-L). For IMDb and AG’s News, we report test error rate (%). For SNLI, we report accuracy (%). MT-DNN fine-tunes BERT with multi-task learning." ], "reasoning": "Self-distillation with averaged BERT as a teacher significantly improves the performance of the BERT-large model on text classification and NLI tasks, with average improvements of 7.02% and 6.59% respectively.", "description": "We also investigate whether self-distillation has similar findings for the BERTlarge model (BERT-L), which contains 24 Transformer layers. Due to the limitation of our devices, we only conduct an experiment on two text classification datasets and one NLI datasets and evaluate strategy BERTSDA, namely self-distillation with averaged BERT as a teacher. We set two different teacher sizes for comparison. As shown in Table 4, self-distillation also gets a significant gain while fine-tuning the BERT-large model. On two text classification tasks, BERT-LSDA(K =. − 1) gives better results and the average improvement is 7.02%. For NLI task, BERT-LSDA(K = 1) gives better result and the improvement is 6.59%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Effects on fine-tuning the BERT-large model (BERT-L). For IMDb and AG’s News, we report test error rate (%). For SNLI, we report accuracy (%). MT-DNN fine-tunes BERT with multi-task learning.'\nProblem: 'What is the average improvement in performance for BERT-LSDA(K = T-1) on text classification tasks?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/314.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Effects on fine-tuning the BERT-large model (BERT-L). For IMDb and AG’s News, we report test error rate (%). For SNLI, we report accuracy (%). MT-DNN fine-tunes BERT with multi-task learning." ], "reasoning": "The table shows that BERT-LSDA(K = T-1) has an average improvement of 7.02% on text classification tasks.", "question": "What is the average improvement in performance for BERT-LSDA(K = T-1) on text classification tasks?", "answer": "So, the answer is {\"answer\": \"7.02%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBERT-LSDA(K = 1) achieves a 6.59% improvement on the SNLI task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/314.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Effects on fine-tuning the BERT-large model (BERT-L). For IMDb and AG’s News, we report test error rate (%). For SNLI, we report accuracy (%). MT-DNN fine-tunes BERT with multi-task learning." ], "reasoning": "The table indicates that BERT-LSDA(K = 1) has a 6.59% improvement on the SNLI task.", "statement": "BERT-LSDA(K = 1) achieves a 6.59% improvement on the SNLI task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Automatic evaluation with perplexity.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/315.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Automatic evaluation with perplexity." ], "reasoning": "TransDG has the lowest perplexity across all datasets, indicating superior grammatical accuracy in generated responses.", "description": "As shown in Table 2, TransDG achieves the lowest perplexity on all the datasets, indicating that the generated responses are more grammatical." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Automatic evaluation with perplexity.'\nProblem: 'Which model achieves the lowest perplexity in the 'Medium' dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/315.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Automatic evaluation with perplexity." ], "reasoning": "By examining the 'Medium' column in the table, we see that TransDG has the lowest perplexity value of 36.12.", "question": "Which model achieves the lowest perplexity in the 'Medium' dataset?", "answer": "So, the answer is {\"answer\": \"TransDG\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTransDG has the highest perplexity in the 'High' dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/315.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Automatic evaluation with perplexity." ], "reasoning": "The table shows that TransDG has a perplexity of 32.18 in the 'High' dataset, which is the lowest among all models, not the highest.", "statement": "TransDG has the highest perplexity in the 'High' dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Automatic evaluation with entity score.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/316.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Automatic evaluation with entity score." ], "reasoning": "Models using external knowledge, especially TransDG, outperform the standard Seq2Seq model in generating meaningful entity words and diverse responses.", "description": "Table 3 demonstrates that the models leveraging external knowledge achieve better performance than the standard Seq2Seq model in generating meaningful entity words and diverse responses. In particular, our model outperforms all the baselines significantly with highest entity score." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Automatic evaluation with entity score.'\nProblem: 'Which model achieves the highest entity score in the 'Medium' category?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/316.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Automatic evaluation with entity score." ], "reasoning": "By comparing the 'Medium' scores of all models, TransDG has the highest score of 1.204.", "question": "Which model achieves the highest entity score in the 'Medium' category?", "answer": { "answer": "TransDG" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Seq2Seq model has the lowest performance in the 'OOV' category.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/316.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Automatic evaluation with entity score." ], "reasoning": "By comparing the 'OOV' scores of all models, Seq2Seq has the lowest score of 0.669.", "statement": "The Seq2Seq model has the lowest performance in the 'OOV' category.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Automatic evaluation with BLEU.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/317.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Automatic evaluation with BLEU." ], "reasoning": "TransDG achieves higher BLEU scores compared to other models, indicating it generates responses more similar to the gold responses.", "description": "The BLEU values shown in Table 4 demonstrates the comparison results from word-level overlaps. TransDG tends to generate responses that are more similar to the gold responses than baselines in most cases." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Automatic evaluation with BLEU.'\nProblem: 'Which model has the highest BLEU-1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/317.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Automatic evaluation with BLEU." ], "reasoning": "By comparing the BLEU-1 scores of all models, TransDG has the highest score of 0.1807.", "question": "Which model has the highest BLEU-1 score?", "answer": "So, the answer is {\"answer\": \"TransDG\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCopyNet has the highest BLEU-2 score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/317.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Automatic evaluation with BLEU." ], "reasoning": "By examining the BLEU-2 scores, CopyNet has the highest score of 0.0181, which is higher than the scores of other models.", "statement": "CopyNet has the highest BLEU-2 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Human evaluation result.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/318.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Human evaluation result." ], "reasoning": "TransDG generates more appropriate and informative responses with higher knowledge relevance compared to other models.", "description": "The human evaluation results are reported in Table 5. As shown in Table 5, TransDG tends to generate more appropriate and informative responses in terms of human annotation. Specifically, the responses generated by TransDG have higher knowledge relevance than other models, indicating that TransDG is effective to incorporate appropriate commonsense knowledge." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Human evaluation result.'\nProblem: 'Which model has the highest relevance score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/318.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Human evaluation result." ], "reasoning": "By comparing the relevance scores of all models, TransDG has the highest relevance score of 1.52.", "question": "Which model has the highest relevance score?", "answer": "So, the answer is {\"answer\": \"TransDG\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTransDG has the highest fluency score among all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/318.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Human evaluation result." ], "reasoning": "By comparing the fluency scores of all models, TransDG has the highest fluency score of 2.41.", "statement": "TransDG has the highest fluency score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Ablation results of TransDG on the test set. Here, Entity represents entity score.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/319.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Ablation results of TransDG on the test set. Here, Entity represents entity score." ], "reasoning": "Removing the question representation and knowledge selection modules significantly reduces TransDG's performance, while response guiding attention and the second-step decoder also impact performance, especially on BLEU scores.", "description": "The ablation test results are reported in Table 7. From the results, we can observe that the performance of TransDG drops sharply when we discard the question representation module and the knowledge selection module transferred from KBQA.. Response guiding attention also has noticeable impact on the performance of TransDG, especially on BLEU scores.. In addition, the second-step decoder can improve the ability of TransDG to generate relevant entities per response." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Ablation results of TransDG on the test set. Here, Entity represents entity score.'\nProblem: 'What is the BLEU-1 score when both the question representation and knowledge selection modules are discarded?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/319.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Ablation results of TransDG on the test set. Here, Entity represents entity score." ], "reasoning": "The table shows that when both the question representation and knowledge selection modules are discarded (w/o QRT+KST), the BLEU-1 score is 0.1612.", "question": "What is the BLEU-1 score when both the question representation and knowledge selection modules are discarded?", "answer": { "answer": "0.1612" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe second-step decoder has no impact on the BLEU-2 score of TransDG.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/319.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Ablation results of TransDG on the test set. Here, Entity represents entity score." ], "reasoning": "The table shows that removing the second-step decoder (w/o SSD) results in a BLEU-2 score of 0.0178, which is the same as the original TransDG model. Therefore, the statement is supported.", "statement": "The second-step decoder has no impact on the BLEU-2 score of TransDG.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Test accuracy for all models on OVERNIGHT dataset, which has eight domains: Basketball, Blocks, Calendar, Housing, Publications, Recipes, Restaurants, and Social. We use the generator-reranker (G-R) architecture with different options. Beam-n: Beam search is applied with size n, pQ: The critic is pre-trained over the Quora dataset, TH1: rerank if there is at least one score above 0.5, TH2: rerank if best score−second best score>0.001. The candidate logical forms are processed with templated expansions method (Section 3.2.3) in this experiment.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/320.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Test accuracy for all models on OVERNIGHT dataset, which has eight domains: Basketball, Blocks, Calendar, Housing, Publications, Recipes, Restaurants, and Social. We use the generator-reranker (G-R) architecture with different options. Beam-n: Beam search is applied with size n, pQ: The critic is pre-trained over the Quora dataset, TH1: rerank if there is at least one score above 0.5, TH2: rerank if best score−second best score>0.001. The candidate logical forms are processed with templated expansions method (Section 3.2.3) in this experiment." ], "reasoning": "The model improves state-of-the-art performance from 79.6% to 82.15% accuracy without reranking, and further to 83.7% with the overall architecture.", "description": "We compare our model with the state-of-the-art models in Table 3.. As Table 3 shows, this model alone (without any reranking) improves the state-of-the-art performance from 79.6% to 82.15% accuracy. Increasing the beam size improves the performance as expected.. Using a pre-trained model improves the performance as well.. Reranking with a threshold rule may be helpful for the overall architecture. We observe that reranking by the critic at all times may not be the best approach. We note that choosing not to rerank when all scores are below 0.5 increases the performance further. On the other hand, reranking if the difference between the best score and second best score is above the threshold we set does not help in this case.. The overall architecture improves the performance of the generator (82.1% accuracy) to 83.7% accuracy." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Test accuracy for all models on OVERNIGHT dataset, which has eight domains: Basketball, Blocks, Calendar, Housing, Publications, Recipes, Restaurants, and Social. We use the generator-reranker (G-R) architecture with different options. Beam-n: Beam search is applied with size n, pQ: The critic is pre-trained over the Quora dataset, TH1: rerank if there is at least one score above 0.5, TH2: rerank if best score−second best score>0.001. The candidate logical forms are processed with templated expansions method (Section 3.2.3) in this experiment.'\nProblem: 'What is the average accuracy of the G-R (Beam-25 & pQ & TH1) method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/320.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Test accuracy for all models on OVERNIGHT dataset, which has eight domains: Basketball, Blocks, Calendar, Housing, Publications, Recipes, Restaurants, and Social. We use the generator-reranker (G-R) architecture with different options. Beam-n: Beam search is applied with size n, pQ: The critic is pre-trained over the Quora dataset, TH1: rerank if there is at least one score above 0.5, TH2: rerank if best score−second best score>0.001. The candidate logical forms are processed with templated expansions method (Section 3.2.3) in this experiment." ], "reasoning": "The table shows the average accuracy of the G-R (Beam-25 & pQ & TH1) method as 83.7%.", "question": "What is the average accuracy of the G-R (Beam-25 & pQ & TH1) method?", "answer": { "answer": "83.7%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe G-R (Beam-25 & pQ) method has an average accuracy of 83.4%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/320.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Test accuracy for all models on OVERNIGHT dataset, which has eight domains: Basketball, Blocks, Calendar, Housing, Publications, Recipes, Restaurants, and Social. We use the generator-reranker (G-R) architecture with different options. Beam-n: Beam search is applied with size n, pQ: The critic is pre-trained over the Quora dataset, TH1: rerank if there is at least one score above 0.5, TH2: rerank if best score−second best score>0.001. The candidate logical forms are processed with templated expansions method (Section 3.2.3) in this experiment." ], "reasoning": "The table shows that the G-R (Beam-25 & pQ) method has an average accuracy of 83.4%.", "statement": "The G-R (Beam-25 & pQ) method has an average accuracy of 83.4%.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: The performances of the IM2LATEX-100K Bi-LSTM model. We discover that the look-ahead improves the model from the greedy search method — noted that LA is more directly comparable to the greedy search because of their same beam size. We also show the scores of the beam search for the reference'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/321.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The performances of the IM2LATEX-100K Bi-LSTM model. We discover that the look-ahead improves the model from the greedy search method — noted that LA is more directly comparable to the greedy search because of their same beam size. We also show the scores of the beam search for the reference" ], "reasoning": "The 5-LA search strategy achieves the highest BLEU score, indicating that look-ahead improves the model performance compared to greedy search and beam search.", "description": "Table 1: The performances of the IM2LATEX-100K Bi-LSTM model. We discover that the look-ahead improves the model from the greedy search method — noted that LA is more directly comparable to the greedy search because of their same beam size. We also show the scores of the beam search for the reference" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: The performances of the IM2LATEX-100K Bi-LSTM model. We discover that the look-ahead improves the model from the greedy search method — noted that LA is more directly comparable to the greedy search because of their same beam size. We also show the scores of the beam search for the reference'\nProblem: 'Which search strategy achieves the highest BLEU score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/321.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The performances of the IM2LATEX-100K Bi-LSTM model. We discover that the look-ahead improves the model from the greedy search method — noted that LA is more directly comparable to the greedy search because of their same beam size. We also show the scores of the beam search for the reference" ], "reasoning": "By comparing the BLEU scores of all search strategies listed in the table, the 5-LA search strategy has the highest score of 86.79.", "question": "Which search strategy achieves the highest BLEU score?", "answer": { "answer": "5-LA" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe greedy search strategy achieves a higher BLEU score than the beam search strategy.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/321.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The performances of the IM2LATEX-100K Bi-LSTM model. We discover that the look-ahead improves the model from the greedy search method — noted that LA is more directly comparable to the greedy search because of their same beam size. We also show the scores of the beam search for the reference" ], "reasoning": "The table shows that the BLEU score for the greedy search strategy is 86.24, while the BLEU score for the beam search strategy is 86.28. Therefore, the statement is refuted.", "statement": "The greedy search strategy achieves a higher BLEU score than the beam search strategy.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Average number of words per question and answer, and average longest n-gram overlap between passage and question.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/322.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average number of words per question and answer, and average longest n-gram overlap between passage and question." ], "reasoning": "As models improve, the average length of answers increases, while the largest n-gram overlap between passage and question decreases from 3 to 2 tokens.", "description": "The average number of words in questions and answers, as well as the average longest n-gram overlap between passage and question are furthermore given in Table 3. We can again observe two clear trends: from weaker towards stronger models used in the annotation loop, the average length of answers increases, and the largest n-gram overlap drops from 3 to 2 tokens." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Average number of words per question and answer, and average longest n-gram overlap between passage and question.'\nProblem: 'What is the trend in the average answer length as models become stronger?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/322.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average number of words per question and answer, and average longest n-gram overlap between passage and question." ], "reasoning": "The table shows that as models progress from DSQuAD to DRoBERTa, the average answer length increases from 2.6 to 3.2.", "question": "What is the answer length for DBiDAF?", "answer": { "answer": "2.9" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average question length for DBERT is 9.8.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/322.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average number of words per question and answer, and average longest n-gram overlap between passage and question." ], "reasoning": "The table indicates that the average question length for DBERT is indeed 9.8.", "statement": "The average question length for DBERT is 9.8.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: The performances of the LSTM model trained on the WMT16 multimodal translation dataset with different LA steps. We show the look-ahead module is able to improve the model on the entire testing set. However, either the LA module or the beam search method harm the models when the length of the target sentences is longer than 25 words.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/323.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The performances of the LSTM model trained on the WMT16 multimodal translation dataset with different LA steps. We show the look-ahead module is able to improve the model on the entire testing set. However, either the LA module or the beam search method harm the models when the length of the target sentences is longer than 25 words." ], "reasoning": "The look-ahead (LA) module improves the LSTM model's performance on the entire testing set, but both the LA module and beam search harm performance for target sentences longer than 25 words.", "description": "Table 2: The performances of the LSTM model trained on the WMT16 multimodal translation dataset with different LA steps. We show the look-ahead module is able to improve the model on the entire testing set. However, either the LA module or the beam search method harm the models when the length of the target sentences is longer than 25 words." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: The performances of the LSTM model trained on the WMT16 multimodal translation dataset with different LA steps. We show the look-ahead module is able to improve the model on the entire testing set. However, either the LA module or the beam search method harm the models when the length of the target sentences is longer than 25 words.'\nProblem: 'Which search strategy achieves the highest BLEU score on the entire testing set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/323.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The performances of the LSTM model trained on the WMT16 multimodal translation dataset with different LA steps. We show the look-ahead module is able to improve the model on the entire testing set. However, either the LA module or the beam search method harm the models when the length of the target sentences is longer than 25 words." ], "reasoning": "To find the highest BLEU score on the entire testing set, we need to compare the BLEU scores of all search strategies listed in the table. The highest BLEU score is 33.83, achieved by the Beam Search (B=10) strategy.", "question": "Which search strategy achieves the highest BLEU score on the entire testing set?", "answer": "So, the answer is {\"answer\": \"Beam Search (B=10)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 4-LA search strategy achieves the highest BLEU score for target sentences longer than 25 words.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/323.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The performances of the LSTM model trained on the WMT16 multimodal translation dataset with different LA steps. We show the look-ahead module is able to improve the model on the entire testing set. However, either the LA module or the beam search method harm the models when the length of the target sentences is longer than 25 words." ], "reasoning": "To verify this statement, we need to compare the BLEU scores for target sentences longer than 25 words across all search strategies. The highest BLEU score for target sentences longer than 25 words is 23.86, achieved by the Greedy Search strategy, not the 4-LA strategy.", "statement": "The 4-LA search strategy achieves the highest BLEU score for target sentences longer than 25 words.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: We show the results of applying LA module to the transformer model trained on the WMT14 dataset. We find that the LA module slightly improves the original model but harms the performance when the LA time step is 5. We suggest one of the reasons of these results are caused by the EOS problem.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/324.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: We show the results of applying LA module to the transformer model trained on the WMT14 dataset. We find that the LA module slightly improves the original model but harms the performance when the LA time step is 5. We suggest one of the reasons of these results are caused by the EOS problem." ], "reasoning": "The LA module improves the transformer's performance on the WMT14 dataset except when the LA time step is 5, potentially due to the EOS problem.", "description": "Table 3: We show the results of applying LA module to the transformer model trained on the WMT14 dataset. We find that the LA module slightly improves the original model but harms the performance when the LA time step is 5. We suggest one of the reasons of these results are caused by the EOS problem." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: We show the results of applying LA module to the transformer model trained on the WMT14 dataset. We find that the LA module slightly improves the original model but harms the performance when the LA time step is 5. We suggest one of the reasons of these results are caused by the EOS problem.'\nProblem: 'What is the BLEU score when the LA time step is 5, and how does it compare to the Greedy Search?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/324.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: We show the results of applying LA module to the transformer model trained on the WMT14 dataset. We find that the LA module slightly improves the original model but harms the performance when the LA time step is 5. We suggest one of the reasons of these results are caused by the EOS problem." ], "reasoning": "The BLEU score for the 5-LA time step is 27.35, which is lower than the Greedy Search score of 27.50.", "question": "What is the BLEU score when the LA time step is 5?", "answer": { "answer": "27.35" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 2-LA search strategy has the highest BLEU score among all LA strategies.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/324.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: We show the results of applying LA module to the transformer model trained on the WMT14 dataset. We find that the LA module slightly improves the original model but harms the performance when the LA time step is 5. We suggest one of the reasons of these results are caused by the EOS problem." ], "reasoning": "The table shows that the 2-LA search strategy has a BLEU score of 27.71, which is higher than the scores for 3-LA, 4-LA, and 5-LA strategies.", "statement": "The 2-LA search strategy has the highest BLEU score among all LA strategies.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: We show the results of integrating auxiliary EOS loss into the training state. γ is the weight of the auxiliary EOS loss. We find the EOS loss not only boosts the performance of the model when using the greedy search, the model is more robust to the larger Look-ahead steps with reasonable weights of auxiliary EOS loss.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/325.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: We show the results of integrating auxiliary EOS loss into the training state. γ is the weight of the auxiliary EOS loss. We find the EOS loss not only boosts the performance of the model when using the greedy search, the model is more robust to the larger Look-ahead steps with reasonable weights of auxiliary EOS loss." ], "reasoning": "Integrating auxiliary EOS loss into the training state improves model performance, especially with greedy search and larger Look-ahead steps.", "description": "Table 4: We show the results of integrating auxiliary EOS loss into the training state. γ is the weight of the auxiliary EOS loss. We find the EOS loss not only boosts the performance of the model when using the greedy search, the model is more robust to the larger Look-ahead steps with reasonable weights of auxiliary EOS loss." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: We show the results of integrating auxiliary EOS loss into the training state. γ is the weight of the auxiliary EOS loss. We find the EOS loss not only boosts the performance of the model when using the greedy search, the model is more robust to the larger Look-ahead steps with reasonable weights of auxiliary EOS loss.'\nProblem: 'What is the highest score achieved with the 3-LA search strategy and what weight of auxiliary EOS loss was used?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/325.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: We show the results of integrating auxiliary EOS loss into the training state. γ is the weight of the auxiliary EOS loss. We find the EOS loss not only boosts the performance of the model when using the greedy search, the model is more robust to the larger Look-ahead steps with reasonable weights of auxiliary EOS loss." ], "reasoning": "To find the highest score for the 3-LA search strategy, look at the row labeled '3-LA' and identify the highest value. The highest score is 28.10, which corresponds to a γ value of 1.0.", "question": "What is the highest score achieved with the 3-LA search strategy?", "answer": "So, the answer is {\"answer\": \"28.10\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model achieves the highest score with the 2-LA search strategy when γ is 1.0.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/325.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: We show the results of integrating auxiliary EOS loss into the training state. γ is the weight of the auxiliary EOS loss. We find the EOS loss not only boosts the performance of the model when using the greedy search, the model is more robust to the larger Look-ahead steps with reasonable weights of auxiliary EOS loss." ], "reasoning": "To verify this statement, look at the row labeled '2-LA' and find the highest score. The highest score in this row is 28.20, which corresponds to a γ value of 1.0.", "statement": "The model achieves the highest score with the 2-LA search strategy when γ is 1.0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Translation quality evaluation (BLEU scores).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/326.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Translation quality evaluation (BLEU scores)." ], "reasoning": "The Local Joint Self-attention model achieves the highest BLEU scores on IWSLT'14 de-en and WMT'14 en-fr benchmarks, surpassing previous models.", "description": "The entry Joint Self-attention corresponds to the results of our implementation of (He et al., 2018), that significantly improves the original results by 0.7 BLEU point on the WMT14 de-en benchmark, and 0.2 on IWSLT. The same architecture with the proposed locality constraints (Local Joint Self-attention) establishes a new state of the art in IWSLT'14 de-en with 35.7 BLEU, surpassing all previous published results by at least in 0.5 BLEU, and our results with the unconstrained version by 0.4. The. Table 1 presents a comparison of the translation quality measured via BLEU score between the currently dominant Transformer (Vaswani et al., 2017) and Dynamic Convolutions (Wu et al., 2019) models, as well as the work by He et al. (2018), which also proposes a joint encoderdecoder structure, and also other refinements over the transformer architecture like (Ahmed et al., 2017), (Chen et al., 2018), (Shaw et al., 2018) and (Ott et al., 2018) .. The Joint Self-attention model obtains the same SoTA BLEU score of (Wu et al., 2019) on WMT'14 en-de, and the same SoTA score of (Ott et al., 2018) and (Wu et al., 2019) on WMT'14 enfr. The local attention constraints do not provide a significant gain on these bigger models, but it improves the BLEU score on WMT'14 en-fr to a new SoTA of 43.3." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Translation quality evaluation (BLEU scores).'\nProblem: 'Which model achieves the highest BLEU score on the IWSLT'14 de-en benchmark?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/326.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Translation quality evaluation (BLEU scores)." ], "reasoning": "The table shows that the Local Joint Self-attention model achieves a BLEU score of 35.7 on the IWSLT'14 de-en benchmark, which is the highest score listed.", "question": "Which model achieves the highest BLEU score on the IWSLT'14 de-en benchmark?", "answer": { "answer": "Local Joint Self-attention" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Joint Self-attention model achieves the highest BLEU score on the WMT'14 en-de benchmark.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/326.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Translation quality evaluation (BLEU scores)." ], "reasoning": "The table shows that the Joint Self-attention model achieves a BLEU score of 29.7 on the WMT'14 en-de benchmark, which is the same as the wu2018dynconv and Local Joint Self-attention models, but not higher.", "statement": "The Joint Self-attention model achieves the highest BLEU score on the WMT'14 en-de benchmark.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Results of text-line extraction on the DIVA-HisDB dataset (see Section III-A measured with the competition tool(see Section IV-A. Our proposed method outperforms state-of-the-art results by reducing the error by 80.7% and achieving nearly perfect results. Methods with * notation use semantic segmentation at pixel-level as pre-processing step.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/327.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Results of text-line extraction on the DIVA-HisDB dataset (see Section III-A measured with the competition tool(see Section IV-A. Our proposed method outperforms state-of-the-art results by reducing the error by 80.7% and achieving nearly perfect results. Methods with * notation use semantic segmentation at pixel-level as pre-processing step." ], "reasoning": "The proposed method achieves nearly perfect results with 99.42% in Line iu % and outperforms the state-of-the-art method which has 97.86%, resulting in an error reduction of 80.7%.", "description": "In Table. there are the results on the DIVA-HisDB dataset (see Section III-A). Our method achieves nearly perfect results (99.42%) and outperforms state-of-the-art (97.86%) resulting in a error reduction of 80.7%.. Note that the lower end of the heatmap scale compares favourably with state-ofthe-art (see Table I) meaning that regardless of the choice of parameters, our method produces excellent results." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: Results of text-line extraction on the DIVA-HisDB dataset (see Section III-A measured with the competition tool(see Section IV-A. Our proposed method outperforms state-of-the-art results by reducing the error by 80.7% and achieving nearly perfect results. Methods with * notation use semantic segmentation at pixel-level as pre-processing step.'\nProblem: 'What is the Line iu % achieved by the proposed method and how does it compare to the state-of-the-art method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/327.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Results of text-line extraction on the DIVA-HisDB dataset (see Section III-A measured with the competition tool(see Section IV-A. Our proposed method outperforms state-of-the-art results by reducing the error by 80.7% and achieving nearly perfect results. Methods with * notation use semantic segmentation at pixel-level as pre-processing step." ], "reasoning": "The proposed method achieves a Line iu % of 99.42%, which is higher than the state-of-the-art method's 97.86%, indicating superior performance.", "question": "What is the Line iu % achieved by the proposed method and how does it compare to the state-of-the-art method?", "answer": { "answer": "99.42%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed method achieves a Line iu % of 99.42%, outperforming the state-of-the-art method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/327.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Results of text-line extraction on the DIVA-HisDB dataset (see Section III-A measured with the competition tool(see Section IV-A. Our proposed method outperforms state-of-the-art results by reducing the error by 80.7% and achieving nearly perfect results. Methods with * notation use semantic segmentation at pixel-level as pre-processing step." ], "reasoning": "The table shows that the proposed method achieves a Line iu % of 99.42%, which is higher than the state-of-the-art method's 97.86%, thus supporting the statement.", "statement": "The proposed method achieves a Line iu % of 99.42%, outperforming the state-of-the-art method.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Results of the experiments shown in Table I with the difference that every method listed has received the ground truth of the semantic segmentation at pixel-level as input. Our proposed text-line extraction method is superior to state-of-the-art even if both methods run on the same perfect input. Moreover, in our experience, an algorithm which is not designed to take advantage of this pre-processing step will not benefit from it.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/328.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Results of the experiments shown in Table I with the difference that every method listed has received the ground truth of the semantic segmentation at pixel-level as input. Our proposed text-line extraction method is superior to state-of-the-art even if both methods run on the same perfect input. Moreover, in our experience, an algorithm which is not designed to take advantage of this pre-processing step will not benefit from it." ], "reasoning": "Swapping the semantic segmentation network with pixel-level ground-truth resulted in 100% line IU, indicating the method's errors were due to segmentation inaccuracies.", "description": "The answer is in Table II where we performed the same task, but this time we swapped our semantic segmentation network. with the pixel-level ground-truth provided along with the data. This represents the upper-bound performances, as no tool will produce a better segmentation than the ground-truth. In this scenario our method performed at 100% line IU, reinforcing our previous observation that our text-line extraction method has made the mistakes only in the presence of wrong results from the semantic segmentation step." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Results of the experiments shown in Table I with the difference that every method listed has received the ground truth of the semantic segmentation at pixel-level as input. Our proposed text-line extraction method is superior to state-of-the-art even if both methods run on the same perfect input. Moreover, in our experience, an algorithm which is not designed to take advantage of this pre-processing step will not benefit from it.'\nProblem: 'What was the Line IU percentage when the proposed method was used with pixel-level ground-truth?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/328.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Results of the experiments shown in Table I with the difference that every method listed has received the ground truth of the semantic segmentation at pixel-level as input. Our proposed text-line extraction method is superior to state-of-the-art even if both methods run on the same perfect input. Moreover, in our experience, an algorithm which is not designed to take advantage of this pre-processing step will not benefit from it." ], "reasoning": "The table shows the Line IU percentage for different methods. The proposed method achieved 100% Line IU with pixel-level ground-truth.", "question": "What was the Line IU percentage when the proposed method was used with pixel-level ground-truth?", "answer": "So, the answer is {\"answer\": \"100\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed method achieved the highest Pixel IU percentage.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/328.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Results of the experiments shown in Table I with the difference that every method listed has received the ground truth of the semantic segmentation at pixel-level as input. Our proposed text-line extraction method is superior to state-of-the-art even if both methods run on the same perfect input. Moreover, in our experience, an algorithm which is not designed to take advantage of this pre-processing step will not benefit from it." ], "reasoning": "The table shows that the 'wavelength (tight polygons)' method achieved a Pixel IU of 98.95%, which is higher than the proposed method's 97.22%.", "statement": "The proposed method achieved the highest Pixel IU percentage.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Word Embedding Comparison Across Vision Language Tasks. (a) contains the results of learning an embedding from scratch random initialization with fine-tuning during training. The remaining sections compare (b) Word2Vec, (c) FastText, and (d) sentence level embeddings InferSent and BERT. All experiments show three model variants: Average Embedding, Self-Attention, and LSTM, with and without fine-tuning during training. Average Embedding and Self-Attention are not used in generation tasks for Image Captioning and VQA as they are known to show worse performance; sentence level embeddings are not applicable for these tasks. See text for discussion.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/329.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Word Embedding Comparison Across Vision Language Tasks. (a) contains the results of learning an embedding from scratch random initialization with fine-tuning during training. The remaining sections compare (b) Word2Vec, (c) FastText, and (d) sentence level embeddings InferSent and BERT. All experiments show three model variants: Average Embedding, Self-Attention, and LSTM, with and without fine-tuning during training. Average Embedding and Self-Attention are not used in generation tasks for Image Captioning and VQA as they are known to show worse performance; sentence level embeddings are not applicable for these tasks. See text for discussion." ], "reasoning": "Using Word2Vec rather than an embedding trained from scratch tends to improve performance, especially with larger vocabularies, and fine-tuning a word embedding on a vision-language task can significantly enhance performance.", "description": "The datasets and vision-language task models are described in the appendix, but are referenced in Table 1.. Unsurprisingly, when comparing the first lines of Table 1(a,b), we find that using Word2Vec rather than an embedding trained from scratch tends to improve performance. This is more important when considering a larger vocabulary as seen comparing phrase grounding experiments on DiDeMo and ReferIt, whose embeddings trained from scratch using their smaller vocabulary compare favorably to Word2Vec.. Word2Vec only falls behind within a point or two across all tasks, and even outperforms or performs equally as well as FastText for certain tasks (e.g. text-to-clip, image captioning).. Table 1 also contains a comparison of language model variants across the five vision-language tasks we evaluate on. We see that fine-tuning a word embedding on a visionlanguage task can have dramatic effects on the performance of the language model (e.g. 5-10% increase to mean recall on image-sentence retrieval).. When comparing the architecture choices from Figure 3 we see that for retrieval-based tasks (i.e. where the output is not free-form text) the Average Embedding and SelfAttention models perform better than a simple LSTM-based approach, with Self-Attention being best on average.. The only apparent exception to this is the text-to-clip task.. InferSent and BERT reach comparable values to the best Word2Vec models for image-sentence retrieval on Flickr30K, performing more poorly for the MSCOCO dataset. For the remaining retrieval tasks, metrics are below the best performing model and embedding combination within 1-3 points, again noting the unusual exception of InferSent on phrase grounding of Flickr30K Entities, which significantly drops below scratch performance.. While all language models perform closely on ReferIt phrase grounding, this still suggests that there is no need to use the more complex LSTM language model without additional modification.. Lastly, sentence level embeddings InferSent and BERT are compared in Table 1(d); results are without fine-tuning.. The two are comparable to each other with the exception of phrase grounding accuracy on Flickr30K Entities; BERT surprisingly outperforms InferSent by 11.55%. Both InferSent and BERT do not provide the best results across any task, and thus are not a leading option for vision-language tasks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Word Embedding Comparison Across Vision Language Tasks. (a) contains the results of learning an embedding from scratch random initialization with fine-tuning during training. The remaining sections compare (b) Word2Vec, (c) FastText, and (d) sentence level embeddings InferSent and BERT. All experiments show three model variants: Average Embedding, Self-Attention, and LSTM, with and without fine-tuning during training. Average Embedding and Self-Attention are not used in generation tasks for Image Captioning and VQA as they are known to show worse performance; sentence level embeddings are not applicable for these tasks. See text for discussion.'\nProblem: 'Which method and embedding combination achieved the highest mean recall for Image-Sentence Retrieval on MSCOCO?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/329.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Word Embedding Comparison Across Vision Language Tasks. (a) contains the results of learning an embedding from scratch random initialization with fine-tuning during training. The remaining sections compare (b) Word2Vec, (c) FastText, and (d) sentence level embeddings InferSent and BERT. All experiments show three model variants: Average Embedding, Self-Attention, and LSTM, with and without fine-tuning during training. Average Embedding and Self-Attention are not used in generation tasks for Image Captioning and VQA as they are known to show worse performance; sentence level embeddings are not applicable for these tasks. See text for discussion." ], "reasoning": "To find the highest mean recall for Image-Sentence Retrieval on MSCOCO, we need to compare the mean recall values across all methods and embedding combinations. The highest value is 80.7, achieved by FastText with Average Embedding + ft.", "question": "Which method and embedding combination achieved the highest mean recall for Image-Sentence Retrieval on MSCOCO?", "answer": "So, the answer is {\"answer\": \"FastText with Average Embedding + ft\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSelf-Attention with fine-tuning outperforms LSTM with fine-tuning for Phrase Grounding on ReferIt.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/329.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Word Embedding Comparison Across Vision Language Tasks. (a) contains the results of learning an embedding from scratch random initialization with fine-tuning during training. The remaining sections compare (b) Word2Vec, (c) FastText, and (d) sentence level embeddings InferSent and BERT. All experiments show three model variants: Average Embedding, Self-Attention, and LSTM, with and without fine-tuning during training. Average Embedding and Self-Attention are not used in generation tasks for Image Captioning and VQA as they are known to show worse performance; sentence level embeddings are not applicable for these tasks. See text for discussion." ], "reasoning": "To verify this statement, we compare the accuracy values for Self-Attention + ft and LSTM + ft for Phrase Grounding on ReferIt. Self-Attention + ft has an accuracy of 53.87, while LSTM + ft has an accuracy of 53.95. Therefore, the statement is refuted.", "statement": "Self-Attention with fine-tuning outperforms LSTM with fine-tuning for Phrase Grounding on ReferIt.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Modifications of Word2Vec. (a) contains Word2Vec retrofitted results using only the WordNet (wn) lexicon from [14]. Next, (b) is our baseline embedding which includes the new Visual Genome relational graph. Visual Word2Vec results are provided in (c), and (d), (e) are Fisher vectors on top of Word2Vec. See text for discussion.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/330.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Modifications of Word2Vec. (a) contains Word2Vec retrofitted results using only the WordNet (wn) lexicon from [14]. Next, (b) is our baseline embedding which includes the new Visual Genome relational graph. Visual Word2Vec results are provided in (c), and (d), (e) are Fisher vectors on top of Word2Vec. See text for discussion." ], "reasoning": "GrOVLE shows consistent improvement across most vision-language tasks compared to other embeddings, with a smaller performance drop in image-sentence retrieval compared to HGLMM 6K-D.", "description": "We see a small, but consistent improvement across most of the vision-language tasks using GrOVLE as seen in Table 2(b). These changes result in an embedding with comparable performance to the HGLMM 6K-D features, which are reported in Table 2(e). However, our word embedding tends to perform better when embeddings are the same size (i.e. 300-D). For the generation-based tasks (i.e. captioning and VQA), the benefits of using adapted embeddings are less clear. This may simply be an artifact of the challenges in evaluating these tasks (i.e., the captions are improving in a way the metrics don't capture).. Visual Word2Vec performs comparably amongst results for generation tasks (i.e. image captioning and VQA), but these tasks have little variance in results, with less than a point of difference across the adapted embeddings.. The small gain provided in generation tasks by Visual Word2Vec does not out-weight the drops in performance across other tasks such as the significant mean recall drop of 6.3 compared to HGLMM's 6K-D Self-Attention result in line two of Table 2(c) and Table 2(e) for image-sentence retrieval of Flickr30K. For comparison, GrOVLE's Self-Attention result in Table 2(b) is only 3 points lower.. Finally, we report results using HGLMM of different dimension. HGLMM 300-D features are used for a more fair comparison to other embeddings. While the HGLMM 6K-D representation primarily results in the highest performance, it performs more poorly on generation tasks and also results in high variance. For example, column one in Table 2(e) shows a range of 7.1 in mean recall, unlike GrOVLE which has a range of 2.6." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Modifications of Word2Vec. (a) contains Word2Vec retrofitted results using only the WordNet (wn) lexicon from [14]. Next, (b) is our baseline embedding which includes the new Visual Genome relational graph. Visual Word2Vec results are provided in (c), and (d), (e) are Fisher vectors on top of Word2Vec. See text for discussion.'\nProblem: 'Which embedding has the highest mean recall for Image-Sentence Retrieval on the Flickr30K dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/330.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Modifications of Word2Vec. (a) contains Word2Vec retrofitted results using only the WordNet (wn) lexicon from [14]. Next, (b) is our baseline embedding which includes the new Visual Genome relational graph. Visual Word2Vec results are provided in (c), and (d), (e) are Fisher vectors on top of Word2Vec. See text for discussion." ], "reasoning": "To find the highest mean recall for Image-Sentence Retrieval on the Flickr30K dataset, we need to compare the mean recall values across all embeddings listed in the table. The highest value is 75.1, which corresponds to HGLMM (6K-D) with Self-Attention + ft.", "question": "Which embedding has the highest mean recall for Image-Sentence Retrieval on the Flickr30K dataset?", "answer": "So, the answer is {\"answer\": \"HGLMM (6K-D) with Self-Attention + ft\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGrOVLE has a lower mean recall for Image-Sentence Retrieval on the Flickr30K dataset compared to HGLMM (6K-D).\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/330.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Modifications of Word2Vec. (a) contains Word2Vec retrofitted results using only the WordNet (wn) lexicon from [14]. Next, (b) is our baseline embedding which includes the new Visual Genome relational graph. Visual Word2Vec results are provided in (c), and (d), (e) are Fisher vectors on top of Word2Vec. See text for discussion." ], "reasoning": "The table shows that GrOVLE's highest mean recall for Image-Sentence Retrieval on the Flickr30K dataset is 72.3, while HGLMM (6K-D) has a higher mean recall of 75.1.", "statement": "GrOVLE has a lower mean recall for Image-Sentence Retrieval on the Flickr30K dataset compared to HGLMM (6K-D).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: We include results with additional models to verify trends. See text for discussion and the appendix for more.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/331.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: We include results with additional models to verify trends. See text for discussion and the appendix for more." ], "reasoning": "The five-task pretrained GrOVLE with fine-tuning shows the best performance across all tasks, as demonstrated by the consistent improvements in Table 4.", "description": "To address this, we fine-tune GrOVLE across the five VL tasks. We provide results for a four and five multi-task trained embedding. The four task experiments are performed with the final task embedding fixed to demonstrate how well the embeddings would generalize to new tasks. We also provide results for pretraining on five tasks with and without finetuning during the last task.. This multi-task variant is the best performing across all tasks, thus we release this embedding for public use.. To verify that the multi-task GrOVLE performance improvements generalize across task model architecture, we provide results using additional task models in Table 4.. Table 4 provides more models per task and demonstrates consistent results: embeddings can significantly affect performance and GrOVLE variants are still the best embedding overall. As we move down the table we find even larger performance improvements made by using the five-task pretrained GrOVLE with fine-tuning than in Table 3." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: We include results with additional models to verify trends. See text for discussion and the appendix for more.'\nProblem: 'Which model achieved the highest accuracy in the VQA BAN task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/331.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: We include results with additional models to verify trends. See text for discussion and the appendix for more." ], "reasoning": "By examining the VQA BAN column, we see that the multi-task pretraining with target task and fine-tuning model achieved the highest accuracy of 69.97.", "question": "Which model achieved the highest accuracy in the VQA BAN task?", "answer": "Thus, the answer is {\"answer\": \"multi-task pretraining with target task and fine-tuning\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe FastText + ft model achieved the highest accuracy in the Phrase Grounding QA R-CNN task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/331.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: We include results with additional models to verify trends. See text for discussion and the appendix for more." ], "reasoning": "By examining the Phrase Grounding QA R-CNN column, we see that the FastText + ft model achieved an accuracy of 53.01, which is not the highest. The highest accuracy in this task is 54.10, achieved by the multi-task pretraining with target task and fine-tuning model.", "statement": "The FastText + ft model achieved the highest accuracy in the Phrase Grounding QA R-CNN task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison of training our word embeddings on four tasks and testing on the fifth, as well as training on all five tasks.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/332.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of training our word embeddings on four tasks and testing on the fifth, as well as training on all five tasks." ], "reasoning": "The improved embedding shows the best performance when fine-tuned for the target task, especially in image-sentence retrieval.", "description": "Table 3 reports results of the multi-task training procedure described above. We use the best performing language model in our comparisons for each task, i.e. Self-Attention for image-sentence retrieval and phrase grounding, and the LSTM language model for text-to-clip, image captioning, and VQA. The first lines of Table 3 report the results of the original fixed GrOVLE embedding, which should be considered the baseline. The second line of Table 3 reports performance when the four-task pretrained GrOVLE is fixed when used in the target task, i.e. the task currently being run. The third and fourth line of Table 3 report the results of our embedding when they were trained on all five tasks, and kept fixed or fine-tuned for the target task, respectively.. The results of line three and four demonstrate that our improved embedding tends to transfer better when applied with fine-tuning during the target task. We find similar trends in performance improvements across tasks: larger gains occur for image-sentence retrieval with +7.9 mean recall for the Flickr30K dataset and +6.3 for MSCOCO. All other tasks have performance improvements under one point, showing that while the vision-language tasks appear to transfer well without harming performance, they are leveraged most in image-sentence retrieval, with an exception of phrase grounding accuracy on ReferIt (+2.36%)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Comparison of training our word embeddings on four tasks and testing on the fifth, as well as training on all five tasks.'\nProblem: 'What is the mean recall improvement for image-sentence retrieval on the Flickr30K dataset when using multi-task pretraining with target task fine-tuning compared to the baseline?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/332.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of training our word embeddings on four tasks and testing on the fifth, as well as training on all five tasks." ], "reasoning": "The baseline mean recall for image-sentence retrieval is 64.7. With multi-task pretraining and target task fine-tuning, the mean recall is 72.6. The improvement is 72.6 - 64.7 = 7.9.", "question": "What is the mean recall improvement for image-sentence retrieval on the Flickr30K dataset when using multi-task pretraining with target task fine-tuning compared to the baseline?", "answer": "So, the answer is {\"answer\": \"7.9\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe phrase grounding accuracy improves by more than 2% when using multi-task pretraining with target task fine-tuning compared to the baseline.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/332.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of training our word embeddings on four tasks and testing on the fifth, as well as training on all five tasks." ], "reasoning": "The baseline phrase grounding accuracy is 52.15%. With multi-task pretraining and target task fine-tuning, the accuracy is 54.51%. The improvement is 54.51 - 52.15 = 2.36%, which is more than 2%.", "statement": "The phrase grounding accuracy improves by more than 2% when using multi-task pretraining with target task fine-tuning compared to the baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Consistency of the adversarial effect (or lack thereof) for different models in the loop when retraining the model on the same data again, but with a new random seed.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/333.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Consistency of the adversarial effect (or lack thereof) for different models in the loop when retraining the model on the same data again, but with a new random seed." ], "reasoning": "The reproducibility of the annotation pipeline is measured by evaluating the performance of two models with identical setups but different random initializations and data orders.", "description": "Our annotation pipeline is designed to reject any samples where the model correctly predicts the answer. How reproducible is this when retraining the same model with the same data? To measure this, we evaluate the performance of two models of identical setup for each respective architecture, which differ only in their random initialisation and data order during SGD sampling. We can thus isolate how strongly the resulting dataset depends on the particular random initialisation and order of data points used to train the model. The results of this experiment are shown in Table 4." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Consistency of the adversarial effect (or lack thereof) for different models in the loop when retraining the model on the same data again, but with a new random seed.'\nProblem: 'Which model and resource combination achieved the highest F1 score for Seed 2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/333.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Consistency of the adversarial effect (or lack thereof) for different models in the loop when retraining the model on the same data again, but with a new random seed." ], "reasoning": "To find the highest F1 score for Seed 2, compare the F1 scores for Seed 2 across all model and resource combinations. The highest F1 score for Seed 2 is 30.3, achieved by the BERT model on the DBERT-dev resource.", "question": "Which model achieved the highest F1 score for Seed 2 on DBERT-dev?", "answer": "So, the answer is {\"answer\": \"BERT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BiDAF model achieved an EM score of 5.5 on the DBiDAF-test resource for Seed 2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/333.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Consistency of the adversarial effect (or lack thereof) for different models in the loop when retraining the model on the same data again, but with a new random seed." ], "reasoning": "The table shows that the BiDAF model achieved an EM score of 5.5 on the DBiDAF-test resource for Seed 2.", "statement": "The BiDAF model achieved an EM score of 5.5 on the DBiDAF-test resource for Seed 2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Overall performance in terms of personality (C Score), quality (Perplexity, BLEU), diversity (Distinct-1), structure similarity of different users (Diff Score (×10−10)), model change after adaptation (Δ score (×10−10)) .'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/334.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Overall performance in terms of personality (C Score), quality (Perplexity, BLEU), diversity (Distinct-1), structure similarity of different users (Diff Score (×10−10)), model change after adaptation (Δ score (×10−10)) ." ], "reasoning": "Our model CMAML achieves good performance on quality, personality, and diversity as indicated by its high BLEU, C Score, and Distinct-1 values.", "description": "We present our results in Table 1. As shown in Table 1, our model CMAML achieves good performance on quality, personality, and diversity." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Overall performance in terms of personality (C Score), quality (Perplexity, BLEU), diversity (Distinct-1), structure similarity of different users (Diff Score (×10−10)), model change after adaptation (Δ score (×10−10)) .'\nProblem: 'Which model has the highest BLEU score and what is its corresponding Perplexity?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/334.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Overall performance in terms of personality (C Score), quality (Perplexity, BLEU), diversity (Distinct-1), structure similarity of different users (Diff Score (×10−10)), model change after adaptation (Δ score (×10−10)) ." ], "reasoning": "By examining the table, CMAML-CMAML has the highest BLEU score of 1.59. The corresponding Perplexity for CMAML-CMAML is 35.88.", "question": "Which model has the highest BLEU score and what is its corresponding Perplexity?", "answer": "So, the answer is {\"answer\": \"35.88\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model with the highest Distinct-1 score also has the highest Diff Score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/334.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Overall performance in terms of personality (C Score), quality (Perplexity, BLEU), diversity (Distinct-1), structure similarity of different users (Diff Score (×10−10)), model change after adaptation (Δ score (×10−10)) ." ], "reasoning": "The table shows that CMAML-CMAML has the highest Distinct-1 score of 0.0089 and also the highest Diff Score of 412.77.", "statement": "The model with the highest Distinct-1 score also has the highest Diff Score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Training models on various datasets, each with 10,000 samples, and measuring their generalisation to different evaluation datasets. Results in bold indicate the best result per model.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/335.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Training models on various datasets, each with 10,000 samples, and measuring their generalisation to different evaluation datasets. Results in bold indicate the best result per model." ], "reasoning": "Training on datasets with a model in the loop generally improves generalization to other datasets, but stronger models in the loop lead to lower test accuracy on datasets without a model in the loop.", "description": "We next conduct a series of experiments in which we train on DBiDAF, DBERT, and DRoBERTa, and observe how well models can then learn to generalise on the respective test portions of these datasets. Table 5 shows the results, and there is a multitude of observations.. First, one clear trend we observe across all training data setups is a clear negative performance progression when evaluated against datasets constructed with a stronger model in the loop. This trend holds true for all but the BiDAF model, in each of the training configurations, and for each of the evaluation datasets. For example, RoBERTa trained on DRoBERTa achieves 71.4, 53.5, 48.6 and 38.9 F1 when evaluated on DSQuAD, DBiDAF, DBERT and DRoBERTa, respectively. Second, we observe that the BiDAF model is not able to generalise well to datasets constructed with a model in the loop, independent of its training setup. In particular it is unable to learn from DBiDAF, thus failing to overcome some of its own blind spots through adversarial training. Both when training only on DBiDAF, as well as when adding DSQuAD to DBiDAF during training (cf. Table 6), BiDAF performs poorly across all the adversarial datasets.. results in Table 5, where training on DBiDAF in several cases led to better generalisation than training on DRoBERTa.. we further train each of our three models on either DBiDAF, DBERT, or DRoBERTa and test on DSQuAD, with results in the DSQuAD columns of Table 5.. First, we observe clear generalisation improvements towards DDROP across all models compared to training on DSQuAD(10K) when using any of the DBiDAF, DBERT, or DRoBERTa datasets for training. That is, including a model in the loop for the training dataset leads to improved transfer towards DDROP. Note that the DROP dataset also makes use of a BiDAF model in the loop during annotation; these results are in line with our prior observations when testing the same setups on DBiDAF, DBERT and DRoBERTa, compared to training on DSQuAD(10K). Second, we observe overall strong transfer results towards DNQ: up to 71.0F1 for a BERT model trained on DBiDAF. Note that this result is similar and even slightly improves over model training with SQuAD data of the same size. That is, relative to training on SQuAD data, training on adversarially collected data DBiDAF does not impede generalisation to the DNQ dataset, which was created without a model in the annotation loop. We then however see a similar negative performance progression as observed before when testing on DSQuAD: the stronger the model in the annotation loop of the training dataset, the lower the test accuracy on test data from a data distribution composed without using a model in the loop." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Training models on various datasets, each with 10,000 samples, and measuring their generalisation to different evaluation datasets. Results in bold indicate the best result per model.'\nProblem: 'What is the F1 score of the BERT model trained on DBiDAF when evaluated on the DNQ dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/335.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Training models on various datasets, each with 10,000 samples, and measuring their generalisation to different evaluation datasets. Results in bold indicate the best result per model." ], "reasoning": "To find the F1 score of the BERT model trained on DBiDAF when evaluated on the DNQ dataset, locate the row for BERT trained on DBiDAF and then find the F1 score in the DNQ column.", "question": "What is the F1 score of the BERT model trained on DBiDAF when evaluated on the DNQ dataset?", "answer": { "answer": "71.0" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BiDAF model trained on DBERT performs better on the DDROP dataset than on the DSQuAD dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/335.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Training models on various datasets, each with 10,000 samples, and measuring their generalisation to different evaluation datasets. Results in bold indicate the best result per model." ], "reasoning": "To verify this statement, compare the performance of the BiDAF model trained on DBERT on the DDROP and DSQuAD datasets. The F1 score for DDROP is 12.0, and for DSQuAD it is 18.8, indicating that the performance on DSQuAD is better.", "statement": "The BiDAF model trained on DBERT performs better on the DDROP dataset than on the DSQuAD dataset.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Corpus-level BLEU scores on the validation sets for the same model architecture trained on different data.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/336.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Corpus-level BLEU scores on the validation sets for the same model architecture trained on different data." ], "reasoning": "The BOTH model, which combines WEBSPLIT and WIKISPLIT, achieves the highest BLEU scores on both the WebSplit and WikiSplit validation sets, indicating improved performance over individual models.", "description": "We compare three training configurations: WEBSPLIT only, WIKISPLIT only, and BOTH, which is simply their concatenation.. The WEBSPLIT model scores 35.3 BLEU on the WebSplit validation set but fails to generalize beyond its narrow domain, as evidenced by reaching only 4.2 BLEU on the WikiSplit validation set.. In contrast, the WIKISPLIT model achieves 59.4 BLEU on the WebSplit validation set,. Reintroducing the downsampled, in-domain training data (BOTH) further improves performance on the WebSplit evaluation." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Corpus-level BLEU scores on the validation sets for the same model architecture trained on different data.'\nProblem: 'Which training configuration achieves the highest BLEU score on the WebSplit validation set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/336.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Corpus-level BLEU scores on the validation sets for the same model architecture trained on different data." ], "reasoning": "The table shows BLEU scores for different training configurations on the WebSplit validation set. The BOTH configuration achieves the highest score of 61.4.", "question": "Which training configuration achieves the highest BLEU score on the WebSplit validation set?", "answer": "So, the answer is {\"answer\": \"BOTH\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe WIKISPLIT model achieves the highest BLEU score on the WikiSplit validation set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/336.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Corpus-level BLEU scores on the validation sets for the same model architecture trained on different data." ], "reasoning": "The table shows that the WIKISPLIT model achieves a BLEU score of 76.0 on the WikiSplit validation set, while the BOTH model achieves a slightly higher score of 76.1.", "statement": "The WIKISPLIT model achieves the highest BLEU score on the WikiSplit validation set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Manual evaluation results, as counts over the simple sentences predicted by each model for a random sample of 50 inputs from WebSplit 1.0 validation set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/337.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Manual evaluation results, as counts over the simple sentences predicted by each model for a random sample of 50 inputs from WebSplit 1.0 validation set." ], "reasoning": "The BOTH model achieved the highest accuracy with 95% correct simple sentences and the lowest incidence of missed or unsupported statements, while AG18 had the lowest accuracy at 22%.", "description": "As shown in Table 6, the BOTH model produced the most accurate output (95% correct simple sentences), with the lowest incidence of missed or unsupported statements.. outputs from Aharoni and Goldberg(2018) (AG18), which were 22% accurate." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Manual evaluation results, as counts over the simple sentences predicted by each model for a random sample of 50 inputs from WebSplit 1.0 validation set.'\nProblem: 'Which model had the highest accuracy and the lowest incidence of unsupported statements?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/337.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Manual evaluation results, as counts over the simple sentences predicted by each model for a random sample of 50 inputs from WebSplit 1.0 validation set." ], "reasoning": "The BOTH model had the highest accuracy with 95% correct simple sentences and the lowest incidence of unsupported statements with only 4.", "question": "Which model had the highest accuracy and the lowest incidence of unsupported statements?", "answer": "So, the answer is {\"answer\": \"BOTH\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe WebSplit model had a higher accuracy than the WikiSplit model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/337.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Manual evaluation results, as counts over the simple sentences predicted by each model for a random sample of 50 inputs from WebSplit 1.0 validation set." ], "reasoning": "The table shows that the WebSplit model had an accuracy of 32%, while the WikiSplit model had an accuracy of 91%. Therefore, the statement is incorrect.", "statement": "The WebSplit model had a higher accuracy than the WikiSplit model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results on the WebSplit v1.0 test set when varying the training data while holding model architecture fixed: corpus-level BLEU, sentence-level BLEU (to match past work), simple sentences per complex sentence, and tokens per simple sentence (micro-average). AG18 is the previous best model by aharoni:2018, which used the full WebSplit training set, whereas we downsampled it.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/338.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on the WebSplit v1.0 test set when varying the training data while holding model architecture fixed: corpus-level BLEU, sentence-level BLEU (to match past work), simple sentences per complex sentence, and tokens per simple sentence (micro-average). AG18 is the previous best model by aharoni:2018, which used the full WebSplit training set, whereas we downsampled it." ], "reasoning": "Combining the WikiSplit dataset with the downsampled WebSplit yields a 32 point improvement in BLEU over the prior best result.", "description": "We relate our approach to prior work on Web-Split v1.0 by reporting scores on its test set in Table 5. Our best performance in BLEU is again obtained by combining the proposed WikiSplit dataset with the downsampled WebSplit, yielding a 32 point improvement over the prior best result." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Results on the WebSplit v1.0 test set when varying the training data while holding model architecture fixed: corpus-level BLEU, sentence-level BLEU (to match past work), simple sentences per complex sentence, and tokens per simple sentence (micro-average). AG18 is the previous best model by aharoni:2018, which used the full WebSplit training set, whereas we downsampled it.'\nProblem: 'What is the BLEU score when combining the WikiSplit dataset with the downsampled WebSplit?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/338.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on the WebSplit v1.0 test set when varying the training data while holding model architecture fixed: corpus-level BLEU, sentence-level BLEU (to match past work), simple sentences per complex sentence, and tokens per simple sentence (micro-average). AG18 is the previous best model by aharoni:2018, which used the full WebSplit training set, whereas we downsampled it." ], "reasoning": "The table shows that the BLEU score for 'Both' (combining WikiSplit and WebSplit) is 62.4, which is the highest score in the table.", "question": "What is the BLEU score when combining the WikiSplit dataset with the downsampled WebSplit?", "answer": { "answer": "62.4" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BLEU score for the WebSplit dataset alone is higher than the BLEU score for the Source dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/338.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on the WebSplit v1.0 test set when varying the training data while holding model architecture fixed: corpus-level BLEU, sentence-level BLEU (to match past work), simple sentences per complex sentence, and tokens per simple sentence (micro-average). AG18 is the previous best model by aharoni:2018, which used the full WebSplit training set, whereas we downsampled it." ], "reasoning": "The table shows that the BLEU score for WebSplit is 34.2, while the BLEU score for Source is 58.7. Therefore, the statement is refuted.", "statement": "The BLEU score for the WebSplit dataset alone is higher than the BLEU score for the Source dataset.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Quality results for local embeddings.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/339.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Quality results for local embeddings." ], "reasoning": "EmbDI generally outperforms the baseline in local embeddings, except for MA where it is beaten once. Increasing row permutations in Basic improves MR but decreases MA without benefiting MC.", "description": "We report the quality results in Table 2, where each number represents the fraction of tests passed. While on average the local embeddings for EmbDI are largely superior to the baseline, our solution is beaten once for MA. By increasing the percentage of row permutations in Basic, results for MR improve but decrease for MA, without significant benefit for MC." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Quality results for local embeddings.'\nProblem: 'Which method and metric combination has the highest average score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/339.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Quality results for local embeddings." ], "reasoning": "To find the highest average score, compare the 'Avg' column for both Basic and EmbDI methods. EmbDI walks Avg has the highest score of 0.75.", "question": "Which method and metric combination has the highest average score?", "answer": { "answer": "EmbDI walks Avg" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIncreasing the percentage of row permutations in Basic improves the results for MR.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/339.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Quality results for local embeddings." ], "reasoning": "The description states that increasing the percentage of row permutations in Basic improves MR, which is supported by the table showing Basic: no walks MR score of 0.54.", "statement": "Increasing the percentage of row permutations in Basic improves the results for MR.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Quality results for SM.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/340.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Quality results for SM." ], "reasoning": "EmbDI local embeddings consistently outperform the baseline in F-measure across all scenarios, with RefS achieving the highest scores due to dataset overlap.", "description": "We test an unsupervised setting using (1) the algorithm proposed in Section 4.4 with EmbDI local embeddings, and (2) an existing matching system with both pre-trained embeddings (SeepP ) and our local embeddings (SeepL). Pre-trained embeddings for tokens and tuples have been obtained from Glove .. Table 3 reports the results w.r.t. manually defined attribute matches. The simple unsupervised method with EmbDI local embeddings outperforms the baseline in terms of Fmeasure in all scenarios. Results of RefS are the best because of the high overlap between its datasets. The baseline improves when it is executed with EmbDI local embeddings, showing their superior quality w.r.t. pre-trained ones. The Basic local embeddings lead to 0 attribute matches.. We also observe that results for SeepPreTrain depend on the quality of the original attribute labels. If we replace the original (expressive and correct) labels with synthetic ones, Seep-PreTrain obtains F-measure values between .30 and .38. Local embeddings from EmbDI do not depend on the presence of the attribute labels.. Similarly, decreasing the size of the walks to 5 for the SM task raises the F-measure for RefL to .92 (from .77)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Quality results for SM.'\nProblem: 'Which method achieved the highest F-measure score for the RefL scenario?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/340.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Quality results for SM." ], "reasoning": "The table shows the F-measure scores for different methods across various scenarios. For the RefL scenario, the highest F-measure score is 0.88, achieved by SeepL.", "question": "Which method achieved the highest F-measure score for the RefL scenario?", "answer": { "answer": "SeepL" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nEmbDI local embeddings achieved an F-measure of 0.80 for the Movie scenario.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/340.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Quality results for SM." ], "reasoning": "The table shows that EmbDI local embeddings achieved an F-measure of 0.80 for the Movie scenario, which supports the statement.", "statement": "EmbDI local embeddings achieved an F-measure of 0.80 for the Movie scenario.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: F-Measure results for ER.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/341.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F-Measure results for ER." ], "reasoning": "EmbDI embeddings consistently outperform other embeddings in both unsupervised and supervised settings, with local embeddings showing significant improvements in quality.", "description": "As baseline, we use our unsupervised algorithm with EmbDI embeddings and pre-trained embeddings. We also test our local embeddings in the supervised setting with a state of the art ER system (DeepERL), comparing its results to the ones obtained with pre-trained embeddings (DeepERP ).. Results in Table 4 show that EmbDI embeddings obtain better quality results in all scenarios in both settings. As observed in the SM experiments, using local embeddings instead of pre-trained ones increases significantly the quality of an existing system. In this case, supervised DeepER shows an average 6% absolute improvement in F-measure in the tested setting with 5% of the ground truth passed as training data. The improvements decreases to 4% with more training data (10%). Also for ER, the local embeddings obtained with the basic method lead to 0 row matched.. Execut" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: F-Measure results for ER.'\nProblem: 'What is the F-measure improvement of supervised DeepER L over supervised DeepER P for the Movie dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/341.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F-Measure results for ER." ], "reasoning": "The F-measure for supervised DeepER P on the Movie dataset is 0.82, while for supervised DeepER L it is 0.88. The improvement is calculated as 0.88 - 0.82 = 0.06, which is a 6% improvement.", "question": "What is the F-measure improvement of supervised DeepER L over supervised DeepER P for the Movie dataset?", "answer": "So, the answer is {\"answer\": \"6%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nEmbDI embeddings achieve the highest F-measure in the unsupervised setting for the RefS dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/341.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F-Measure results for ER." ], "reasoning": "The table shows that for the RefS dataset, the F-measure for EmbDI embeddings in the unsupervised setting is 0.95, which is higher than the other unsupervised methods (Basic: 0, Glove: 0.84).", "statement": "EmbDI embeddings achieve the highest F-measure in the unsupervised setting for the RefS dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Training models on SQuAD, as well as SQuAD combined with different adversarially created datasets. Results in bold indicate the best result per model.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/342.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Training models on SQuAD, as well as SQuAD combined with different adversarially created datasets. Results in bold indicate the best result per model." ], "reasoning": "RoBERTa achieves the highest scores on DBiDAF, DBERT, and DRoBERTa evaluation sets, especially when trained on DSQuAD+DRoBERTa, indicating improved generalization.", "description": "For example, RoBERTa trained on DRoBERTa reaches 38.9F1 on DRoBERTa, and this number further increases to 47.2F1 when including SQuAD during training (cf. Table 6).. In Table 6 we show experimental results for the same models and training datasets, but now including SQuAD as additional training data. In this training setup we generally see improved generalisation to DBiDAF, DBERT, and DRoBERTa. Interestingly, the relative differences between DBiDAF, DBERT, and DRoBERTa as training set used in conjunction with SQuAD are now much diminished, and especially DRoBERTa as (part of the) training set now generalises substantially better. RoBERTa achieves the strongest results on any of the DBiDAF, DBERT, and DRoBERTa evaluation sets, in particular when trained on DSQuAD+DRoBERTa.. we identify a risk of datasets constructed with weaker models in the loop becoming outdated. For example, RoBERTa achieves 58.2EM/73.2F1 on DBiDAF, in contrast to 0.0EM/5.5F1 for BiDAF – which is not far from non-expert human performance of 62.6EM/78.5F1.. We furthermore observe a gradual decrease in generalisation to SQuAD when training on DBiDAF towards training on DRoBERTa. This suggests that the stronger the model used in the annotation loop, the more dissimilar the data distribution becomes from the original SQuAD distribution. We will later find further support for this explanation in a qualitative analysis (Section 5). It may however also be due to a limitation of BERT and RoBERTa – similar to BiDAF – in learning from a data distribution designed to beat these models; an even stronger model might learn more e.g. from DRoBERTa." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Training models on SQuAD, as well as SQuAD combined with different adversarially created datasets. Results in bold indicate the best result per model.'\nProblem: 'What is the F1 score of RoBERTa when trained on DSQuAD+DRoBERTa and evaluated on DRoBERTa?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/342.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Training models on SQuAD, as well as SQuAD combined with different adversarially created datasets. Results in bold indicate the best result per model." ], "reasoning": "To find the F1 score of RoBERTa when trained on DSQuAD+DRoBERTa and evaluated on DRoBERTa, locate the row for RoBERTa with the training dataset DSQuAD+DRoBERTa and find the F1 score under the evaluation dataset DRoBERTa.", "question": "What is the F1 score of RoBERTa when trained on DSQuAD+DRoBERTa and evaluated on DRoBERTa?", "answer": "So, the answer is {\"answer\": \"47.2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRoBERTa achieves the highest F1 score on DBiDAF when trained on DSQuAD+DBiDAF.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/342.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Training models on SQuAD, as well as SQuAD combined with different adversarially created datasets. Results in bold indicate the best result per model." ], "reasoning": "To verify this statement, compare the F1 scores of RoBERTa on DBiDAF when trained on different datasets. The highest F1 score for RoBERTa on DBiDAF is 71.2 when trained on DSQuAD+DBiDAF.", "statement": "RoBERTa achieves the highest F1 score on DBiDAF when trained on DSQuAD+DBiDAF.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Results of the automatic evaluation procedure on a random sample of 1000 sentences.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/343.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of the automatic evaluation procedure on a random sample of 1000 sentences." ], "reasoning": "The proposed sentence splitting corpus, MinWikiSplit, contains on average four simplified target sentences per complex source sentence, with each target proposition consisting of 12 tokens, and no input is simply copied to the output.", "description": "The results are provided in Table 4.. The scores demonstrate that on average our proposed sentence splitting corpus contains four simplified target sentences per complex source sentence, with every target proposition consisting of 12 tokens. Moreover, no input is simply copied to the output, but split into smaller components." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Results of the automatic evaluation procedure on a random sample of 1000 sentences.'\nProblem: 'What is the average number of simplified target sentences per complex source sentence in the MinWikiSplit corpus?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/343.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of the automatic evaluation procedure on a random sample of 1000 sentences." ], "reasoning": "The description states that the proposed sentence splitting corpus contains four simplified target sentences per complex source sentence. The table shows that MinWikiSplit has an average of 3.84 simplified target sentences per complex source sentence.", "question": "What is the average number of simplified target sentences per complex source sentence in the MinWikiSplit corpus?", "answer": "So, the answer is {\"answer\": \"3.84\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe MinWikiSplit corpus has a 100% same input-output ratio.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/343.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of the automatic evaluation procedure on a random sample of 1000 sentences." ], "reasoning": "The description mentions that no input is simply copied to the output in the MinWikiSplit corpus. The table shows that the % SAME for MinWikiSplit is 0.00, indicating no input-output ratio.", "statement": "The MinWikiSplit corpus has a 100% same input-output ratio.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Averaged human evaluation ratings on a random sample of 300 sentences from MinWikiSplit. Grammaticality (G), meaning preservation (M) and structural simplicity (S) are measured using a 1 (very bad) to 5 (very good) scale.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/344.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Averaged human evaluation ratings on a random sample of 300 sentences from MinWikiSplit. Grammaticality (G), meaning preservation (M) and structural simplicity (S) are measured using a 1 (very bad) to 5 (very good) scale." ], "reasoning": "The human evaluation results show high scores in grammatical soundness, meaning preservation, and structural simplicity.", "description": "The results of the human evaluation are displayed in Table 6.. These scores show that we succeed in producing output sequences that reach a high level of grammatical soundness and almost always perfectly preserve the original meaning of the input. The third dimension under consideration, structural simplicity, which captures the degree of minimality in the simplified sentences, scores high values, too." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Averaged human evaluation ratings on a random sample of 300 sentences from MinWikiSplit. Grammaticality (G), meaning preservation (M) and structural simplicity (S) are measured using a 1 (very bad) to 5 (very good) scale.'\nProblem: 'What is the score for structural simplicity in the human evaluation results?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/344.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Averaged human evaluation ratings on a random sample of 300 sentences from MinWikiSplit. Grammaticality (G), meaning preservation (M) and structural simplicity (S) are measured using a 1 (very bad) to 5 (very good) scale." ], "reasoning": "The table shows the scores for grammatical soundness (G), meaning preservation (M), and structural simplicity (S). The score for structural simplicity (S) is 3.43.", "question": "What is the score for structural simplicity in the human evaluation results?", "answer": { "answer": "3.43" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe score for grammatical soundness is higher than the score for meaning preservation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/344.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Averaged human evaluation ratings on a random sample of 300 sentences from MinWikiSplit. Grammaticality (G), meaning preservation (M) and structural simplicity (S) are measured using a 1 (very bad) to 5 (very good) scale." ], "reasoning": "The table shows that the score for grammatical soundness (G) is 4.36, and the score for meaning preservation (M) is 4.10. Since 4.36 is higher than 4.10, the statement is supported.", "statement": "The score for grammatical soundness is higher than the score for meaning preservation.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparative table of some of the available hate speech and abusive language corpora in terms of labels and sizes.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/345.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparative table of some of the available hate speech and abusive language corpora in terms of labels and sizes." ], "reasoning": "The table compares various datasets used for labeling derogatory tweets, detailing the number of tweets, labels used, and the number of annotators per tweet.", "description": "We use Amazon Mechanical Turk to label around 13,000 potentially derogatory tweets. Table 1 compares different labelsets that exist in the literature. For instance, Waseem and Hovy (2016) use racist, sexist, and normal as labels; Davidson et al. (2017) label their data as hateful, offensive (but not hateful), and neither, while ElSherief et al. (2018) present an English dataset that records the target category based on which hate speech discriminates against people, such as ethnicity, gender, or sexual orientation and ask human annotators to classify the tweets as hate and non hate. Founta et al. (2018) label their data as offensive, abusive, hateful, aggressive, cyberbullying, spam, and normal." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparative table of some of the available hate speech and abusive language corpora in terms of labels and sizes.'\nProblem: 'Which dataset uses the most annotators per tweet?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/345.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparative table of some of the available hate speech and abusive language corpora in terms of labels and sizes." ], "reasoning": "By examining the 'Annotators/Tweet' column, the dataset by FountaDCLBSVSK18 uses the most annotators per tweet, ranging from 5 to 20.", "question": "Which dataset uses the most annotators per tweet?", "answer": { "answer": "FountaDCLBSVSK18" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe dataset by DavidsonWMW17 uses more than 3 annotators per tweet.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/345.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparative table of some of the available hate speech and abusive language corpora in terms of labels and sizes." ], "reasoning": "The table shows that the dataset by DavidsonWMW17 uses 3 or more annotators per tweet, which supports the statement.", "statement": "The dataset by DavidsonWMW17 uses more than 3 annotators per tweet.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Development set sentence retrieval performance. * We calculated the scores using the official code, and for ** we used the F1 formula to calculate the score.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/346.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Development set sentence retrieval performance. * We calculated the scores using the official code, and for ** we used the F1 formula to calculate the score." ], "reasoning": "Both pointwise and pairwise BERT sentence retrieval methods improve recall, and applying a threshold to the pointwise method achieves the best precision and F1 scores.", "description": "Table 1 compares the development set performance of different variants of the proposed sentence retrieval method with the state of the art results on the FEVER dataset. The results indicate that both pointwise and pairwise BERT sentence retrieval improve the recall. The UNC and DREAM precision scores are better than our methods without a decision threshold, however, a threshold can regulate the trade-off between the recall and precision, and achieve the best precision and F1 scores." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Development set sentence retrieval performance. * We calculated the scores using the official code, and for ** we used the F1 formula to calculate the score.'\nProblem: 'Which model achieves the highest F1 score and what is its precision?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/346.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Development set sentence retrieval performance. * We calculated the scores using the official code, and for ** we used the F1 formula to calculate the score." ], "reasoning": "The table shows that the 'Pointwise + Threshold' model achieves the highest F1 score of 53.25%. The precision for this model is 38.18%.", "question": "Which model achieves the highest F1 score?", "answer": "So, the answer is {\"answer\": \"Pointwise + Threshold\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Pointwise + Threshold model has the highest recall.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/346.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Development set sentence retrieval performance. * We calculated the scores using the official code, and for ** we used the F1 formula to calculate the score." ], "reasoning": "The table indicates that the 'Pointwise + Threshold' model has a recall of 88.00%, which is not the highest recall. The 'Pairwise Ranknet + HNM' model has the highest recall of 88.32%.", "statement": "The Pointwise + Threshold model has the highest recall.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Full evaluation scores of the only binary classification task where the single task single language model consistently outperforms multilingual multitask models.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/347.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Full evaluation scores of the only binary classification task where the single task single language model consistently outperforms multilingual multitask models." ], "reasoning": "STSL performs the best among all models on directness classification, showing high consistency in both micro and macro-F1 scores, particularly excelling in English.", "description": "single task single language (STSL), single task multilingual (STML), and multitask multilingual models (MTML) on our dataset.. STSL performs the best among all models on the directness classification, and it is also consistent in both micro and macro-F1 scores.. due to the fact that the directness has only two labels and multilabeling is not allowed in this task. Tasks involving imbalanced data, multiclass and multilabel annotations harm the performance of the directness in multitask settings.. Since macro-F1 is the average of all F1 scores of individual labels, all deep learning models have high macro-F1 scores in English which indicates that they are particularly good at classifying the direct class. STSL is also comparable or better than traditional BOW feature-based classifiers when performed on other tasks in terms of microF1 and for most of the macro-F1 scores. This shows the power of the deep learning approach.. Except for the directness, MTSL usually outperforms STSL or is comparable to it.. MTML settings do not lead to a big improvement which may be due to the class imbalance, multilabel tasks, and the difference in the nature of the tasks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Full evaluation scores of the only binary classification task where the single task single language model consistently outperforms multilingual multitask models.'\nProblem: 'Which model has the highest Macro-F1 Avg score for directness classification?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/347.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Full evaluation scores of the only binary classification task where the single task single language model consistently outperforms multilingual multitask models." ], "reasoning": "By examining the Macro-F1 Avg column, we see that STSL has the highest score of 0.86.", "question": "Which model has the highest Macro-F1 Avg score for directness classification?", "answer": { "answer": "STSL" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSTSL has the highest Micro-F1 Avg score for directness classification.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/347.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Full evaluation scores of the only binary classification task where the single task single language model consistently outperforms multilingual multitask models." ], "reasoning": "The table shows that STSL has a Micro-F1 Avg score of 0.76, which is the highest among all models.", "statement": "STSL has the highest Micro-F1 Avg score for directness classification.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Full evaluation of tasks where multilingual and multitask models outperform on average single task single language model on four different tasks.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/348.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Full evaluation of tasks where multilingual and multitask models outperform on average single task single language model on four different tasks." ], "reasoning": "Multilingual training improves the target group classification task in all languages due to the aggregation of annotated data, despite the overall performance decrease in other tasks.", "description": "When we jointly train each task on the three languages, the performance decreases in most cases, other than the target group classification tasks.. Yet, multilingual training of the target group classification task improves in all languages. Since the target group classification task involves 16 labels, the amount of data annotated for each label is lower than in other tasks. Hence, when aggregating annotated data in different languages, the size of the training data also increases, due to the relative regularity of identification words of different groups in all three languages in comparison to other tasks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Full evaluation of tasks where multilingual and multitask models outperform on average single task single language model on four different tasks.'\nProblem: 'Which model shows the highest Micro-F1 score for the Target Group attribute in French?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/348.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Full evaluation of tasks where multilingual and multitask models outperform on average single task single language model on four different tasks." ], "reasoning": "To find the highest Micro-F1 score for the Target Group attribute in French, look at the 'Target Group' rows and compare the 'Micro-F1 FR' scores. The highest score is 0.72, achieved by the MTML model.", "question": "Which model shows the highest Micro-F1 score for the Target Group attribute in French?", "answer": "So, the answer is {\"answer\": \"MTML\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe MTML model has the highest Macro-F1 average score for the Target Attribute task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/348.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Full evaluation of tasks where multilingual and multitask models outperform on average single task single language model on four different tasks." ], "reasoning": "To verify this statement, compare the 'Macro-F1 Avg' scores for the Target Attribute task across all models. The MTML model has a score of 0.28, which is not the highest. The highest score is 0.42 by the MTSL model.", "statement": "The MTML model has the highest Macro-F1 average score for the Target Attribute task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison of Exact Match and F1-score of multilingual BERT and the baseline on French and Japanese SQuAD. F1 and EM are the two official metrics of the SQuAD benchmark. EM measures the percentage of predictions that match exactly the ground-truth location of the answer. F1 measures the average overlap between the prediction and ground truth answer.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/349.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of Exact Match and F1-score of multilingual BERT and the baseline on French and Japanese SQuAD. F1 and EM are the two official metrics of the SQuAD benchmark. EM measures the percentage of predictions that match exactly the ground-truth location of the answer. F1 measures the average overlap between the prediction and ground truth answer." ], "reasoning": "Multilingual BERT significantly outperforms the baseline in both French and Japanese question answering tasks, demonstrating high zero-shot transfer ability.", "description": "A sample of the SQuAD v1.1 test set (only the first paragraph of each of the 48 Wikipedia pages) has been translated by humans in French and Japanese. We here evaluate the performance of the fine-tuned multilingual BERT on them and compare the results to a baseline .. Table 1 displays the Exact Match (EM) and F1-score of the baseline and multilingual BERT on the selected datasets. We can observe that multilingual BERT is able to significantly outperform the baseline on both the Japanese and the French question answering task.. was already noted in the public benchmarks and we add here that BERT has a high ability for QA zero-shot transfer. It is even able to significantly outperform the baseline" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison of Exact Match and F1-score of multilingual BERT and the baseline on French and Japanese SQuAD. F1 and EM are the two official metrics of the SQuAD benchmark. EM measures the percentage of predictions that match exactly the ground-truth location of the answer. F1 measures the average overlap between the prediction and ground truth answer.'\nProblem: 'What is the Exact Match (EM) score of Multilingual BERT on the Japanese dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/349.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of Exact Match and F1-score of multilingual BERT and the baseline on French and Japanese SQuAD. F1 and EM are the two official metrics of the SQuAD benchmark. EM measures the percentage of predictions that match exactly the ground-truth location of the answer. F1 measures the average overlap between the prediction and ground truth answer." ], "reasoning": "The table shows the Exact Match (EM) scores for both the baseline and Multilingual BERT on the Japanese dataset. Multilingual BERT has an EM score of 59.94.", "question": "What is the Exact Match (EM) score of Multilingual BERT on the Japanese dataset?", "answer": { "answer": "59.94" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nMultilingual BERT has a higher F1-score than the baseline on the French dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/349.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of Exact Match and F1-score of multilingual BERT and the baseline on French and Japanese SQuAD. F1 and EM are the two official metrics of the SQuAD benchmark. EM measures the percentage of predictions that match exactly the ground-truth location of the answer. F1 measures the average overlap between the prediction and ground truth answer." ], "reasoning": "The table shows that Multilingual BERT has a French F1-score of 76.65, which is higher than the baseline's French F1-score of 61.88.", "statement": "Multilingual BERT has a higher F1-score than the baseline on the French dataset.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Exact Match and F1-score of multilingual BERT on each of the cross-lingual SQuAD datasets. The row language is the one of the paragraph and the column language is the one of the question. The figures in bold are the best exact match, for each language, among the datasets where they occur.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/350.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Exact Match and F1-score of multilingual BERT on each of the cross-lingual SQuAD datasets. The row language is the one of the paragraph and the column language is the one of the question. The figures in bold are the best exact match, for each language, among the datasets where they occur." ], "reasoning": "The performance of BERT is best for the En-En dataset, with cross-lingual sets performing close to monolingual results. The exact match on En-Fr is higher than Fr-Fr, and Jap-Jap performs better than Jap-En.", "description": "To run cross-lingual tests, we build six additional datasets from the existing ones by mixing context in one language with question in another language. The mixed datasets will be made available online. in a github repository. The performance of BERT on all datasets is displayed in Table 2.. the performance is the best for the En-En dataset. The performance on Fr-Fr and Jap-Jap is also very good as noted in the first experiment. We additionally note here that results on cross-lingual sets are close to monolingual results: either as good, or slightly worse or slightly better. For instance, the exact match on the En-Fr dataset is higher than the exact match on the Fr-Fr dataset. We also observe that, in general, the exact match and F1-score are close together when the context is in Japanese whereas there is generally a larger gap for the other two languages.. the performance on Jap-En is lower than on Jap-Jap whereas the performance on En-Fr is higher than on Fr-Fr,. Results for the Jap-Jap dataset are better than results for the Jap-En dataset" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Exact Match and F1-score of multilingual BERT on each of the cross-lingual SQuAD datasets. The row language is the one of the paragraph and the column language is the one of the question. The figures in bold are the best exact match, for each language, among the datasets where they occur.'\nProblem: 'Which dataset has the highest exact match score when the context is in Japanese?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/350.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Exact Match and F1-score of multilingual BERT on each of the cross-lingual SQuAD datasets. The row language is the one of the paragraph and the column language is the one of the question. The figures in bold are the best exact match, for each language, among the datasets where they occur." ], "reasoning": "From the table, the exact match (EM) scores for the context in Japanese are 57.49 for En, 45.26 for Fr, and 59.93 for Jap. The highest exact match score is 59.93 for Jap-Jap.", "question": "What is the highest exact match score when the context is in Japanese?", "answer": "So, the answer is {\"answer\": \"59.93\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe exact match score for the En-Fr dataset is higher than the Fr-Fr dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/350.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Exact Match and F1-score of multilingual BERT on each of the cross-lingual SQuAD datasets. The row language is the one of the paragraph and the column language is the one of the question. The figures in bold are the best exact match, for each language, among the datasets where they occur." ], "reasoning": "From the table, the exact match (EM) score for En-Fr is 67.28, while for Fr-Fr it is 67.28. Since 67.28 is equal to 67.28, the statement is refuted.", "statement": "The exact match score for the En-Fr dataset is higher than the Fr-Fr dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Development set verification scores.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/351.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Development set verification scores." ], "reasoning": "The BERT model trained on the best retrieval systems, such as BERT (Large) & BERT (Pointwise), significantly improves both the FEVER score and label accuracy.", "description": "In Table 2, we compare the development set results of the state of the art methods with the BERT model trained on different retrieved evidence sets. The BERT claim verification system even if it is trained on the UKP-Athene sentence retrieval component (Hanselowski et al., 2018), the state of the art method with the highest recall, improves both label accuracy and FEVER score. Training based on the BERT sentence retrieval predictions significantly enhances the verification results because while it explicitly improves the FEVER score by providing more correct evidence sentences, it provides a better training set for the verification system. The large BERTs are only trained on the best retrieval systems, and as expected significantly improve the performance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Development set verification scores.'\nProblem: 'Which model achieved the highest FEVER score and what was the corresponding label accuracy?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/351.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Development set verification scores." ], "reasoning": "The table shows that the model BERT (Large) & BERT (Pointwise) achieved the highest FEVER score of 72.42%, and the corresponding label accuracy was 74.58%.", "question": "Which model achieved the highest FEVER score?", "answer": "So, the answer is {\"answer\": \"BERT (Large) & BERT (Pointwise)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BERT (Large) & BERT (Pointwise) model achieved a FEVER score of 72.42%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/351.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Development set verification scores." ], "reasoning": "The table indicates that the BERT (Large) & BERT (Pointwise) model indeed achieved a FEVER score of 72.42%.", "statement": "The BERT (Large) & BERT (Pointwise) model achieved a FEVER score of 72.42%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Doc-level BLEU scores on the DGT valid and test sets of our submitted models in all tracks.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/352.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Doc-level BLEU scores on the DGT valid and test sets of our submitted models in all tracks." ], "reasoning": "The MT+NLG models do not outperform the pure MT models in unconstrained mode according to their BLEU scores on DGT-valid.", "description": "For each track, we selected the best models according to their BLEU score on DGT-valid. The scores are shown in Table 3,. We see that in the same data conditions (unconstrained mode), the MT+NLG models are not better than the pure MT models." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Doc-level BLEU scores on the DGT valid and test sets of our submitted models in all tracks.'\nProblem: 'What is the BLEU score on DGT-valid for the best unconstrained MT model targeting EN?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/352.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Doc-level BLEU scores on the DGT valid and test sets of our submitted models in all tracks." ], "reasoning": "To find the BLEU score on DGT-valid for the best unconstrained MT model targeting EN, look for the row where Track is MT, Target is EN, and Constrained is no. The Valid score in this row is 64.2.", "question": "What is the BLEU score on DGT-valid for the best unconstrained MT model targeting EN?", "answer": "So, the answer is {\"answer\": \"64.2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BLEU score on DGT-valid for the best constrained MT+NLG model targeting DE is higher than the best constrained MT model targeting DE.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/352.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Doc-level BLEU scores on the DGT valid and test sets of our submitted models in all tracks." ], "reasoning": "To verify this statement, compare the Valid scores for the constrained MT+NLG and MT models targeting DE. The Valid score for the MT+NLG model is 49.4, and for the MT model, it is 49.8. Since 49.4 is not higher than 49.8, the statement is refuted.", "statement": "The BLEU score on DGT-valid for the best constrained MT+NLG model targeting DE is higher than the best constrained MT model targeting DE.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: English NLG comparison against state-of-the-art on Rotowire-test. BLEU of submitted NLG (EN) model, averaged over 3 runs. Because Rotowire tokenization is slightly different, we apply a set of fixes to the model outputs (e.g., 1-of-3 → 1 - of - 3).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/353.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: English NLG comparison against state-of-the-art on Rotowire-test. BLEU of submitted NLG (EN) model, averaged over 3 runs. Because Rotowire tokenization is slightly different, we apply a set of fixes to the model outputs (e.g., 1-of-3 → 1 - of - 3)." ], "reasoning": "Our English NLG model achieved a 5.7 BLEU score improvement on the Rotowire-test compared to the previous state-of-the-art model.", "description": "Table 6 shows a 5.7 BLEU improvement on Rotowire-test by our English NLG model compared to the previous state of the art." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: English NLG comparison against state-of-the-art on Rotowire-test. BLEU of submitted NLG (EN) model, averaged over 3 runs. Because Rotowire tokenization is slightly different, we apply a set of fixes to the model outputs (e.g., 1-of-3 → 1 - of - 3).'\nProblem: 'What is the BLEU score of the previous state-of-the-art model on the Rotowire-test?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/353.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: English NLG comparison against state-of-the-art on Rotowire-test. BLEU of submitted NLG (EN) model, averaged over 3 runs. Because Rotowire tokenization is slightly different, we apply a set of fixes to the model outputs (e.g., 1-of-3 → 1 - of - 3)." ], "reasoning": "The table shows the BLEU scores of different models on the Rotowire-test. The previous state-of-the-art model is Puduppully et al. (2019) with a BLEU score of 16.5.", "question": "What is the BLEU score of the previous state-of-the-art model on the Rotowire-test?", "answer": "So, the answer is {\"answer\": \"16.5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BLEU score of Wiseman et al. (2017) on the Rotowire-test is 14.5.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/353.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: English NLG comparison against state-of-the-art on Rotowire-test. BLEU of submitted NLG (EN) model, averaged over 3 runs. Because Rotowire tokenization is slightly different, we apply a set of fixes to the model outputs (e.g., 1-of-3 → 1 - of - 3)." ], "reasoning": "The table lists the BLEU scores of different models on the Rotowire-test. Wiseman et al. (2017) has a BLEU score of 14.5.", "statement": "The BLEU score of Wiseman et al. (2017) on the Rotowire-test is 14.5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: English NLG ablation study, starting from a 3 best player baseline (the submitted NLG model has 4 players). BLEU averages over 3 runs. Standard deviation ranges between 0.1 and 0.4.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/354.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: English NLG ablation study, starting from a 3 best player baseline (the submitted NLG model has 4 players). BLEU averages over 3 runs. Standard deviation ranges between 0.1 and 0.4." ], "reasoning": "Removing team-level aggregated scores, player position, or week day does not significantly affect BLEU scores, but information about next games is useful.", "description": "From Table 7, we see that sorting players helps, but only slightly. Using only team-level information, and no information about players gives worse but still decent BLEU scores.. Week day, player position or team-level aggregated scores can be removed without hurting BLEU. However, information about next games seems useful. Interestingly, relying on position only and removing most tags (e.g., , ) seems to be fine. In this case, we also print all-zero stats, for the position of each statistic to be consistent across players and games." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: English NLG ablation study, starting from a 3 best player baseline (the submitted NLG model has 4 players). BLEU averages over 3 runs. Standard deviation ranges between 0.1 and 0.4.'\nProblem: 'What is the BLEU score for the model when no player information is used?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/354.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: English NLG ablation study, starting from a 3 best player baseline (the submitted NLG model has 4 players). BLEU averages over 3 runs. Standard deviation ranges between 0.1 and 0.4." ], "reasoning": "The table shows that the BLEU score for the 'No player' model is 20.1 for the Valid set and 18.8 for the Test set.", "question": "What is the BLEU score for the model when no player information is used?", "answer": "So, the answer is {\"answer\": \"20.1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRemoving information about the next game results in the lowest BLEU score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/354.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: English NLG ablation study, starting from a 3 best player baseline (the submitted NLG model has 4 players). BLEU averages over 3 runs. Standard deviation ranges between 0.1 and 0.4." ], "reasoning": "The table shows that the BLEU score for the 'No next game' model is 22.0 for the Valid set and 19.9 for the Test set, which is not the lowest score. The lowest score is for the 'No player' model with 20.1 for Valid and 18.8 for Test.", "statement": "Removing information about the next game results in the lowest BLEU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: F1 score on the development set for low-resource training setups (none, tiny 5k or small 10k labeled Danish sentences). Transfer via multilingual embeddings from Medium (3.2k sentences, 51k tokens) or Large English source data (14k sentences/203k tokens).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/355.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: F1 score on the development set for low-resource training setups (none, tiny 5k or small 10k labeled Danish sentences). Transfer via multilingual embeddings from Medium (3.2k sentences, 51k tokens) or Large English source data (14k sentences/203k tokens)." ], "reasoning": "Cross-lingual transfer in neural NER outperforms traditional HMM-based tagging and benefits from unsupervised word embedding initialization, with few-shot learning greatly improving over zero-shot learning.", "description": "Cross-lingual transfer is powerful (RQ1). Zero-shot learning reaches an F1 score of 58% in the MEDIUM setup, which outperforms training the neural tagger on very limited gold data (plain).. Neural NER is better than traditional HMM-based tagging (TnT) (Brants, 2000) and greatly improves by unsupervised word embedding initialization (+Poly). It is noteworthy that zero-shot transfer benefits only to a limiting degree from more source data (F1 increases by almost 3% when training on all English CoNLL data).. To compare cross-lingual transfer to limited gold data (RQ2), we observe that training the neural system on the small amount of data together with Polyglot embeddings is close to the tiny-shot transfer setup.. Few-shot learning greatly improves over zero-shot learning. The most beneficial way is to add the target data to the source, in comparison to fine-tuning.. In both MEDIUM and LARGE setups are further gains obtained by adding TINY or SMALL amounts of Danish gold data. Interestingly, a) finetuning is less effective; b) it is better to transfer from a medium-sized setup than from the entire CoNLL source data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: F1 score on the development set for low-resource training setups (none, tiny 5k or small 10k labeled Danish sentences). Transfer via multilingual embeddings from Medium (3.2k sentences, 51k tokens) or Large English source data (14k sentences/203k tokens).'\nProblem: 'What is the F1 score for neural transfer with Medium source data in the Small setup?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/355.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: F1 score on the development set for low-resource training setups (none, tiny 5k or small 10k labeled Danish sentences). Transfer via multilingual embeddings from Medium (3.2k sentences, 51k tokens) or Large English source data (14k sentences/203k tokens)." ], "reasoning": "To find the F1 score for neural transfer with Medium source data in the Small setup, locate the 'neural transfer +Medium src' column and the 'Small' row in the table.", "question": "What is the F1 score for neural transfer with Medium source data in the Small setup?", "answer": { "answer": "70.82" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nNeural transfer with Large source data in the Tiny setup has a higher F1 score than neural transfer with Medium source data in the Small setup.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/355.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: F1 score on the development set for low-resource training setups (none, tiny 5k or small 10k labeled Danish sentences). Transfer via multilingual embeddings from Medium (3.2k sentences, 51k tokens) or Large English source data (14k sentences/203k tokens)." ], "reasoning": "Compare the F1 scores for neural transfer with Large source data in the Tiny setup (67.49) and neural transfer with Medium source data in the Small setup (70.82). The former is lower than the latter.", "statement": "Neural transfer with Large source data in the Tiny setup has a higher F1 score than neural transfer with Medium source data in the Small setup.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: F1 score for Danish NER.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/356.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F1 score for Danish NER." ], "reasoning": "The neural Bilstm tagger outperforms Polyglot in most categories except for Location, where Polyglot performs better.", "description": "Existing systems (RQ3) are evaluated and results Polyglot (Al-Rfou et al., 2013) overall performs better than DKIE (Derczynski et al., 2014).2 The best system is our cross-lingual transfer NER from MEDIUM source data paired with SMALL amounts of gold data.. Per-entity evaluation shows that the neural Bilstm tagger outperforms Polyglot except for Location, which is consistent across evaluation sets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: F1 score for Danish NER.'\nProblem: 'Which system performs best overall on the Dev set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/356.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F1 score for Danish NER." ], "reasoning": "To determine the best overall system on the Dev set, compare the 'All' column values for each system. Bilstm has the highest value of 70.8.", "question": "Which system performs best overall on the Dev set?", "answer": { "answer": "Bilstm" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPolyglot performs better than DKIE in the Location category on the Dev set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/356.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F1 score for Danish NER." ], "reasoning": "In the Dev set, Polyglot has a value of 73.4 in the Location (LOC) category, while DKIE has a value of 63.6.", "statement": "Polyglot performs better than DKIE in the Location category on the Dev set.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results of present keyphrase prediction on five datasets. Suffix “-2RF1” denotes that a model is trained by our reinforcement learning approach.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/357.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of present keyphrase prediction on five datasets. Suffix “-2RF1” denotes that a model is trained by our reinforcement learning approach." ], "reasoning": "The reinforcement learning algorithm catSeqTG-2 RF1 significantly enhances the keyphrase extraction performance of all baseline generative models.", "description": "The evaluation results of different models on predicting present keyphrases are shown in Table 2. We observe that our reinforcement learning algorithm catSeqTG-2 [ITALIC] RF1 consistently improves the keyphrase extraction ability of all baseline generative models by a large margin." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results of present keyphrase prediction on five datasets. Suffix “-2RF1” denotes that a model is trained by our reinforcement learning approach.'\nProblem: 'Which model achieved the highest F1 score for the NUS dataset at M?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/357.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of present keyphrase prediction on five datasets. Suffix “-2RF1” denotes that a model is trained by our reinforcement learning approach." ], "reasoning": "By examining the NUS F1@M column, we see that the highest score is 0.433, achieved by the catSeqTG-2 RF1 model.", "question": "Which model achieved the highest F1 score for the NUS dataset at M?", "answer": "So, the answer is {\"answer\": \"catSeqTG-2 RF1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe catSeqTG-2 RF1 model achieved the highest F1 score for the KP20k dataset at M.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/357.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of present keyphrase prediction on five datasets. Suffix “-2RF1” denotes that a model is trained by our reinforcement learning approach." ], "reasoning": "By looking at the KP20k F1@M column, we see that the highest score is 0.386, achieved by the catSeqTG-2 RF1 model.", "statement": "The catSeqTG-2 RF1 model achieved the highest F1 score for the KP20k dataset at M.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: The abilities of predicting the correct number of keyphrases on the KP20k dataset. MAE denotes the mean absolute error (the lower the better), Avg. # denotes the average number of generated keyphrases per document.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/358.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The abilities of predicting the correct number of keyphrases on the KP20k dataset. MAE denotes the mean absolute error (the lower the better), Avg. # denotes the average number of generated keyphrases per document." ], "reasoning": "Our deep reinforced models outperform baselines in predicting absent keyphrases and generate more absent keyphrases, while achieving higher F-measures for present keyphrases.", "description": "We also report the average number of generated keyphrases per document, denoted as. \"Avg. #\". The results are shown in Table 4, where oracle is a model that always generates the ground-truth keyphrases. The resultant MAEs demonstrate that our deep reinforced models notably outperform the baselines on predicting the number of absent keyphrases and slightly outperform the baselines on predicting the number of present keyphrases. Moreover, our deep reinforced models generate significantly more absent keyphrases than the baselines. Besides, the baseline models and our reinforced models generate similar numbers of present keyphrases, while our reinforced models achieve notably higher F -measures, implying that our methods generate present keyphrases more accurately than the baselines." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: The abilities of predicting the correct number of keyphrases on the KP20k dataset. MAE denotes the mean absolute error (the lower the better), Avg. # denotes the average number of generated keyphrases per document.'\nProblem: 'Which model has the lowest MAE for predicting absent keyphrases?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/358.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The abilities of predicting the correct number of keyphrases on the KP20k dataset. MAE denotes the mean absolute error (the lower the better), Avg. # denotes the average number of generated keyphrases per document." ], "reasoning": "By examining the 'Absent MAE' column, the model with the lowest value is catSeqTG-2 RF1 with an MAE of 1.439.", "question": "Which model has the lowest MAE for predicting absent keyphrases?", "answer": "So, the answer is {\"answer\": \"catSeqTG-2 RF1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe catSeqD-2 RF1 model generates the highest average number of absent keyphrases.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/358.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The abilities of predicting the correct number of keyphrases on the KP20k dataset. MAE denotes the mean absolute error (the lower the better), Avg. # denotes the average number of generated keyphrases per document." ], "reasoning": "By examining the 'Absent Avg. #' column, the model with the highest value is catSeqTG-2 RF1 with an average of 1.749, not catSeqD-2 RF1.", "statement": "The catSeqD-2 RF1 model generates the highest average number of absent keyphrases.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Ablation study on the KP20k dataset. Suffix “-2RF1” denotes our full RL approach. Suffix “-2F1” denotes that we replace our adaptive RF1 reward function in the full approach by an F1 reward function. Suffix “-RF1” denotes that we replace the two separate RF1 reward signals in our full approach with only one RF1 reward signal for all the generated keyphrases.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/359.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation study on the KP20k dataset. Suffix “-2RF1” denotes our full RL approach. Suffix “-2F1” denotes that we replace our adaptive RF1 reward function in the full approach by an F1 reward function. Suffix “-RF1” denotes that we replace the two separate RF1 reward signals in our full approach with only one RF1 reward signal for all the generated keyphrases." ], "reasoning": "The RF1 reward function slightly outperforms the F1 reward function, and catSeq-RF1 performs poorly on absent keyphrase prediction compared to catSeq-2RF1.", "description": "We conduct an ablation study to further analyze our reinforcement learning algorithm. The results are reported in Table 5.. As seen in Table 5, although the performance of catSeq-RF1 is competitive to catSeq-2RF1 on predicting present keyphrases, it yields an extremely poor performance on absent keyphrase prediction.. By comparing the last two rows in Table 5, we observe that our RF1 reward function slightly outperforms the F1 reward function." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Ablation study on the KP20k dataset. Suffix “-2RF1” denotes our full RL approach. Suffix “-2F1” denotes that we replace our adaptive RF1 reward function in the full approach by an F1 reward function. Suffix “-RF1” denotes that we replace the two separate RF1 reward signals in our full approach with only one RF1 reward signal for all the generated keyphrases.'\nProblem: 'Which model has the highest performance on predicting absent keyphrases?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/359.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation study on the KP20k dataset. Suffix “-2RF1” denotes our full RL approach. Suffix “-2F1” denotes that we replace our adaptive RF1 reward function in the full approach by an F1 reward function. Suffix “-RF1” denotes that we replace the two separate RF1 reward signals in our full approach with only one RF1 reward signal for all the generated keyphrases." ], "reasoning": "By comparing the 'Absent F1@M' and 'Absent F1@5' columns, catSeq-2 RF1 has the highest values of 0.047 and 0.024 respectively.", "question": "Which model has the highest performance on predicting absent keyphrases?", "answer": "So, the answer is {\"answer\": \"catSeq-2 RF1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\ncatSeq-RF1 has the highest performance on predicting present keyphrases.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/359.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation study on the KP20k dataset. Suffix “-2RF1” denotes our full RL approach. Suffix “-2F1” denotes that we replace our adaptive RF1 reward function in the full approach by an F1 reward function. Suffix “-RF1” denotes that we replace the two separate RF1 reward signals in our full approach with only one RF1 reward signal for all the generated keyphrases." ], "reasoning": "By comparing the 'Present F1@M' and 'Present F1@5' columns, catSeq-RF1 does not have the highest values. catSeq-2 RF1 has the highest values of 0.383 and 0.310 respectively.", "statement": "catSeq-RF1 has the highest performance on predicting present keyphrases.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Keyphrase prediction results on the KP20k dataset with our new evaluation method.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/360.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Keyphrase prediction results on the KP20k dataset with our new evaluation method." ], "reasoning": "The evaluation scores computed by the new method are higher than those computed by the old method for all generative models.", "description": "Table 6 shows that for all generative models, the evaluation scores computed by our method are higher than those computed by prior method." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Keyphrase prediction results on the KP20k dataset with our new evaluation method.'\nProblem: 'Which model shows the highest increase in Present F1@M score when using the new method compared to the old method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/360.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Keyphrase prediction results on the KP20k dataset with our new evaluation method." ], "reasoning": "To find the model with the highest increase in Present F1@M score, we need to compare the differences between the new and old scores for each model. The model with the highest difference is catSeqTG-2 RF1 with an increase from 0.386 to 0.398.", "question": "Which model shows the highest increase in Present F1@M score when using the new method compared to the old method?", "answer": { "answer": "catSeqTG-2 RF1" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe catSeqD model has a higher Absent F1@M score with the new method compared to the old method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/360.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Keyphrase prediction results on the KP20k dataset with our new evaluation method." ], "reasoning": "The table shows that the Absent F1@M score for the catSeqD model is 0.033 with the new method and 0.031 with the old method, indicating an increase.", "statement": "The catSeqD model has a higher Absent F1@M score with the new method compared to the old method.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Automatic Metrics Evaluation for Different Models.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/361.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Automatic Metrics Evaluation for Different Models." ], "reasoning": "AR+MMI variants outperform AR across all metrics, with AR+MMI+diverse generating the most diverse N-best list and AR+MMI+RL using future lookahead for better performance. Non-AR models generate more diverse responses but are less coherent compared to AR models.", "description": "Results are shown in Table 1.. When comparing AR with AR+MMI, AR+MMI significantly outperforms AR across all metrics,. lookahead strategy to estimate For the variants of AR+MMI, AR+MMI+diverse generates a more diverse N-best list for reranking, and thus outperforms AR+MMI; AR+MMI+RL uses future and thus outperforms backward probability, AR+MMI as well.. It's hard to tell which model performs better, AR or non-AR: AR performs better than non-AR for BLEU and adversarial success, but worse for the other metrics. This means comparing with AR model, non-AR model tends to generate more diverse responses, but might be less coherent.. When comparing non-AR with AR+MMI+diverse, non-AR has relatively lower distinct score, but significantly higher scores BLEU and adversarial success." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Automatic Metrics Evaluation for Different Models.'\nProblem: 'Which model has the highest BLEU score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/361.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Automatic Metrics Evaluation for Different Models." ], "reasoning": "By comparing the BLEU scores of all models in the table, NonAR+MMI has the highest BLEU score of 2.68.", "question": "Which model has the highest BLEU score?", "answer": { "answer": "NonAR+MMI" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAR+MMI+RL has a higher distinct-1 score than AR+MMI.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/361.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Automatic Metrics Evaluation for Different Models." ], "reasoning": "The table shows that AR+MMI+RL has a distinct-1 score of 13.7%, while AR+MMI has a distinct-1 score of 10.6%, indicating that AR+MMI+RL indeed has a higher distinct-1 score.", "statement": "AR+MMI+RL has a higher distinct-1 score than AR+MMI.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Human judgments for Coherence and Content Richeness of the different models.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/362.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human judgments for Coherence and Content Richeness of the different models." ], "reasoning": "NonAR+MMI outperforms AR+MMI in both dialogue coherence and content richness, with a larger gap in content richness.", "description": "For dialogue coherence trend is 3. that NonAR+MMI than AR+MMI, followed by AR and Non-AR. AR is slightly better than Non-AR. For Content Richness, the is significantly better proposed NonAR+MMI than AR+MMI, and the gap is greater than dialogue coherence.. The output from the AR+MMI model is thus by far less diverse than nonAR+MMI, which obtains the MMI score for each generated token." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Human judgments for Coherence and Content Richeness of the different models.'\nProblem: 'Which model has the highest agreement percentage in coherence?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/362.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human judgments for Coherence and Content Richeness of the different models." ], "reasoning": "By examining the 'agr(%) Coherence' column, we see that the NonAR+MMI model has the highest agreement percentage at 52.9%.", "question": "Which model has the highest agreement percentage in coherence?", "answer": { "answer": "NonAR+MMI" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe AR+MMI model has the highest disagreement percentage in content richness.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/362.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human judgments for Coherence and Content Richeness of the different models." ], "reasoning": "By looking at the 'disagr(%) Content Richness' column, we see that the AR model has the highest disagreement percentage at 38.2%, not the AR+MMI model.", "statement": "The AR+MMI model has the highest disagreement percentage in content richness.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: The performances of NonAR+MMI methods on WMT14 En↔De and WMT16 Ro→En. Results from Gu et al. (2018); Lee et al. (2018); Ma et al. (2019) are copied from original papers for reference purposes.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/363.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The performances of NonAR+MMI methods on WMT14 En↔De and WMT16 Ro→En. Results from Gu et al. (2018); Lee et al. (2018); Ma et al. (2019) are copied from original papers for reference purposes." ], "reasoning": "The incorporation of the MMI model significantly improves MT performances across all datasets.", "description": "Results are shown in Table 4. As can be seen, the incorporation of MMI model significantly improves MT performances." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: The performances of NonAR+MMI methods on WMT14 En↔De and WMT16 Ro→En. Results from Gu et al. (2018); Lee et al. (2018); Ma et al. (2019) are copied from original papers for reference purposes.'\nProblem: 'What is the improvement in performance for the WMT14 En→De dataset when using the NAT + MMI model compared to NAT (our implementation)?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/363.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The performances of NonAR+MMI methods on WMT14 En↔De and WMT16 Ro→En. Results from Gu et al. (2018); Lee et al. (2018); Ma et al. (2019) are copied from original papers for reference purposes." ], "reasoning": "To find the improvement, subtract the performance of NAT (our implementation) from the performance of NAT + MMI for the WMT14 En→De dataset.", "question": "What is the improvement in performance for the WMT14 En→De dataset when using the NAT + MMI model compared to NAT (our implementation)?", "answer": { "answer": "1.48" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe NAT + MMI model achieves the highest performance on the WMT16 Ro→En dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/363.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The performances of NonAR+MMI methods on WMT14 En↔De and WMT16 Ro→En. Results from Gu et al. (2018); Lee et al. (2018); Ma et al. (2019) are copied from original papers for reference purposes." ], "reasoning": "Compare the performance of all models on the WMT16 Ro→En dataset. The NAT + MMI model has the highest score of 30.50.", "statement": "The NAT + MMI model achieves the highest performance on the WMT16 Ro→En dataset.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Different weighting variations evaluated on the compounds dataset (32,246 nominal compounds). All variations use t=100 transformations, word representations with n=200 dimensions and the dropout rate that was observed to work best on the dev dataset (see Appendix B for details). Results on the 6442 compounds in the test set of the German compounds dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/364.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Different weighting variations evaluated on the compounds dataset (32,246 nominal compounds). All variations use t=100 transformations, word representations with n=200 dimensions and the dropout rate that was observed to work best on the dev dataset (see Appendix B for details). Results on the 6442 compounds in the test set of the German compounds dataset." ], "reasoning": "TransWeight significantly improves prediction quality with 65.21% of test compounds ranked ≤5, outperforming other models despite using more parameters.", "description": "Table 3 compares the performance of the four weighting variants introduced in Section 3.2. TransWeight-feat, which sums the transformed representations and then weights each component of the summed representation, has the weakest performance, with only 50.82% of the test compounds receiving a rank that is lower than 5. A better performance – 52.90% – is obtained by applying the same weighting for each column of the transformations matrix H. The results of TransWeight-trans are interesting in two respects: first, it outperforms the feature variation, TransWeight-feat, despite training a smaller number of parameters (300 vs. 400 in our setup). Second, it performs on par with the TransWeight-mat variation, although the latter has a larger number of parameters (20,200 in our setup). This suggests that an effective combination method needs to take into account full transformations, i.e. entire rows of H and combine them in a systematic way. TransWeight builds on this insight by making each element of the final composed representation p dependent on each component of the transformed representation H. The result is a noteworthy increase in the quality of the predictions, with ∼12% more of the test representations having a rank ≤5. Although this weighting does use significantly more parameters than the previous weightings (4,000,200 parameters), the number of parameters is relative to the number of transformations t and does not grow with the size of the vocabulary. As the results in the next subsection show, a relatively small number of transformations is sufficient even for larger training vocabularies." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Different weighting variations evaluated on the compounds dataset (32,246 nominal compounds). All variations use t=100 transformations, word representations with n=200 dimensions and the dropout rate that was observed to work best on the dev dataset (see Appendix B for details). Results on the 6442 compounds in the test set of the German compounds dataset.'\nProblem: 'Which model has the highest percentage of test compounds receiving a rank ≤5 and what is that percentage?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/364.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Different weighting variations evaluated on the compounds dataset (32,246 nominal compounds). All variations use t=100 transformations, word representations with n=200 dimensions and the dropout rate that was observed to work best on the dev dataset (see Appendix B for details). Results on the 6442 compounds in the test set of the German compounds dataset." ], "reasoning": "By comparing the percentages in the ≤5 column, TransWeight has the highest percentage at 65.21%.", "question": "Which model has the highest percentage of test compounds receiving a rank ≤5?", "answer": { "answer": "TransWeight%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nTransWeight-feat has the highest cosine distance among all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/364.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Different weighting variations evaluated on the compounds dataset (32,246 nominal compounds). All variations use t=100 transformations, word representations with n=200 dimensions and the dropout rate that was observed to work best on the dev dataset (see Appendix B for details). Results on the 6442 compounds in the test set of the German compounds dataset." ], "reasoning": "The table shows that TransWeight-feat has a cosine distance of 0.344, which is the highest compared to the other models.", "statement": "TransWeight-feat has the highest cosine distance among all models.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Results for English, German and Dutch on the composition of nominal compounds, adjective-noun phrases and adverb-adjective phrases.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/365.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results for English, German and Dutch on the composition of nominal compounds, adjective-noun phrases and adverb-adjective phrases." ], "reasoning": "TransWeight consistently outperforms other models across all languages and phrase types, despite having significantly fewer parameters compared to FullLex+.", "description": "The results using the corrected rank eval. TransWeight, the composition model proposed in this paper, delivers consistent results, being the best performing model across all languages and phrase types. The difference in performance to the runner-up model, FullLex+, translates into more of the test phrases being close to the original repachieving a rank ≤ 5. This resentations, i.e. difference ranges from 8% of the test phrases in the German compounds dataset to less than 1% for English adjective-noun phrases. However, it is important to note the substantial difference in the number of parameters used by the two models: all TransWeight models use 100 transformations and have, therefore, a constant number of 12,020,200 parameters. In contrast the number of parameters used by FullLex+ increases with the size of the training vocabulary, reaching 739,320,200 parameters in the case of the English adjective-noun dataset. The most difficult task for all the composition models in any of the three languages is compound composition. We believe this difficulty can be mainly attributed to the complexity introduced by the position. For example in adjective-noun composition, the adjective always takes the first position, and the noun the second. However, in compounds the same noun can occur in both positions throughout different training examples. Consider for example the compounds boat house and house boat. In boat house – a house to store boats – the meaning of house is shifted towards shelter for an inanimate object, whereas house boat selects from house aspects related to human beings and their daily lives happening on the boat. These positionrelated differences can make it more challenging to create composed representations. that makes adverbadjective easier is the high dataset frequency of some of the adverbs/adjectives. For example, in the English and adjective-noun datasets Another aspect the. adjective-noun dataset a small subset of 52 adjectives like new, good, small, public, etc. are extremely frequent, occurring more than 500 times in the training portion of the adjective-noun sample dataset. Because the adjective is always the first element of the composition, the phrases that include these frequent adjectives amount to around 24.8% of the test dataset. Frequent constituents are more likely to be modeled correctly by composition – thus leading to better results.. The additive models (Addition, SAddition, VAddition) are the least competitive models in our evaluation, on all datasets. The results strongly argue for the point that additive models are too limited for composition. An adequate composed representation cannot be obtained simply as an (weighted) average of the input components.. The Matrix model clearly outperforms the additive models. However, its results are modest in comparison to models like WMask+, BiLinear, FullLex+ and TransWeight. This is to be expected: having a single affine transformation limits the model's capacity to adapt to all the possi. ble input vectors u and v. Because of its small number of parameters, the Matrix model can only capture the general trends in the data.. More interaction between u and v is promoted by the BiLinear model through the d bilinear forms in the tensor. ∈ Rn×d×n. This capacity to absorb more information from the training data translates into better results — the BiLinear model outperforms the Matrix model on all datasets.. In evaluating FullLex we tried to mitigate its treatment of unknown words. Instead of using unknown matrices to model composition of phrases not in the training data, we take a nearest neighbor approach to composition. Take for example the phrase sky-blue dress, where sky-blue does not occur in train. Our implementation, FullLex+, looks for the nearest neighbor of sky-blue that appears in train, blue and uses the matrix associated with it for building the composed representation. The same approach is also used for the WMask model, which is referred to as WMask+.. On this dataset WMask+ fares only slightly worse than FullLex+ (0.70%), an indication that FullLex+ suffers from data sparsity in such scenarios and cannot produce good results without an adequate amount of training data. By contrast, the gap between the two models increases considerably on datasets with more phrases per word — e.g. FullLex+ outperforms WMask+ with 8.07% on the English adjective-noun phrase dataset, which has 11.6 phrases per word." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Results for English, German and Dutch on the composition of nominal compounds, adjective-noun phrases and adverb-adjective phrases.'\nProblem: 'Which model has the highest percentage of Adverb-Adjective Phrases ≤ 5 in the English dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/365.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results for English, German and Dutch on the composition of nominal compounds, adjective-noun phrases and adverb-adjective phrases." ], "reasoning": "To find the model with the highest percentage of Adverb-Adjective Phrases ≤ 5 in the English dataset, we look at the column 'Adverb-Adjective Phrases ≤ 5' under 'English'. The highest value is 95.78%, which corresponds to the TransWeight model.", "question": "Which model has the highest percentage of Adverb-Adjective Phrases ≤ 5 in the English dataset?", "answer": "So, the answer is {\"answer\": \"TransWeight\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Matrix model outperforms the BiLinear model in the German Adjective-Noun Phrases Q3 dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/365.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results for English, German and Dutch on the composition of nominal compounds, adjective-noun phrases and adverb-adjective phrases." ], "reasoning": "To verify this statement, we compare the values for the Matrix and BiLinear models in the 'Adjective-Noun Phrases Q3' column under 'German'. The Matrix model has a value of 8, while the BiLinear model has a value of 3. Therefore, the statement is refuted.", "statement": "The Matrix model outperforms the BiLinear model in the German Adjective-Noun Phrases Q3 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Results on DDI 2013'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/366.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on DDI 2013" ], "reasoning": "Pooling methods over the whole sentence (ENT-SENT and ENT-DYM) outperform ENT-ONLY in the DDI-2013 dataset across different models, with ENT-DYM being better for CNN. Syntax-based pooling (ENT-DEP0) is generally worse than non-syntax pooling methods.", "description": "1. Comparing ENT-SENT, ENT-DYM and ENT-ONLY, we see that the pooling methods over the whole sentence (i.e., ENT-SENT and ENT-DYM) are significantly better than ENT-ONLY that only focuses on the two entity mentions of interest in the DDI-2013 dataset. This is true across different deep learning models in this work. Comparing ENT-SENT and ENT-DYM, their performance are comparable in DDI-2013 (except for CNN where ENT-DYM is better). Comparing the syntax-based pooling methods and the non-syntax pooling methods, the pooling based on dependency paths (i.e., ENT-DEP0) is worse than the non-syntax pooling methods (i.e., ENT-SENT and ENT-DYM) and perform comparably with ENT-ONLY in the DDI-2013 dataset over all the models (except for the CNN model where ENT-ONLY is much worse)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Results on DDI 2013'\nProblem: 'Which pooling method achieves the highest F1 score in the DDI-2013 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/366.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on DDI 2013" ], "reasoning": "By examining the F1 scores across different models and pooling methods, the highest F1 score is found in the BiLSTM model with ENT-DEP1 pooling method.", "question": "Which pooling method achieves the highest F1 score in the DDI-2013 dataset?", "answer": "So, the answer is {\"answer\": \"73.9\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nENT-DEP0 performs better than ENT-ONLY in the CNN model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/366.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on DDI 2013" ], "reasoning": "The table shows that in the CNN model, ENT-ONLY has an F1 score of 47.4, while ENT-DEP0 has an F1 score of 60.6, indicating that ENT-DEP0 performs better.", "statement": "ENT-DEP0 performs better than ENT-ONLY in the CNN model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results of BERTbase in test set of five datasets with different epochs. The SOTA results are from Madabushi et al. (2018) (TrecQA), Sha et al. (2018) (WikiQA, SemEvalcQA-16), Tay et al. (2018b) (YahooQA), Nakov et al. (2017) (SemEvalcQA-17).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/367.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of BERTbase in test set of five datasets with different epochs. The SOTA results are from Madabushi et al. (2018) (TrecQA), Sha et al. (2018) (WikiQA, SemEvalcQA-16), Tay et al. (2018b) (YahooQA), Nakov et al. (2017) (SemEvalcQA-17)." ], "reasoning": "The BERT-based models trained on a fraction of the data outperform the previous state-of-the-art models across all datasets, as shown in Tables 2 and 3.", "description": "We show the main result in Table 2 and 3. Despite training on a fraction of the data available, the proposed BERT-based models surpass the previous state-of-the-art models by a large margin on all datasets" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results of BERTbase in test set of five datasets with different epochs. The SOTA results are from Madabushi et al. (2018) (TrecQA), Sha et al. (2018) (WikiQA, SemEvalcQA-16), Tay et al. (2018b) (YahooQA), Nakov et al. (2017) (SemEvalcQA-17).'\nProblem: 'What is the highest MAP score achieved by the BERT-based models on the SemEvalQA-17 dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/367.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of BERTbase in test set of five datasets with different epochs. The SOTA results are from Madabushi et al. (2018) (TrecQA), Sha et al. (2018) (WikiQA, SemEvalcQA-16), Tay et al. (2018b) (YahooQA), Nakov et al. (2017) (SemEvalcQA-17)." ], "reasoning": "To find the highest MAP score for the SemEvalQA-17 dataset, compare the MAP scores for epoch=3 and epoch=5. The highest score is 0.909 at epoch=3.", "question": "What is the highest MAP score achieved by the BERT-based models on the SemEvalQA-17 dataset?", "answer": "So, the answer is {\"answer\": \"0.909\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BERT-based model at epoch=5 achieved a higher MRR score on the TrecQA dataset compared to the state-of-the-art model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/367.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of BERTbase in test set of five datasets with different epochs. The SOTA results are from Madabushi et al. (2018) (TrecQA), Sha et al. (2018) (WikiQA, SemEvalcQA-16), Tay et al. (2018b) (YahooQA), Nakov et al. (2017) (SemEvalcQA-17)." ], "reasoning": "The table shows that the MRR score for the BERT-based model at epoch=5 on the TrecQA dataset is 0.944, which is higher than the state-of-the-art model's MRR score of 0.865.", "statement": "The BERT-based model at epoch=5 achieved a higher MRR score on the TrecQA dataset compared to the state-of-the-art model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results on BioNLP BB3'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/368.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on BioNLP BB3" ], "reasoning": "In the BB3 dataset, ENT-DEP0 outperforms all non-syntax pooling methods across all models, while ENT-SENT significantly outperforms ENT-DYM.", "description": "However, this comparison is reversed for the BB3 dataset where ENT-ONLY is in general better or comparable to ENT-SENT and ENT-DYM over different deep learning models. in the BB3 dataset, ENT-SENT singificantly outperforms ENT-DYM over all the models. When we switch to the BB3 dataset, it turns out thatENT-DEP0 is significantly better than all the non-syntax pooling methods (i.e., ENT-ONLY, ENT-SENT and ENT-DYM) for all the comparing models." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results on BioNLP BB3'\nProblem: 'Which method significantly outperforms ENT-DYM in the BB3 dataset across all models?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/368.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on BioNLP BB3" ], "reasoning": "The description states that in the BB3 dataset, ENT-SENT significantly outperforms ENT-DYM over all the models.", "question": "Which method significantly outperforms ENT-DYM in the BB3 dataset across all models?", "answer": "So, the answer is {\"answer\": \"ENT-SENT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nENT-DEP0 is significantly better than ENT-SENT in the BB3 dataset for all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/368.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on BioNLP BB3" ], "reasoning": "The description confirms that ENT-DEP0 is significantly better than all non-syntax pooling methods, including ENT-SENT, in the BB3 dataset for all models.", "statement": "ENT-DEP0 is significantly better than ENT-SENT in the BB3 dataset for all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Statistics for different OPIEC corpora. All frequencies are in millions. We count triples with annotations (not annotations directly). Percentages refer to the respective subcorpus.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/369.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Statistics for different OPIEC corpora. All frequencies are in millions. We count triples with annotations (not annotations directly). Percentages refer to the respective subcorpus." ], "reasoning": "About 30% of the triples in OPIEC are clean, and these clean triples are generally shorter and have a higher confidence score. Approximately 49% of all triples in OPIEC contain semantic annotations, which increases to 58% in OPIEC-Linked.", "description": "Basic statistics such as corpus sizes, frequency of various semantic annotations, and information about the length of the extracted triples of OPIEC and its subcorpora are shown in Tab. 2.. Roughly 30% of the triples (104M) in OPIEC are clean according to the above constraints. Table 2 shows that clean triples are generally shorter on average and tend to have a higher confidence score than the full set of triples in OPIEC.. About 49% of all triples in OPIEC contain some sort of semantic annotation (cf. Tab. 2); in OPIEC-Linked, the fraction increases to 58%. Most of the semantic annotations referred to quantities, space or time; these annotations provide important context for the extractions. There is a significantly smaller amount of negative polarity and possibility modality annotations. One reason for the lack of such annotations may be in the nature of the Wikipedia articles, which aim to contain encyclopedic, factual statements and are thus more rarely negated or hedged." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Statistics for different OPIEC corpora. All frequencies are in millions. We count triples with annotations (not annotations directly). Percentages refer to the respective subcorpus.'\nProblem: 'What is the average confidence score of clean triples in OPIEC?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/369.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Statistics for different OPIEC corpora. All frequencies are in millions. We count triples with annotations (not annotations directly). Percentages refer to the respective subcorpus." ], "reasoning": "The table shows that the confidence score for OPIEC-Clean is 0.59±0.23.", "question": "What is the average confidence score of clean triples in OPIEC?", "answer": "So, the answer is {\"answer\": \"0.59±0.23\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe majority of triples in OPIEC-Linked contain semantic annotations.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/369.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Statistics for different OPIEC corpora. All frequencies are in millions. We count triples with annotations (not annotations directly). Percentages refer to the respective subcorpus." ], "reasoning": "The table indicates that 58% of triples in OPIEC-Linked contain semantic annotations, which is more than half.", "statement": "The majority of triples in OPIEC-Linked contain semantic annotations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: The most frequent open relations aligned to the DBpedia relations location, associatedMusicalArtist, and spouse in OPEIC-Linked'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/370.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The most frequent open relations aligned to the DBpedia relations location, associatedMusicalArtist, and spouse in OPEIC-Linked" ], "reasoning": "Open relations can be ambiguous or more specific than KB relations, and their frequencies show the number of OIE triples aligning with specified KB relations.", "description": "Tab. 4 shows the most frequent open relations aligned to the DBpedia relations location, associatedMusicalArtist, and spouse. The frequencies correspond to the number of OIE triples that (1) have the specified open relation (e.g., \"be wife of \") and (2) have a KB hit with the specified KB relation (e.g., spouse). There is clearly no 1:1 correspondence between open relations and KB relations. On the one hand, open relations can be highly ambiguous (e.g., \"be\" has hits to location and associatedMusicalArtits). On the other hand, open relations can also be more specific than KB relations (e.g., \"be guitarist of \" is more specific than associatedMusicalArtist) or semantically different (e.g., \"be widow of \" and spouse) than the KB relations they align to." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: The most frequent open relations aligned to the DBpedia relations location, associatedMusicalArtist, and spouse in OPEIC-Linked'\nProblem: 'Which open relation has the highest frequency for the KB relation 'associatedMusicalArtist'?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/370.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The most frequent open relations aligned to the DBpedia relations location, associatedMusicalArtist, and spouse in OPEIC-Linked" ], "reasoning": "To find the open relation with the highest frequency for 'associatedMusicalArtist', look at the 'associatedMusicalArtist' column and identify the open relation with the largest number. The highest frequency is 3,600 for the open relation 'have'.", "question": "Which open relation has the highest frequency for the KB relation 'associatedMusicalArtist'?", "answer": "So, the answer is {\"answer\": \"have\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe open relation 'be' has the highest frequency for the KB relation 'location'.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/370.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The most frequent open relations aligned to the DBpedia relations location, associatedMusicalArtist, and spouse in OPEIC-Linked" ], "reasoning": "To verify this statement, check the 'location' column for the highest frequency. The highest frequency is 3,175 for the open relation 'have', not 'be'.", "statement": "The open relation 'be' has the highest frequency for the KB relation 'location'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results with frame filtering'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/371.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results with frame filtering" ], "reasoning": "PAFIBERT achieved state-of-the-art results in frame identification on both in-domain and out-of-domain test sets, with better performance using LU-based frame filtering compared to target-based frame filtering.", "description": "Table 2 and 3 compare the accuracies of our models to the results reported by several representative studies in frame-semantic parsing.. As can be seen from the tables, PAFIBERT outperformed other models on both the in-domain Das's test set and the out-of-domain YAGS test set, yielding new state-of-the-art results for frame identification. As expected, the results obtained with LU-based frame filtering were slightly better than those produced using target-based frame filtering, suggesting that a realistic setup is crucial in measuring model performance more precisely.. On Das's test set, for example, PAFIBERT achieved absolute improvements of 4-5% and 9% with frame filtering (Table 2) and without frame filtering (Table 3) respectively. As reported by , frame identification models usually suffer a drastic drop in performance when tested on out-of-domain data. The same trend was observed in our experiments for the YAGS test set. Nevertheless, PAFIBERT still outperformed existing methods by a large margin on this out-of-domain test set. Besides, in the more challenging setup that involved no frame filtering, the results obtained by PAFIBERT were on par with the prior state-of-the-art accuracies achieved with frame filtering." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results with frame filtering'\nProblem: 'What was the accuracy of PAFIBERT filtered by LUs on the YAGS test set for FrameNet 1.5 All?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/371.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results with frame filtering" ], "reasoning": "To find the accuracy of PAFIBERT filtered by LUs on the YAGS test set for FrameNet 1.5 All, locate the corresponding cell in the table. The value is 75.06.", "question": "What was the accuracy of PAFIBERT filtered by LUs on the YAGS test set for FrameNet 1.5 All?", "answer": "So, the answer is {\"answer\": \"75.06\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPAFIBERT filtered by targets achieved higher accuracy on the YAGS test set for FrameNet 1.5 All compared to SEMAFOR.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/371.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results with frame filtering" ], "reasoning": "Compare the accuracy of PAFIBERT filtered by targets and SEMAFOR on the YAGS test set for FrameNet 1.5 All. PAFIBERT filtered by targets achieved 75.01, while SEMAFOR achieved 60.01, supporting the statement.", "statement": "PAFIBERT filtered by targets achieved higher accuracy on the YAGS test set for FrameNet 1.5 All compared to SEMAFOR.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results of BERTbase and BERTlarge in test set of five datasets. The number of training epochs is 3.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/372.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of BERTbase and BERTlarge in test set of five datasets. The number of training epochs is 3." ], "reasoning": "The BERT-based models outperform previous state-of-the-art models on all datasets, even with less training data.", "description": "We show the main result in Table 2 and 3. Despite training on a fraction of the data available, the proposed BERT-based models surpass the previous state-of-the-art models by a large margin on all datasets" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Results of BERTbase and BERTlarge in test set of five datasets. The number of training epochs is 3.'\nProblem: 'Which dataset and model combination achieved the highest MRR score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/372.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of BERTbase and BERTlarge in test set of five datasets. The number of training epochs is 3." ], "reasoning": "To find the highest MRR score, compare all the MRR values in the table. The highest MRR score is 0.961, achieved by the TrecQA large model.", "question": "Which dataset achieved the highest MRR score with large?", "answer": { "answer": "TrecQA" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe SemEvalcQA-16 large model has a higher MRR score than the YahooQA large model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/372.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of BERTbase and BERTlarge in test set of five datasets. The number of training epochs is 3." ], "reasoning": "The MRR score for SemEvalcQA-16 large is 0.911, while the MRR score for YahooQA large is 0.938. Therefore, the statement is refuted.", "statement": "The SemEvalcQA-16 large model has a higher MRR score than the YahooQA large model.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Hyperparameter tuning methods with original pre-processing and fixed CRCNN model. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to Default with p<0.05 except those marked with a •. Note that hyperparameter tuning can involve much higher performance variation depending on the distribution of the data. Therefore, even though there is no statistical significance in the manual search case for the held out fold in the ddi dataset, there was statistical significance for the dev fold which drove those set of hyperparameters. For both ddi and i2b2 datasets, manual search is better than random search with p<0.05.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/373.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Hyperparameter tuning methods with original pre-processing and fixed CRCNN model. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to Default with p<0.05 except those marked with a •. Note that hyperparameter tuning can involve much higher performance variation depending on the distribution of the data. Therefore, even though there is no statistical significance in the manual search case for the held out fold in the ddi dataset, there was statistical significance for the dev fold which drove those set of hyperparameters. For both ddi and i2b2 datasets, manual search is better than random search with p<0.05." ], "reasoning": "Manual search outperformed random search in both i2b2 and ddi corpora, indicating its effectiveness despite requiring less expert knowledge than Bayesian optimization.", "description": "table 6, where we compare hyperparameter tuning methodologies.. Perturbations on the hyperparameter search are listed in table 6 and compare performance with different hyperparameter values found using different tuning strategies.. We tested how manual tuning, requiring less expert knowledge than Bayesian optimization, would compare to the random search strategy in table 6. For both i2b2 and ddi corpora, manual search outperformed random search." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Hyperparameter tuning methods with original pre-processing and fixed CRCNN model. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to Default with p<0.05 except those marked with a •. Note that hyperparameter tuning can involve much higher performance variation depending on the distribution of the data. Therefore, even though there is no statistical significance in the manual search case for the held out fold in the ddi dataset, there was statistical significance for the dev fold which drove those set of hyperparameters. For both ddi and i2b2 datasets, manual search is better than random search with p<0.05.'\nProblem: 'Which hyperparameter tuning strategy achieved the highest performance on the ddi Detect dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/373.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Hyperparameter tuning methods with original pre-processing and fixed CRCNN model. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to Default with p<0.05 except those marked with a •. Note that hyperparameter tuning can involve much higher performance variation depending on the distribution of the data. Therefore, even though there is no statistical significance in the manual search case for the held out fold in the ddi dataset, there was statistical significance for the dev fold which drove those set of hyperparameters. For both ddi and i2b2 datasets, manual search is better than random search with p<0.05." ], "reasoning": "By comparing the performance values for the ddi Detect dataset across different tuning strategies, we see that the highest performance is achieved by the 'Default' strategy with a value of 87.76.", "question": "Which hyperparameter tuning strategy achieved the highest performance on the ddi Detect dataset?", "answer": { "answer": "Default" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRandom search achieved the highest performance on the semeval dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/373.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Hyperparameter tuning methods with original pre-processing and fixed CRCNN model. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to Default with p<0.05 except those marked with a •. Note that hyperparameter tuning can involve much higher performance variation depending on the distribution of the data. Therefore, even though there is no statistical significance in the manual search case for the held out fold in the ddi dataset, there was statistical significance for the dev fold which drove those set of hyperparameters. For both ddi and i2b2 datasets, manual search is better than random search with p<0.05." ], "reasoning": "By examining the performance values for the semeval dataset, we see that the highest performance is achieved by 'Random Search' with a value of 82.2.", "statement": "Random search achieved the highest performance on the semeval dataset.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Pre-processing techniques with CRCNN model. Row labels Original = simple tokenization and lower casing of words, Punct = punctuation removal, Digit = digit removal and Stop = stop word removal. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to Original pre-processing (p<0.05) using a paired t-test except those marked with a •'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/374.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Pre-processing techniques with CRCNN model. Row labels Original = simple tokenization and lower casing of words, Punct = punctuation removal, Digit = digit removal and Stop = stop word removal. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to Original pre-processing (p<0.05) using a paired t-test except those marked with a •" ], "reasoning": "Pre-processing techniques like entity blinding and punctuation/digit handling can significantly impact performance, with entity blinding showing notable improvements for ddi but mixed results for semeval.", "description": "importance of pre-processing in performance improvements. Experiments in table 4 reveal that they can cause larger variations in performance than modeling.. Punctuation and digits hold more importance for the ddi dataset, which is a biomedical dataset, compared to the other two datasets.. We found that stop words seem to be important for relation extraction for all three datasets that we looked at, to a smaller degree for i2b2 compared to the other two datasets.. Entity blinding causes almost 9% improvement in classification performance and 1% improvement in detection performance.. While entity blinding hurts performance for semeval, possibly due to the coarse-grained nature of the replacement, NER blinding does not hurt performance.. entity blinding seems to help test set performance for ddi in table 4, but shows no statistical significance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Pre-processing techniques with CRCNN model. Row labels Original = simple tokenization and lower casing of words, Punct = punctuation removal, Digit = digit removal and Stop = stop word removal. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to Original pre-processing (p<0.05) using a paired t-test except those marked with a •'\nProblem: 'Which pre-processing technique shows the highest improvement in ddi Class performance?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/374.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Pre-processing techniques with CRCNN model. Row labels Original = simple tokenization and lower casing of words, Punct = punctuation removal, Digit = digit removal and Stop = stop word removal. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to Original pre-processing (p<0.05) using a paired t-test except those marked with a •" ], "reasoning": "By comparing the ddi Class performance scores across different pre-processing techniques, entity blinding shows the highest score of 83.56.", "question": "Which pre-processing technique shows the highest improvement in ddi Class performance?", "answer": { "answer": "Entity Blinding" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nEntity blinding improves i2b2 Class performance more than NER blinding.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/374.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Pre-processing techniques with CRCNN model. Row labels Original = simple tokenization and lower casing of words, Punct = punctuation removal, Digit = digit removal and Stop = stop word removal. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to Original pre-processing (p<0.05) using a paired t-test except those marked with a •" ], "reasoning": "The table shows that entity blinding improves i2b2 Class performance to 76.59, while NER blinding results in a performance of 66.26, indicating that entity blinding has a greater positive impact.", "statement": "Entity blinding improves i2b2 Class performance more than NER blinding.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Modeling techniques with original pre-processing. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to CRCNN model (p<0.05) using a paired t-test except those marked with a •. In terms of statistical significance, comparing contextualized embeddings with each other reveals that BERT-tokens is equivalent to ELMo for i2b2, but for semeval BERT-tokens is better than ELMo and for ddi BERT-tokens is better than ELMo only for detection.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/375.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Modeling techniques with original pre-processing. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to CRCNN model (p<0.05) using a paired t-test except those marked with a •. In terms of statistical significance, comparing contextualized embeddings with each other reveals that BERT-tokens is equivalent to ELMo for i2b2, but for semeval BERT-tokens is better than ELMo and for ddi BERT-tokens is better than ELMo only for detection." ], "reasoning": "Piecewise pooling helps i2b2 by 1% but hurts test set performance on ddi and doesn't affect semeval.", "description": "No statistical significance is seen even when the test set result worsens in performance for BERTCLS and Piecewise Pool in table 5 where it hurts test set performance on ddi but is not statistically significant when cross validation is performed.. In table 5, we tested the generalizability of the commonly used piecewise pooling technique. While piecewise pooling helps i2b2 by 1%, it hurts test set performance on ddi and doesn't affect performance on semeval.. We found ELMo and BER tokens to boost performance significantly for all datasets, but that BERT-CLS hurt performance for the medical datasets. While BERT-CLS boosted test set performance for semeval, this was not found to be a statistically significant difference for cross validation." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Modeling techniques with original pre-processing. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to CRCNN model (p<0.05) using a paired t-test except those marked with a •. In terms of statistical significance, comparing contextualized embeddings with each other reveals that BERT-tokens is equivalent to ELMo for i2b2, but for semeval BERT-tokens is better than ELMo and for ddi BERT-tokens is better than ELMo only for detection.'\nProblem: 'Which model boosts performance significantly for all datasets?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/375.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Modeling techniques with original pre-processing. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to CRCNN model (p<0.05) using a paired t-test except those marked with a •. In terms of statistical significance, comparing contextualized embeddings with each other reveals that BERT-tokens is equivalent to ELMo for i2b2, but for semeval BERT-tokens is better than ELMo and for ddi BERT-tokens is better than ELMo only for detection." ], "reasoning": "The description states that ELMo and BERT-tokens boost performance significantly for all datasets. From the table, ELMo shows high performance across all datasets.", "question": "Which model boosts performance significantly for all datasets?", "answer": "So, the answer is {\"answer\": \"ELMo\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPiecewise pooling improves performance on the semeval dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/375.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Modeling techniques with original pre-processing. Test set results at the top with cross validated results (average with standard deviation) below. All cross validated results are statistically significant compared to CRCNN model (p<0.05) using a paired t-test except those marked with a •. In terms of statistical significance, comparing contextualized embeddings with each other reveals that BERT-tokens is equivalent to ELMo for i2b2, but for semeval BERT-tokens is better than ELMo and for ddi BERT-tokens is better than ELMo only for detection." ], "reasoning": "The table shows that piecewise pooling has a performance of 80.55 on the semeval dataset, which is lower than the performance of other models like ELMo and BERT-tokens. The description also states that piecewise pooling doesn't affect performance on semeval.", "statement": "Piecewise pooling improves performance on the semeval dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Machine translation tokenized BLEU test results on IWSLT 2017 de→en, KFTT ja→en, WMT 2016 ro→en and WMT 2014 en→de, respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/376.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Machine translation tokenized BLEU test results on IWSLT 2017 de→en, KFTT ja→en, WMT 2016 ro→en and WMT 2014 en→de, respectively." ], "reasoning": "Replacing softmax with entmax does not hurt performance and often results in slightly higher BLEU scores, with sparse attention Transformers showing better potential for analysis.", "description": "We report test set tokenized BLEU (Papineni et al., 2002) results in Table 1. We can see that replacing softmax by entmax does not hurt performance in any of the datasets; indeed, sparse attention Transformers tend to have slightly higher BLEU, but their sparsity leads to a better potential for analysis. In the next section, we make use of this potential by exploring the learned internal mechanics of the self-attention heads." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Machine translation tokenized BLEU test results on IWSLT 2017 de→en, KFTT ja→en, WMT 2016 ro→en and WMT 2014 en→de, respectively.'\nProblem: 'Which activation function achieved the highest BLEU score for the en→de dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/376.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Machine translation tokenized BLEU test results on IWSLT 2017 de→en, KFTT ja→en, WMT 2016 ro→en and WMT 2014 en→de, respectively." ], "reasoning": "To find the highest BLEU score for the en→de dataset, we compare the scores for each activation function. The scores are 26.02 for softmax, 25.89 for 1.5-entmax, and 26.93 for α-entmax. The highest score is 26.93, achieved by α-entmax.", "question": "Which activation function achieved the highest BLEU score for the en→de dataset?", "answer": { "answer": "α-entmax" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 1.5-entmax activation function achieved the highest BLEU score for the ro→en dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/376.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Machine translation tokenized BLEU test results on IWSLT 2017 de→en, KFTT ja→en, WMT 2016 ro→en and WMT 2014 en→de, respectively." ], "reasoning": "To verify this statement, we compare the BLEU scores for the ro→en dataset. The scores are 32.70 for softmax, 33.10 for 1.5-entmax, and 32.89 for α-entmax. The highest score is 33.10, achieved by 1.5-entmax.", "statement": "The 1.5-entmax activation function achieved the highest BLEU score for the ro→en dataset.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Results for C-LSTM models trained with CC and arXiv embeddings on both subtasks.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/377.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results for C-LSTM models trained with CC and arXiv embeddings on both subtasks." ], "reasoning": "The C-LSTM model achieves macro-F1 scores of 67.49 and 67.02 for subtasks 1.1 and 1.2 respectively, but performs worse than the SVM model using handcrafted features.", "description": "Table 6 shows that the C-LSTM model performs well on the scientific embeddings, but consistently worse than the SVM model using handcrafted features and achieves a macro-F1 score of 67.49 and 67.02 for subtask 1.1 and subtask 1.2 respectively." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Results for C-LSTM models trained with CC and arXiv embeddings on both subtasks.'\nProblem: 'What are the macro-F1 scores for the arXiv dataset in subtasks 1.1 and 1.2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/377.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results for C-LSTM models trained with CC and arXiv embeddings on both subtasks." ], "reasoning": "The table shows the macro-F1 scores for the arXiv dataset as 67.49 for subtask 1.1 and 67.02 for subtask 1.2.", "question": "What are the macro-F1 scores for the arXiv dataset in subtasks 1.1?", "answer": { "answer": "67.49" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe C-LSTM model achieves a higher macro-F1 score for subtask 1.1 than for subtask 1.2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/377.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results for C-LSTM models trained with CC and arXiv embeddings on both subtasks." ], "reasoning": "The table shows that the macro-F1 score for subtask 1.1 is 67.49, which is higher than the macro-F1 score for subtask 1.2, which is 67.02.", "statement": "The C-LSTM model achieves a higher macro-F1 score for subtask 1.1 than for subtask 1.2.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison of segmentation performance with the state-of-the-art methods on four evaluation datasets in terms of IoU.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/378.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of segmentation performance with the state-of-the-art methods on four evaluation datasets in terms of IoU." ], "reasoning": "Our method outperforms all other methods on all datasets, with significant improvements on challenging datasets like UNC+ and G-Ref.", "description": "Table 1 presents comparisons of our method with existing state-of-the-art approaches.. Our proposed method consistently outperforms all other methods on all four datasets. The improvement is particularly significant on the more challenging datasets, such as UNC+ which has no location words and G-Ref which contains longer and richer query expressions." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison of segmentation performance with the state-of-the-art methods on four evaluation datasets in terms of IoU.'\nProblem: 'Which method has the highest score on the ReferIt test dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/378.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of segmentation performance with the state-of-the-art methods on four evaluation datasets in terms of IoU." ], "reasoning": "By comparing the scores of all methods on the ReferIt test dataset, it is evident that our method has the highest score.", "question": "Which method has the highest score on the ReferIt test dataset?", "answer": { "answer": "63.80" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe RRN method has the highest score on the UNC testA dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/378.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of segmentation performance with the state-of-the-art methods on four evaluation datasets in terms of IoU." ], "reasoning": "By examining the scores on the UNC testA dataset, it is clear that our method has the highest score, not the RRN method.", "statement": "The RRN method has the highest score on the UNC testA dataset.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Ablation study of different attention methods for multimodal features on the UNC val set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/379.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation study of different attention methods for multimodal features on the UNC val set." ], "reasoning": "The proposed cross-modal self-attention method achieves the highest IoU score compared to other attention methods, indicating its superior performance.", "description": "As shown in Table 2, the proposed cross-modal self-attention outperforms all other attention methods significantly." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Ablation study of different attention methods for multimodal features on the UNC val set.'\nProblem: 'Which attention method achieves the highest IoU score and what is the score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/379.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation study of different attention methods for multimodal features on the UNC val set." ], "reasoning": "By comparing the IoU scores of all attention methods listed in the table, the cross-modal self-attention method has the highest IoU score of 50.12.", "question": "Which attention method achieves the highest IoU score?", "answer": "So, the answer is {\"answer\": \"Cross-modal self-attention\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe word-pixel pair attention method outperforms the pixel attention method.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/379.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation study of different attention methods for multimodal features on the UNC val set." ], "reasoning": "By comparing the IoU scores of the word-pixel pair attention method (47.57) and the pixel attention method (47.84), it is evident that the pixel attention method has a higher score.", "statement": "The word-pixel pair attention method outperforms the pixel attention method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Ablation study on the UNC val set. The top four methods compare results of different methods for multimodal feature representations. The bottom five results show comparisons of multi-level feature fusion methods. CMSA and GF denote the proposed cross-modal self-attention and gated multi-level fusion modules. All methods use the same base model (DeepLab-101) and DenseCRF for postprocessing. ∗The numbers for [15] are slightly higher than original numbers reported in their paper which did not use DenseCRF postprocessing.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/380.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation study on the UNC val set. The top four methods compare results of different methods for multimodal feature representations. The bottom five results show comparisons of multi-level feature fusion methods. CMSA and GF denote the proposed cross-modal self-attention and gated multi-level fusion modules. All methods use the same base model (DeepLab-101) and DenseCRF for postprocessing. ∗The numbers for [15] are slightly higher than original numbers reported in their paper which did not use DenseCRF postprocessing." ], "reasoning": "The proposed crossmodal self-attentive feature based approaches achieve significantly better performance than other baselines, with CMSA-W outperforming CMSA-S for multimodal feature representation. The proposed gated multi-level fusion outperforms other multi-scale feature fusion methods.", "description": "As shown in Table 3 (top 4 rows), the proposed crossmodal self-attentive feature based approaches achieve significantly better performance than other baselines.. the word based method CMSA-W outperforms sentence based method CMSA-S for multimodal feature representation.. As presented in the bottom 5 rows in Table 3, the proposed gated multi-level fusion outperforms these other multi-scale feature fusion methods." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Ablation study on the UNC val set. The top four methods compare results of different methods for multimodal feature representations. The bottom five results show comparisons of multi-level feature fusion methods. CMSA and GF denote the proposed cross-modal self-attention and gated multi-level fusion modules. All methods use the same base model (DeepLab-101) and DenseCRF for postprocessing. ∗The numbers for [15] are slightly higher than original numbers reported in their paper which did not use DenseCRF postprocessing.'\nProblem: 'Which method achieves the highest IoU score and how does it compare to the CMSA-W method?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/380.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation study on the UNC val set. The top four methods compare results of different methods for multimodal feature representations. The bottom five results show comparisons of multi-level feature fusion methods. CMSA and GF denote the proposed cross-modal self-attention and gated multi-level fusion modules. All methods use the same base model (DeepLab-101) and DenseCRF for postprocessing. ∗The numbers for [15] are slightly higher than original numbers reported in their paper which did not use DenseCRF postprocessing." ], "reasoning": "The method CMSA+GF(Ours) achieves the highest IoU score of 58.32. Comparing this to the CMSA-W method, which has an IoU score of 50.12, CMSA+GF(Ours) outperforms CMSA-W.", "question": "Which method achieves the highest IoU score and how does it compare to the CMSA-W method?", "answer": "So, the answer is {\"answer\": \"CMSA+GF(Ours)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCMSA-W has a higher precision at 0.5 than CMSA-S.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/380.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation study on the UNC val set. The top four methods compare results of different methods for multimodal feature representations. The bottom five results show comparisons of multi-level feature fusion methods. CMSA and GF denote the proposed cross-modal self-attention and gated multi-level fusion modules. All methods use the same base model (DeepLab-101) and DenseCRF for postprocessing. ∗The numbers for [15] are slightly higher than original numbers reported in their paper which did not use DenseCRF postprocessing." ], "reasoning": "According to the table, CMSA-W has a precision at 0.5 of 51.95, while CMSA-S has a precision at 0.5 of 51.19. Therefore, CMSA-W has a higher precision at 0.5 than CMSA-S.", "statement": "CMSA-W has a higher precision at 0.5 than CMSA-S.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Experimental results of the evaluation metrics.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/381.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experimental results of the evaluation metrics." ], "reasoning": "Model 1 shows higher bias than Models 2 and 3 based on ADR measures, while Model 1 trained with MV labels performs similarly across demographic groups.", "description": "We report metric results when grouping crowd workers into 5 bins using the full range of ADR of the dataset. The ADR-based bias measures (Tab. 2) follow the expected trend (model 1 appears more biased than models 2 and 3) contrary to the protected attributes-based measures. Model 1 trained with MV labels exhibits similar performance across demographic groups. Model 2's performance is different across groups" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Experimental results of the evaluation metrics.'\nProblem: 'Which model has the highest disparity in the first metric?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/381.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experimental results of the evaluation metrics." ], "reasoning": "To determine the model with the highest disparity in the first metric, compare the disparity values (Disp.) for the first metric across all models. Model 1 has a disparity of 0.94, Model 2 has 0.72, and Model 3 has 0.", "question": "Which model has the highest disparity in the first metric?", "answer": "So, the answer is {\"answer\": \"Model 1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nModel 2 has the highest general performance in the second metric.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/381.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experimental results of the evaluation metrics." ], "reasoning": "To verify this statement, compare the general performance (Gen. perf.) values for the second metric across all models. Model 1 has 0.68, Model 2 has 0.68, and Model 3 has 1.", "statement": "Model 2 has the highest general performance in the second metric.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: DA Classification Accuracy'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/382.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: DA Classification Accuracy" ], "reasoning": "Our model outperforms state-of-the-art methods by 1.6% on SwDA and comes within 0.6% on MRDA, significantly improving over other methods.", "description": "We compare the classification accuracy of our model against several other recent methods (Table 3).. Our model outperforms state-of-the-art methods by 1.6% on SwDA, the primary dataset for this task, and comes within 0.6% on MRDA. It also beats a TF-IDF GloVe baseline (described in Section 5.2) by 16.4% and 12.2%, respectively.. The improvements that the model is able to make over the other methods are significant, however, the gains on MRDA still fall short of the state-of-the-art by 0.6%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: DA Classification Accuracy'\nProblem: 'How much does our model outperform the TF-IDF GloVe baseline on the SwDA dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/382.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: DA Classification Accuracy" ], "reasoning": "The TF-IDF GloVe baseline has an accuracy of 66.5% on SwDA, while our model has an accuracy of 82.9%. The difference is 82.9% - 66.5% = 16.4%.", "question": "How much does our model outperform the TF-IDF GloVe baseline on the SwDA dataset?", "answer": "So, the answer is {\"answer\": \"16.4%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur model achieves the highest accuracy on the MRDA dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/382.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: DA Classification Accuracy" ], "reasoning": "The table shows that Chen et al. (2018) has the highest accuracy on the MRDA dataset with 91.7%, while our model has 91.1%.", "statement": "Our model achieves the highest accuracy on the MRDA dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Present keyphrase prediction performance (F1-score) on four benchmark datasets. Dataset names are listed in the header followed by the average number of target phrases per document. The baseline One2One model is on the first row, followed by different One2Seq variants. Bold indicates best score in each column.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/383.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Present keyphrase prediction performance (F1-score) on four benchmark datasets. Dataset names are listed in the header followed by the average number of target phrases per document. The baseline One2One model is on the first row, followed by different One2Seq variants. Bold indicates best score in each column." ], "reasoning": "The average performance improves from RANDOM to APPEAR-AP, especially noticeable in F1@10 scores.", "description": "We report experimental results on four common benchmark datasets (totalling 1241 testing data points) in Table 1.. As shown in Table 1, the average performance increases from RANDOM to APPEAR-AP: a trend that becomes particularly obvious for F1@10." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Present keyphrase prediction performance (F1-score) on four benchmark datasets. Dataset names are listed in the header followed by the average number of target phrases per document. The baseline One2One model is on the first row, followed by different One2Seq variants. Bold indicates best score in each column.'\nProblem: 'Which method shows the highest average F1@10 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/383.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Present keyphrase prediction performance (F1-score) on four benchmark datasets. Dataset names are listed in the header followed by the average number of target phrases per document. The baseline One2One model is on the first row, followed by different One2Seq variants. Bold indicates best score in each column." ], "reasoning": "To find the method with the highest average F1@10 score, compare the values in the 'Average F1@10' column. APPEAR-AP has the highest value of 0.287.", "question": "Which method shows the highest average F1@10 score?", "answer": "So, the answer is {\"answer\": \"Appear-Ap\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe RANDOM method has the lowest average F1@5 score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/383.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Present keyphrase prediction performance (F1-score) on four benchmark datasets. Dataset names are listed in the header followed by the average number of target phrases per document. The baseline One2One model is on the first row, followed by different One2Seq variants. Bold indicates best score in each column." ], "reasoning": "To verify this statement, compare the values in the 'Average F1@5' column. RANDOM has an average F1@5 score of 0.305, which is not the lowest. The lowest is One2One with 0.306.", "statement": "The RANDOM method has the lowest average F1@5 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: F1 scores on present keyphrase generation of One2Seq models with different model complexities (BeamWidth=50).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/384.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F1 scores on present keyphrase generation of One2Seq models with different model complexities (BeamWidth=50)." ], "reasoning": "Table 4 shows that both BigRNN and Transformer models do not achieve higher performance than the BaseRNN model.", "description": "As shown in Table 4, BigRNN and Transformer are not able to outperform BaseRNN." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: F1 scores on present keyphrase generation of One2Seq models with different model complexities (BeamWidth=50).'\nProblem: 'Which model and parameter combination achieved the highest F@10 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/384.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F1 scores on present keyphrase generation of One2Seq models with different model complexities (BeamWidth=50)." ], "reasoning": "To find the highest F@10 score, we need to compare all F@10 values across different models and parameters. The highest F@10 score is 0.347, achieved by BaseRNN 13M with the Appear-Pre parameter.", "question": "Which model with Appear-Pre parameter achieved the highest F@10 score?", "answer": { "answer": "BaseRNN 13M" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Transformer model with 80M parameters and the Length parameter achieved the highest F@5 score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/384.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F1 scores on present keyphrase generation of One2Seq models with different model complexities (BeamWidth=50)." ], "reasoning": "The highest F@5 score for the Transformer model with 80M parameters is 0.361, achieved with the Length parameter. This is indeed the highest F@5 score in the table.", "statement": "The Transformer model with 80M parameters and the Length parameter achieved the highest F@5 score.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Per-token macro-F1 scores. For ADR, the F1 scores are for chunks via approximate matching (Nikfarjam et al., 2015; Tsai et al., 2006). ‘rand-LSTM’ is an LSTM with randomly initialized word vectors. ‘ELMo-LSTM’ is an LSTM initialized with pretrained ELMo embeddings. ‘HB’ signals sparse, high-dimensional feature representations based on hand-built feature functions. The mean values and standard deviations are calculated using F1 scores of three runs of repeated experiments, as discussed in section 3. Statistical significance notation for the last two rows (two top-performing models) is ∗: p<0.05; ∗∗: p<0.01; ∗∗∗: p<0.001.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/385.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Per-token macro-F1 scores. For ADR, the F1 scores are for chunks via approximate matching (Nikfarjam et al., 2015; Tsai et al., 2006). ‘rand-LSTM’ is an LSTM with randomly initialized word vectors. ‘ELMo-LSTM’ is an LSTM initialized with pretrained ELMo embeddings. ‘HB’ signals sparse, high-dimensional feature representations based on hand-built feature functions. The mean values and standard deviations are calculated using F1 scores of three runs of repeated experiments, as discussed in section 3. Statistical significance notation for the last two rows (two top-performing models) is ∗: p<0.05; ∗∗: p<0.01; ∗∗∗: p<0.001." ], "reasoning": "The combined model ELMo-LSTM-CRF-HB shows superior performance across all tasks compared to other models, as evident in table 2.", "description": "The power these features bring to the model, beyond what is captured in the ELMo-LSTM representations, is evident in table 2, column 1.. The results in table 2, column 2, clearly favor the combined model ELMo-LSTM-CRF-HB that uses both these features and the ELMo-LSTM.. Our test-set results, given in table 2, column 3, show the power of our combined model ELMo-LSTM-CRF-HB.. We report results for the standard test set. The power of the combined model ELMo-LSTM-CRF-HB is again evident in the results in table 2, column 4.. Our results for this experiment are given in table 2, column 5, and point to the superiority of our combined model ELMo-LSTM-CRF-HB.. Our discussion seeks to show that the combined model ELMo-LSTM-CRF-HB, which shows superior performance in all tasks (table 2), is making meaningful use of both kinds of features (hand-built and ELMo) and both of the major model components (LSTM and CRF).. We note also that, where the performance of the two base models is very similar (table 2), the potential scores in the combined model are also more similar." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Per-token macro-F1 scores. For ADR, the F1 scores are for chunks via approximate matching (Nikfarjam et al., 2015; Tsai et al., 2006). ‘rand-LSTM’ is an LSTM with randomly initialized word vectors. ‘ELMo-LSTM’ is an LSTM initialized with pretrained ELMo embeddings. ‘HB’ signals sparse, high-dimensional feature representations based on hand-built feature functions. The mean values and standard deviations are calculated using F1 scores of three runs of repeated experiments, as discussed in section 3. Statistical significance notation for the last two rows (two top-performing models) is ∗: p<0.05; ∗∗: p<0.01; ∗∗∗: p<0.001.'\nProblem: 'What is the performance of the ELMo-LSTM-CRF-HB model in the Prescription task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/385.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Per-token macro-F1 scores. For ADR, the F1 scores are for chunks via approximate matching (Nikfarjam et al., 2015; Tsai et al., 2006). ‘rand-LSTM’ is an LSTM with randomly initialized word vectors. ‘ELMo-LSTM’ is an LSTM initialized with pretrained ELMo embeddings. ‘HB’ signals sparse, high-dimensional feature representations based on hand-built feature functions. The mean values and standard deviations are calculated using F1 scores of three runs of repeated experiments, as discussed in section 3. Statistical significance notation for the last two rows (two top-performing models) is ∗: p<0.05; ∗∗: p<0.01; ∗∗∗: p<0.001." ], "reasoning": "The Prescription task is listed under the 'Prescription' column in table 2. The ELMo-LSTM-CRF-HB model's performance is highlighted in yellow, showing a score of 82.0 ± 0.03.", "question": "What is the performance of the ELMo-LSTM-CRF-HB model in the Prescription task?", "answer": { "answer": "82.0 ± 0.03" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ELMo-LSTM-CRF-HB model outperforms the rand-LSTM-CRF model in the Diagnosis task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/385.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Per-token macro-F1 scores. For ADR, the F1 scores are for chunks via approximate matching (Nikfarjam et al., 2015; Tsai et al., 2006). ‘rand-LSTM’ is an LSTM with randomly initialized word vectors. ‘ELMo-LSTM’ is an LSTM initialized with pretrained ELMo embeddings. ‘HB’ signals sparse, high-dimensional feature representations based on hand-built feature functions. The mean values and standard deviations are calculated using F1 scores of three runs of repeated experiments, as discussed in section 3. Statistical significance notation for the last two rows (two top-performing models) is ∗: p<0.05; ∗∗: p<0.01; ∗∗∗: p<0.001." ], "reasoning": "In the Diagnosis task, the ELMo-LSTM-CRF-HB model has a score of 85.3 ± 0.24, while the rand-LSTM-CRF model has a score of 77.3 ± 0.05, indicating that the ELMo-LSTM-CRF-HB model outperforms the rand-LSTM-CRF model.", "statement": "The ELMo-LSTM-CRF-HB model outperforms the rand-LSTM-CRF model in the Diagnosis task.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Relative F1 score improvements of different labels. For each label, we give the number of supporting examples (Support), the F1 score of our combined model, and the relative improvements over the HB-CRF model. The F1 scores of minor labels suffer from insufficient training data, and thus have lower values. However, the combined model shows the largest relative improvements in these categories. ADR results are shown in table A4.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/386.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Relative F1 score improvements of different labels. For each label, we give the number of supporting examples (Support), the F1 score of our combined model, and the relative improvements over the HB-CRF model. The F1 scores of minor labels suffer from insufficient training data, and thus have lower values. However, the combined model shows the largest relative improvements in these categories. ADR results are shown in table A4." ], "reasoning": "The combined model shows the largest improvements in F1 scores for the smallest categories across all relevant datasets.", "description": "Table 3 suggests that the combined model does make progress here, in that the largest gains, across all relevant datasets, tend to be for the smallest categories." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Relative F1 score improvements of different labels. For each label, we give the number of supporting examples (Support), the F1 score of our combined model, and the relative improvements over the HB-CRF model. The F1 scores of minor labels suffer from insufficient training data, and thus have lower values. However, the combined model shows the largest relative improvements in these categories. ADR results are shown in table A4.'\nProblem: 'Which category in the Prescription Reasons dataset shows the highest improvement in F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/386.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Relative F1 score improvements of different labels. For each label, we give the number of supporting examples (Support), the F1 score of our combined model, and the relative improvements over the HB-CRF model. The F1 scores of minor labels suffer from insufficient training data, and thus have lower values. However, the combined model shows the largest relative improvements in these categories. ADR results are shown in table A4." ], "reasoning": "By examining the Prescription Reasons Improvement column, the 'Contraindicated-for' category shows the highest improvement with 103.5%.", "question": "Which category in the Prescription Reasons dataset shows the highest improvement in F1 score?", "answer": "So, the answer is {\"answer\": \"Contraindicated-for\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'Disease' category in the Diagnosis Detection dataset has the highest F1 score improvement.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/386.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Relative F1 score improvements of different labels. For each label, we give the number of supporting examples (Support), the F1 score of our combined model, and the relative improvements over the HB-CRF model. The F1 scores of minor labels suffer from insufficient training data, and thus have lower values. However, the combined model shows the largest relative improvements in these categories. ADR results are shown in table A4." ], "reasoning": "By checking the Diagnosis Detection Improvement column, the 'Chemical' category has the highest improvement with 6.7%, not the 'Disease' category.", "statement": "The 'Disease' category in the Diagnosis Detection dataset has the highest F1 score improvement.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Corpus statistics and label distributions of Friends and EmotionPush datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/387.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Corpus statistics and label distributions of Friends and EmotionPush datasets." ], "reasoning": "Both Friends and EmotionPush datasets have an imbalanced class distribution with Neutral being the most common class, followed by Joy, Sadness, and Anger.", "description": "we describe data and label distribution of Friends and EmotionPush datasets. In terms of label distribution for both datasets, Neutral are the most common class, followed by Joy, Sadness, and Anger. Both datasets have imbalanced class distribution, and especially the ratio of Sadness and Anger is very small. For instance, they account for only 3.4% and 5.2%, respectively in the Friends dataset. In the case of EmotionPush, Anger label accounts for less than 1% of the training set." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Corpus statistics and label distributions of Friends and EmotionPush datasets.'\nProblem: 'What is the percentage of the Anger label in the EmotionPush training set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/387.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Corpus statistics and label distributions of Friends and EmotionPush datasets." ], "reasoning": "The table shows the label distribution for the EmotionPush training set, where the Anger label accounts for 0.9%.", "question": "What is the percentage of the Anger label in the EmotionPush training set?", "answer": "So, the answer is {\"answer\": \"0.9%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Sadness label accounts for 3.4% in the Friends test set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/387.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Corpus statistics and label distributions of Friends and EmotionPush datasets." ], "reasoning": "The table shows that the Sadness label in the Friends test set accounts for 3.7%, not 3.4%.", "statement": "The Sadness label accounts for 3.4% in the Friends test set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison between Essentia and FSA baseline on paraphrase extraction'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/388.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison between Essentia and FSA baseline on paraphrase extraction" ], "reasoning": "ESSENTIA extracts significantly more paraphrases than FSA, improving recall by 460% for Snips and 247% for HotelQA, despite FSA having higher precision.", "description": "Table 1 compares the performance of ESSENTIA with the FSA baseline for paraphrase mining. Specifically, we show the number of phrase pairs extracted by ESSENTIA and FSA from both datasets (\"# of extracted pairs\" column), number of valid paraphrases within these pairs (\"# of valid pairs\" column), and precision (\"Precision\" column). Although FSA has higher precision due to conservative sentence alignment, ESSENTIA extracts significantly more paraphrases, improving the recall by 460% (Snips) and 247% (HotelQA) over the baseline." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparison between Essentia and FSA baseline on paraphrase extraction'\nProblem: 'What is the precision of ESSENTIA for the HotelQA dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/388.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison between Essentia and FSA baseline on paraphrase extraction" ], "reasoning": "To find the precision of ESSENTIA for the HotelQA dataset, look at the row where the dataset is HotelQA and the method is ESSENTIA. The precision value in this row is 28.91%.", "question": "What is the precision of ESSENTIA for the HotelQA dataset?", "answer": "So, the answer is {\"answer\": \"28.91%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nESSENTIA has higher precision than FSA for the Snips dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/388.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison between Essentia and FSA baseline on paraphrase extraction" ], "reasoning": "To verify this statement, compare the precision values of ESSENTIA and FSA for the Snips dataset. ESSENTIA has a precision of 48.55%, while FSA has a precision of 83.33%. Since 48.55% is lower than 83.33%, the statement is refuted.", "statement": "ESSENTIA has higher precision than FSA for the Snips dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Classification test scores for classifying R vs U in the BR, US, and combined BR + US dataset. The baseline score is 50%.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/389.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Classification test scores for classifying R vs U in the BR, US, and combined BR + US dataset. The baseline score is 50%." ], "reasoning": "The BR dataset achieved an accuracy of 85% using 60 features, while the US dataset achieved 72% accuracy using 49 features. The combined dataset achieved 70% accuracy using 18 features.", "description": "The classification of BR and US datasets used the 60 and 49 most relevant features, respectively. The test accuracy for the BR and US datasets were 85% and 72%, respectively, with a baseline score of 50%. The combined dataset used a reduced set of features consisting of an intersection of the most relevant features observed in both BR and US, achieving a test score of 70% using only 18 features.. classes. We have shown that these features may be used to classify news articles in a language other than English." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Classification test scores for classifying R vs U in the BR, US, and combined BR + US dataset. The baseline score is 50%.'\nProblem: 'What is the test accuracy of the combined BR and US dataset using the intersection of the most relevant features?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/389.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Classification test scores for classifying R vs U in the BR, US, and combined BR + US dataset. The baseline score is 50%." ], "reasoning": "The combined dataset used a reduced set of features consisting of an intersection of the most relevant features observed in both BR and US, achieving a test score of 70%.", "question": "What is the test accuracy of the combined BR and US dataset using the intersection of the most relevant features?", "answer": "So, the answer is {\"answer\": \"70%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BR dataset achieved a higher test accuracy than the US dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/389.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Classification test scores for classifying R vs U in the BR, US, and combined BR + US dataset. The baseline score is 50%." ], "reasoning": "The table shows that the BR dataset had a test accuracy of 85%, while the US dataset had a test accuracy of 72%, indicating that the BR dataset achieved a higher test accuracy.", "statement": "The BR dataset achieved a higher test accuracy than the US dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Storage size(MB) comparison of full archive sync between geth and ethanos from 7M+1 to 8M block.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/390.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Storage size(MB) comparison of full archive sync between geth and ethanos from 7M+1 to 8M block." ], "reasoning": "Ethanos reduces the total blockchain size by about 16GB compared to geth, mainly by reducing the size of Trie nodes by about 18GB, while slightly increasing the size of Headers, Bodies, and Receipts.", "description": "Table 1 gives full archive sync size of each data type for geth and ethanos at the 8M block. Ethanos reduces the total blockchain size about 16GB compared to geth. Especially, it reduces the size of Trie nodes about 18GB, because ethanos only maintains the state trie only with active accounts during a month which are only 10% of the total accounts. However, hash field of bloom filter in block header slightly increases Headers, and restore transactions increment Bodies and Receipts about 1.6GB. As a result, ethanos reduced full archive node by about 8% compared to ethereum." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Storage size(MB) comparison of full archive sync between geth and ethanos from 7M+1 to 8M block.'\nProblem: 'How much does ethanos reduce the size of Trie nodes compared to geth?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/390.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Storage size(MB) comparison of full archive sync between geth and ethanos from 7M+1 to 8M block." ], "reasoning": "The table shows that the size of Trie nodes for geth is 183,100.00 and for ethanos is 165,140.00. The difference is 183,100.00 - 165,140.00 = 17,960.00.", "question": "How much does ethanos reduce the size of Trie nodes compared to geth?", "answer": "So, the answer is {\"answer\": \"17,960.00\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nEthanos reduces the total blockchain size by about 16GB compared to geth.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/390.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Storage size(MB) comparison of full archive sync between geth and ethanos from 7M+1 to 8M block." ], "reasoning": "The table shows that the total size for geth is 202,727.47 and for ethanos is 186,496.20. The difference is 202,727.47 - 186,496.20 = 16,231.27, which is approximately 16GB.", "statement": "Ethanos reduces the total blockchain size by about 16GB compared to geth.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Storage size(MB) comparison of fast sync and compact sync between geth and ethanos from 7M+1 to 7M+864K (5th checkpoint) block.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/391.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Storage size(MB) comparison of fast sync and compact sync between geth and ethanos from 7M+1 to 7M+864K (5th checkpoint) block." ], "reasoning": "Ethanos significantly reduces the Trie nodes size in both fast sync and compact sync modes, but increases the transaction size.", "description": "Table 2 shows storage size of geth and ethanos for each bootstrapping mode. Ethanos reduces Trie nodes size of geth from 2,220MB to 804.79MB; however, it increases the transaction size (Bodies, Receipts) with block headers (Headers). Therefore, ethanos did not reduce the size of fast sync much, but it significantly reduced compact sync from 2,592.54MB of geth to 1,206.97MB, which is less than 50%. Our result shows that the trie node size of each checkpoint is about 800MB," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Storage size(MB) comparison of fast sync and compact sync between geth and ethanos from 7M+1 to 7M+864K (5th checkpoint) block.'\nProblem: 'What is the total storage size reduction achieved by ethanos in compact sync mode compared to geth?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/391.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Storage size(MB) comparison of fast sync and compact sync between geth and ethanos from 7M+1 to 7M+864K (5th checkpoint) block." ], "reasoning": "The total storage size for compact sync geth is 2,592.54MB, and for compact sync ethanos is 1,206.97MB. The reduction is 2,592.54MB - 1,206.97MB.", "question": "What is the total storage size reduction achieved by ethanos in compact sync mode compared to geth?", "answer": { "answer": "1,385.57" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nEthanos reduces the Trie nodes size in fast sync mode to less than 1,000MB.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/391.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Storage size(MB) comparison of fast sync and compact sync between geth and ethanos from 7M+1 to 7M+864K (5th checkpoint) block." ], "reasoning": "The table shows that the Trie nodes size for fast sync ethanos is 804.79MB, which is less than 1,000MB.", "statement": "Ethanos reduces the Trie nodes size in fast sync mode to less than 1,000MB.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Experiment results. Note that sequence length for word level system is shorter, thus we search by ratio 0.3 which shares similar chrF1 with search on subword level system with ratio 0.4. An ideal adversarial sample generation must achieve degradation with respect to higher semantic similarity with origin inputs (HE).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/392.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Experiment results. Note that sequence length for word level system is shorter, thus we search by ratio 0.3 which shares similar chrF1 with search on subword level system with ratio 0.4. An ideal adversarial sample generation must achieve degradation with respect to higher semantic similarity with origin inputs (HE)." ], "reasoning": "The model can generate adversarial samples with stable semantics across different models using the same training settings.", "description": "our model can stably generate adversarial samples without significant change in semantics with the same training setting for different models," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Experiment results. Note that sequence length for word level system is shorter, thus we search by ratio 0.3 which shares similar chrF1 with search on subword level system with ratio 0.4. An ideal adversarial sample generation must achieve degradation with respect to higher semantic similarity with origin inputs (HE).'\nProblem: 'What is the highest Avg MT02-08 RD score for the 'Ours' model across all model types?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/392.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Experiment results. Note that sequence length for word level system is shorter, thus we search by ratio 0.3 which shares similar chrF1 with search on subword level system with ratio 0.4. An ideal adversarial sample generation must achieve degradation with respect to higher semantic similarity with origin inputs (HE)." ], "reasoning": "To find the highest Avg MT02-08 RD score for the 'Ours' model, we need to compare the RD scores for 'Ours' across all model types: Transformer-word (0.163), Transformer-BPE (0.277), and RNN-search-BPE (0.208). The highest score is 0.277.", "question": "What is the highest Avg MT02-08 RD score for the 'Ours' model across all model types?", "answer": "So, the answer is {\"answer\": \"0.277\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'Ours' model has the highest Avg MT02-08 HE score in the Transformer-BPE category.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/392.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Experiment results. Note that sequence length for word level system is shorter, thus we search by ratio 0.3 which shares similar chrF1 with search on subword level system with ratio 0.4. An ideal adversarial sample generation must achieve degradation with respect to higher semantic similarity with origin inputs (HE)." ], "reasoning": "In the Transformer-BPE category, the Avg MT02-08 HE scores are: Search (0.2) - 34.27, Search (0.4) - 27.27, and Ours - 31.35. The highest score is 34.27, which is not the 'Ours' model.", "statement": "The 'Ours' model has the highest Avg MT02-08 HE score in the Transformer-BPE category.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Sentence-level (left) and fragment-level experiments (right). All-propaganda is a baseline that always output the propaganda class.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/393.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Sentence-level (left) and fragment-level experiments (right). All-propaganda is a baseline that always output the propaganda class." ], "reasoning": "The multi-granularity network model improves performance on both the FLC and SLC tasks, particularly in recall and F1 score for SLC.", "description": "The left side of Table 1 shows the performance for the three baselines and for our multi-granularity network on the FLC task.. Table 1 (right) shows that using additional information from the sentence-level for the token-level classification (BERT-Granularity) yields small improvements. The multi-granularity models outperform all baselines thanks to their higher precision. This shows the effect of the model excluding sentences that it determined to be non-propagandistic from being considered for token-level classification.. The right side of Table 1 shows the results for the SLC task. We apply our multi-granularity network model to the sentence-level classification task to see its effect on low granularity when we train the model with a high granularity task. Interestingly, it yields huge performance improvements on the sentence-level classification result. Compared to the BERT baseline, it increases the recall by 8.42%, resulting in a 3.24% increase of the F1 score." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Sentence-level (left) and fragment-level experiments (right). All-propaganda is a baseline that always output the propaganda class.'\nProblem: 'Which model shows the highest recall for the SLC task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/393.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Sentence-level (left) and fragment-level experiments (right). All-propaganda is a baseline that always output the propaganda class." ], "reasoning": "To find the model with the highest recall for the SLC task, look at the 'Task SLC R' column and identify the highest value.", "question": "Which model shows the highest recall for the SLC task?", "answer": { "answer": "ReLU" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BERT model has the highest precision for the FLC task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/393.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Sentence-level (left) and fragment-level experiments (right). All-propaganda is a baseline that always output the propaganda class." ], "reasoning": "To verify this statement, check the 'Task FLC P' column and compare the precision values of all models. The BERT model has a precision of 21.48, which is not the highest.", "statement": "The BERT model has the highest precision for the FLC task.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results on CQA dev-random-split with CoS-E used during training.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/394.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on CQA dev-random-split with CoS-E used during training." ], "reasoning": "Adding CoS-E-open-ended explanations to the BERT baseline improves accuracy by 1.7%, and using CAGE-reasoning further increases accuracy to 72.6%.", "description": "Table 2 shows results that compare a BERT baseline that uses only the CQA inputs and the same architecture but trained using inputs that contain explanations from CoS-E during training. The BERT baseline model reaches 64% accuracy and adding open-ended human explanations (CoS-E-open-ended) alongside the questions during training results in a 2% boost in accuracy.. In Table 2, using CAGE-reasoning at both train and validation resulted in an accuracy of 72%, but Table 4 shows that if CAGE-reasoning truly captured all information provided in CoS-E-openended, performance would be 90%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results on CQA dev-random-split with CoS-E used during training.'\nProblem: 'What is the accuracy improvement when using CAGE-reasoning compared to the BERT baseline?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/394.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on CQA dev-random-split with CoS-E used during training." ], "reasoning": "The BERT baseline has an accuracy of 63.8%, while CAGE-reasoning has an accuracy of 72.6%. The improvement is calculated as 72.6% - 63.8%.", "question": "What is the accuracy improvement when using CAGE-reasoning compared to the BERT baseline?", "answer": "So, the answer is {\"answer\": \"8.8%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUsing CoS-E-open-ended explanations results in a higher accuracy than using CAGE-reasoning.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/394.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on CQA dev-random-split with CoS-E used during training." ], "reasoning": "The table shows that CoS-E-open-ended has an accuracy of 65.5%, while CAGE-reasoning has an accuracy of 72.6%. Therefore, the statement is incorrect.", "statement": "Using CoS-E-open-ended explanations results in a higher accuracy than using CAGE-reasoning.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Test accuracy on CQA v1.0. The addition of CoS-E-open-ended during training dramatically improves performance. Replacing CoS-E during training with CAGE reasoning during both training and inference leads to an absolute gain of 10% over the previous state-of-the-art.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/395.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Test accuracy on CQA v1.0. The addition of CoS-E-open-ended during training dramatically improves performance. Replacing CoS-E during training with CAGE reasoning during both training and inference leads to an absolute gain of 10% over the previous state-of-the-art." ], "reasoning": "The table shows the accuracy results of different methods on the CQA test split, highlighting the performance of CoS-E-openended and CAGE-reasoning models.", "description": "Table 3 shows the results obtained on the CQA test split. We report our two best models that represent using human explanations (CoS-E-openended) for training only and using language model explanations (CAGE-reasoning) during both train and test." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Test accuracy on CQA v1.0. The addition of CoS-E-open-ended during training dramatically improves performance. Replacing CoS-E during training with CAGE reasoning during both training and inference leads to an absolute gain of 10% over the previous state-of-the-art.'\nProblem: 'Which method achieved the highest accuracy among the models using human explanations and language model explanations?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/395.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Test accuracy on CQA v1.0. The addition of CoS-E-open-ended during training dramatically improves performance. Replacing CoS-E during training with CAGE reasoning during both training and inference leads to an absolute gain of 10% over the previous state-of-the-art." ], "reasoning": "To find the highest accuracy among the models using human explanations and language model explanations, compare the accuracy percentages of CoS-E-openended (60.2%) and CAGE-reasoning (64.7%).", "question": "Which method achieved the highest accuracy among the models using human explanations and language model explanations?", "answer": "So, the answer is {\"answer\": \"CAGE-reasoning\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe CAGE-reasoning method achieved an accuracy of 64.7%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/395.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Test accuracy on CQA v1.0. The addition of CoS-E-open-ended during training dramatically improves performance. Replacing CoS-E during training with CAGE reasoning during both training and inference leads to an absolute gain of 10% over the previous state-of-the-art." ], "reasoning": "The table shows that the CAGE-reasoning method has an accuracy of 64.7%, which matches the statement.", "statement": "The CAGE-reasoning method achieved an accuracy of 64.7%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Oracle results on CQA dev-random-split using different variants of CoS-E for both training and validation. * indicates CoS-E-open-ended used during both training and validation to contrast with CoS-E-open-ended used only during training in Table 2.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/396.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Oracle results on CQA dev-random-split using different variants of CoS-E for both training and validation. * indicates CoS-E-open-ended used during both training and validation to contrast with CoS-E-open-ended used only during training in Table 2." ], "reasoning": "Using explanations during both training and validation improves accuracy over the BERT baseline, as shown by the higher performance in Table 4.", "description": "Table 4 also contains results that use only the explanation and exclude the original question from CQA denoted by 'w/o question'. These variants also use explanation during both train and validation.. We observe that even using these limited kind of explanations improves over the BERT baseline in Table 4, which suggests that the explanations are providing useful information beyond just mentioning the correct or incorrect answers.. In Table 2, using CAGE-reasoning at both train and validation resulted in an accuracy of 72%, but Table 4 shows that if CAGE-reasoning truly captured all information provided in CoS-E-openended, performance would be 90%." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Oracle results on CQA dev-random-split using different variants of CoS-E for both training and validation. * indicates CoS-E-open-ended used during both training and validation to contrast with CoS-E-open-ended used only during training in Table 2.'\nProblem: 'What is the accuracy of the CoS-E-openended method without the original question?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/396.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Oracle results on CQA dev-random-split using different variants of CoS-E for both training and validation. * indicates CoS-E-open-ended used during both training and validation to contrast with CoS-E-open-ended used only during training in Table 2." ], "reasoning": "The table shows the accuracy of different methods, and the CoS-E-openended method without the original question has an accuracy of 84.5%.", "question": "What is the accuracy of the CoS-E-openended method without the original question?", "answer": "So, the answer is {\"answer\": \"84.5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe CoS-E-selected method has an accuracy of 70.0%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/396.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Oracle results on CQA dev-random-split using different variants of CoS-E for both training and validation. * indicates CoS-E-open-ended used during both training and validation to contrast with CoS-E-open-ended used only during training in Table 2." ], "reasoning": "The table lists the accuracy of the CoS-E-selected method as 70.0%, which matches the statement.", "statement": "The CoS-E-selected method has an accuracy of 70.0%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Results for explanation transfer from CQA to out-of-domain SWAG and Sotry Cloze tasks.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/397.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results for explanation transfer from CQA to out-of-domain SWAG and Sotry Cloze tasks." ], "reasoning": "Adding explanations to the BERT baseline resulted in a slight performance decrease of less than 0.6% for both SWAG and Story Cloze tasks.", "description": "Table 6 shows the results obtained by the BERT baseline without explanations and using our transferred explanations from CQA to SWAG and Story Cloze. We observed that adding explanations led to a very small decrease (< 0.6%) in the performance compared to the baseline for both tasks." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Results for explanation transfer from CQA to out-of-domain SWAG and Sotry Cloze tasks.'\nProblem: 'What was the performance decrease in the SWAG task when explanations were added to the BERT baseline?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/397.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results for explanation transfer from CQA to out-of-domain SWAG and Sotry Cloze tasks." ], "reasoning": "The BERT baseline performance for SWAG was 84.2, and with explanations, it was 83.6. The decrease is 84.2 - 83.6 = 0.6.", "question": "What was the performance decrease in the SWAG task when explanations were added to the BERT baseline?", "answer": { "answer": "0.6" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAdding explanations to the BERT baseline improved performance in the Story Cloze task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/397.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results for explanation transfer from CQA to out-of-domain SWAG and Sotry Cloze tasks." ], "reasoning": "The table shows that the BERT baseline performance for Story Cloze was 89.8, and with explanations, it was 89.5, indicating a decrease rather than an improvement.", "statement": "Adding explanations to the BERT baseline improved performance in the Story Cloze task.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: F1 and ensembled F1 score for citation needed detection training on the FA split and testing on the LQN split of Redi et al. (2019). The FA split contains statements with citations from featured articles and the LQN split consists of statements which were flagged as not having a citation but needing one. Listed are the mean, standard deviation, and ensembled results across 15 seeds (eP, eR, and eF1). Bold indicates best performance, underline indicates second best. *The reported value is from rerunning their released model on the test dataset. The value in brackets is the value reported in the original paper.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/398.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: F1 and ensembled F1 score for citation needed detection training on the FA split and testing on the LQN split of Redi et al. (2019). The FA split contains statements with citations from featured articles and the LQN split consists of statements which were flagged as not having a citation but needing one. Listed are the mean, standard deviation, and ensembled results across 15 seeds (eP, eR, and eF1). Bold indicates best performance, underline indicates second best. *The reported value is from rerunning their released model on the test dataset. The value in brackets is the value reported in the original paper." ], "reasoning": "The BERT model outperforms the state-of-the-art model by 6 F1 points, and further improvements are seen with PU learning and PUC, with the best model being BERT + PUC with an F1 score of 0.826.", "description": "Our results for citation needed detection are given in Table 1. The vanilla BERT model already significantly outperforms the state of the art model from Redi et al. (2019) (a GRU network with global attention) by 6 F1 points. We saw further gains in performance with PU learning, as well as when using PUC. Additionally, the models using PU learning had lower variance, indicating more consistent performance across runs. The best performing model we saw was the one trained using PUC with an F1 score of 0.826." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: F1 and ensembled F1 score for citation needed detection training on the FA split and testing on the LQN split of Redi et al. (2019). The FA split contains statements with citations from featured articles and the LQN split consists of statements which were flagged as not having a citation but needing one. Listed are the mean, standard deviation, and ensembled results across 15 seeds (eP, eR, and eF1). Bold indicates best performance, underline indicates second best. *The reported value is from rerunning their released model on the test dataset. The value in brackets is the value reported in the original paper.'\nProblem: 'Which model had the highest eR score and what was its F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/398.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: F1 and ensembled F1 score for citation needed detection training on the FA split and testing on the LQN split of Redi et al. (2019). The FA split contains statements with citations from featured articles and the LQN split consists of statements which were flagged as not having a citation but needing one. Listed are the mean, standard deviation, and ensembled results across 15 seeds (eP, eR, and eF1). Bold indicates best performance, underline indicates second best. *The reported value is from rerunning their released model on the test dataset. The value in brackets is the value reported in the original paper." ], "reasoning": "The BERT + PUC model had the highest eR score of 0.871, and its F1 score was 0.818.", "question": "Which model had the highest eR score?", "answer": "So, the answer is {\"answer\": \"BERT + PUC\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BERT model with PU learning had a higher F1 score than the vanilla BERT model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/398.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: F1 and ensembled F1 score for citation needed detection training on the FA split and testing on the LQN split of Redi et al. (2019). The FA split contains statements with citations from featured articles and the LQN split consists of statements which were flagged as not having a citation but needing one. Listed are the mean, standard deviation, and ensembled results across 15 seeds (eP, eR, and eF1). Bold indicates best performance, underline indicates second best. *The reported value is from rerunning their released model on the test dataset. The value in brackets is the value reported in the original paper." ], "reasoning": "The table shows that the BERT model with PU learning had an F1 score of 0.814, while the vanilla BERT model had an F1 score of 0.810.", "statement": "The BERT model with PU learning had a higher F1 score than the vanilla BERT model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Properties of the training and test set: the number of synsets, the maximum depth of the hierarchy counted from (and including) the top synset of the domain, the number of basic level concepts, and the inter-rater agreement (κ).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/399.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Properties of the training and test set: the number of synsets, the maximum depth of the hierarchy counted from (and including) the top synset of the domain, the number of basic level concepts, and the inter-rater agreement (κ)." ], "reasoning": "The inter-rater agreement (κ) is higher for tools and fruit than for the musical domain.", "description": "Table 1 lists the properties of the training and test set including the inter-rater agreement (κ). The agreement is substantial, with some variation between the domains: κ is higher for tools and fruit than for the musical domain." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Properties of the training and test set: the number of synsets, the maximum depth of the hierarchy counted from (and including) the top synset of the domain, the number of basic level concepts, and the inter-rater agreement (κ).'\nProblem: 'Which domain has the highest inter-rater agreement (κ)?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/399.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Properties of the training and test set: the number of synsets, the maximum depth of the hierarchy counted from (and including) the top synset of the domain, the number of basic level concepts, and the inter-rater agreement (κ)." ], "reasoning": "By comparing the κ values for each domain, the domain with the highest κ value is identified.", "question": "Which domain has the highest inter-rater agreement (κ)?", "answer": "So, the answer is {\"answer\": \"Edible fruit\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe inter-rater agreement (κ) for the musical domain is the lowest among the listed domains.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/399.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Properties of the training and test set: the number of synsets, the maximum depth of the hierarchy counted from (and including) the top synset of the domain, the number of basic level concepts, and the inter-rater agreement (κ)." ], "reasoning": "By comparing the κ values for each domain, the musical domain has the lowest κ value of 0.64.", "statement": "The inter-rater agreement (κ) for the musical domain is the lowest among the listed domains.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Accuracy scores × 1000 for different train and test dataset combinations'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/200.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy scores × 1000 for different train and test dataset combinations" ], "reasoning": "The table shows that monolingual settings yield high-quality results, but smaller training datasets lead to lower scores. Cross-lingual settings improve quality, especially when combining datasets.", "description": "averaged. The Table 3 presents the results of these experiments. The Table 3 shows, that: 1. in monolingual setting, we can get highquality results. The scores are significantly lower than the scores of the same model on the standard dataset, due to the smaller sizes of the training datasets. Nevertheless, one can see, that our approach to word stress detection applies not only to the Russian language data, but also to the data in the Belarusian and Ukrainian languages; 2. cross-lingual setting (1): the Belarusian training dataset, being the smallest one among the three datasets, is not a good source for training word stress detection models in other languages, while the Ukrainian dataset stands. out as a good source for training word stress detection systems both for the Russian and Belarusian languages; 3. cross-lingual setting (2): adding one or two datasets to the other languages improves the quality. For example, around 10% of accuracy is gained by adding the Russian training dataset to the Belarusian training dataset, while testing on Belarusian." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Accuracy scores × 1000 for different train and test dataset combinations'\nProblem: 'Which training dataset combination yields the highest score when tested on the Russian dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/200.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy scores × 1000 for different train and test dataset combinations" ], "reasoning": "To find the highest score for the Russian test dataset, look at the 'test dataset Russian' column and identify the highest value. The highest score is 760, achieved by the 'Russian, Ukrainian, Belarusian' training dataset.", "question": "Which training dataset combination yields the highest score when tested on the Russian dataset?", "answer": "So, the answer is {\"answer\": \"Russian, Ukrainian, Belarusian\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Ukrainian training dataset yields the highest score when tested on the Ukrainian dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/200.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy scores × 1000 for different train and test dataset combinations" ], "reasoning": "To verify this statement, look at the 'test dataset Ukrainian' column and identify the highest value. The highest score is 701, achieved by the 'Ukrainian, Belarusian' training dataset, not the Ukrainian training dataset alone.", "statement": "The Ukrainian training dataset yields the highest score when tested on the Ukrainian dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: BLEU and exact-match scores over held-out test set'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/201.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: BLEU and exact-match scores over held-out test set" ], "reasoning": "The neural generator trained on both gold and silver data achieves the highest BLEU score and exact match accuracy, outperforming other models.", "description": "We compare the performance of our neural generator when trained on either gold, silver, or gold and silver data (Table 1). Generation quality is primarily evaluated with BLEU. Semi-supervised training. leads to an 11 BLEU point improvement. compar. train. In addition to BLEU, we also report exact match accuracy on the overlapping subset. Results show that our neural models outperform the grammar-based generator by a large margin." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: BLEU and exact-match scores over held-out test set'\nProblem: 'Which model achieves the highest exact match accuracy?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/201.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: BLEU and exact-match scores over held-out test set" ], "reasoning": "By comparing the exact match accuracy values in the table, the Neural MRS (gold + silver) model has the highest exact match accuracy of 32.07%.", "question": "Which model achieves the highest exact match accuracy?", "answer": "So, the answer is {\"answer\": \"Neural MRS (gold + silver)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ACE (ERG) model has the lowest BLEU score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/201.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: BLEU and exact-match scores over held-out test set" ], "reasoning": "The table shows that the ACE (ERG) model has a BLEU score of 62.05, which is lower than the BLEU scores of the other models listed.", "statement": "The ACE (ERG) model has the lowest BLEU score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Hyper-parameter values.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/202.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Hyper-parameter values." ], "reasoning": "The embedding sizes are set to 50, the hidden size of LSTM models to 200, and dropout is applied with a rate of 0.5. SGD is used for optimization with an initial learning rate of 0.015 and a decay rate of 0.05.", "description": "Table 3 shows the values of hyper-parameters for our models,. In particular, the embedding sizes are set to 50 and the hidden size of LSTM models to 200. Dropout (Srivastava et al., 2014) is applied to both word and character embeddings with a rate of 0.5. Stochastic gradient descent (SGD) is used for optimization, with an initial learning rate of 0.015 and a decay rate of 0.05." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Hyper-parameter values.'\nProblem: 'What is the regularization parameter λ used in the model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/202.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Hyper-parameter values." ], "reasoning": "The table lists the regularization parameter λ under the 'Parameter bigram emb size' column with a value of 1e-8.", "question": "What is the regularization parameter λ used in the model?", "answer": "So, the answer is {\"answer\": \"1e-8\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LSTM layer size is set to 2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/202.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Hyper-parameter values." ], "reasoning": "The table shows that the LSTM layer size is set to 1, not 2.", "statement": "The LSTM layer size is set to 2.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Development results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/203.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Development results." ], "reasoning": "The lattice LSTM-CRF model achieves the highest F1-score of 71.62%, outperforming both word-based and character-based methods.", "description": "As shown in Table 4, without using word segmentation, a characterbased LSTM-CRF model gives a development F1score of 62.47%. Adding character-bigram and softword representations as described in Section 3.1 increases the F1-score to 67.63% and 65.71%, respectively, demonstrating the usefulness of both sources of information. In addition, a combination of both gives a 69.64% F1-score, which is the best. among various character representations.. Table 4 shows a variety of different settings for word-based Chinese NER. With automatic segmentation, a word-based LSTM CRF baseline gives a 64.12% F1-score, which is higher compared to the character-based baseline. This demonstrates that both word information and character information are useful for Chinese NER. The two methods of. word+char LSTM and word+char LSTM(cid:48), lead to similar improvements.. A CNN representation of character sequences gives a slightly higher F1-score compared to LSTM character representations. On the other hand, further using character bigram information leads to increased F1-score over word+char LSTM, but decreased F1-score over word+char CNN.. As shown in Table 4, the lattice LSTM-CRF model gives a development F1-score of 71.62%, which is significantly7 higher compared with both the word-based and character-based methods, despite that it does not use character bigrams or word segmentation information." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Development results.'\nProblem: 'Which model achieves the highest F1-score without using word segmentation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/203.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Development results." ], "reasoning": "The table shows that the lattice LSTM-CRF model, which does not use word segmentation, achieves the highest F1-score of 71.62%.", "question": "Which model achieves the highest F1-score without using word segmentation?", "answer": "So, the answer is {\"answer\": \"Lattice\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe word-based LSTM CRF baseline achieves a higher F1-score than the character-based LSTM CRF model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/203.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Development results." ], "reasoning": "The table shows that the word-based LSTM CRF baseline has an F1-score of 64.12%, while the character-based LSTM CRF model has an F1-score of 62.47%. Therefore, the statement is supported.", "statement": "The word-based LSTM CRF baseline achieves a higher F1-score than the character-based LSTM CRF model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Main results on OntoNotes.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/204.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Main results on OntoNotes." ], "reasoning": "The word-based models show competitive results with gold-standard segmentation, and the lattice model achieves the highest F1-score with automatic segmentation.", "description": "The OntoNotes test results are shown in Table 5. With gold-standard segmentation, our word-based methods give competitive results to the state-of-the-art on the dataset (Che et al., 2013; Wang et al., 2013),. In addition, the results show. that our word-based models can serve as highly competitive baselines. With automatic segmentation, the F1-score of word+char+bichar LSTM decreases from 75.77% to 71.70%, showing the influence of segmentation to NER. Consistent with observations on the development set, adding lattice word information leads to an 88.81% → 93.18% increasement of F1-score over the character baseline, as compared with 88.81% → 91.87% by adding bichar+softword. The lattice model gives significantly the best F1-score on automatic segmentation." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Main results on OntoNotes.'\nProblem: 'What is the F1-score of the lattice model with no segmentation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/204.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Main results on OntoNotes." ], "reasoning": "The table shows the F1-score of the lattice model with no segmentation as 73.88%.", "question": "What is the F1-score of the lattice model with no segmentation?", "answer": "So, the answer is {\"answer\": \"73.88\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe F1-score of the word baseline model with gold segmentation is higher than the F1-score of the word baseline model with automatic segmentation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/204.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Main results on OntoNotes." ], "reasoning": "The table shows that the F1-score of the word baseline model with gold segmentation is 69.52%, while with automatic segmentation it is 65.63%. Therefore, the statement is supported.", "statement": "The F1-score of the word baseline model with gold segmentation is higher than the F1-score of the word baseline model with automatic segmentation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Main results on MSRA.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/205.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Main results on MSRA." ], "reasoning": "The lattice model achieves the highest accuracy on the MSRA dataset, significantly outperforming both the best character-based and word-based models.", "description": "Results on the MSRA dataset are shown in Table 6.. Our chosen segmentor gives 95.93% accuracy on 5-fold cross-validated training set.. Compared with the existing methods, our wordbased and character-based LSTM-CRF models give competitive accuracies. The lattice model significantly outperforms both the best characterbased and word-based models (p < 0.01), achieving the best result on this standard benchmark." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Main results on MSRA.'\nProblem: 'Which model achieves the highest F1 score on the MSRA dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/205.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Main results on MSRA." ], "reasoning": "The table shows the F1 scores for various models. The lattice model has the highest F1 score of 93.18.", "question": "Which model achieves the highest F1 score on the MSRA dataset?", "answer": { "answer": "Lattice" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe word-based LSTM-CRF model achieves an F1 score higher than 90 on the MSRA dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/205.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Main results on MSRA." ], "reasoning": "The table shows that the word-based LSTM-CRF model has an F1 score of 86.65, which is lower than 90.", "statement": "The word-based LSTM-CRF model achieves an F1 score higher than 90 on the MSRA dataset.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: Main results on resume NER.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/206.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Main results on resume NER." ], "reasoning": "The lattice model achieves the highest precision, recall, and F1 scores compared to other models, indicating its superior performance.", "description": "Results on the resume NER test data are shown in Table 8.. the lattice model significantly outperforms both the word-based mode and the character-based model for Weibo and resume (p < 0.01), giving state-of-the-art results." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 8: Main results on resume NER.'\nProblem: 'Which model has the highest F1 score and what is that score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/206.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Main results on resume NER." ], "reasoning": "To find the model with the highest F1 score, compare the F1 scores of all models listed in the table. The lattice model has the highest F1 score of 94.46.", "question": "Which model has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"Lattice\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe word baseline model has the highest precision score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/206.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Main results on resume NER." ], "reasoning": "The table shows that the word baseline model has a precision score of 93.72, which is lower than the precision scores of the +char+bichar LSTM, +bichar+softword, and lattice models. Therefore, the statement is refuted.", "statement": "The word baseline model has the highest precision score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Development set results for English–Estonian. character-F and BLEU scores in percentages. +/− stands for adding/removing a component. Multiple modifications are indicated by increasing the indentation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/207.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Development set results for English–Estonian. character-F and BLEU scores in percentages. +/− stands for adding/removing a component. Multiple modifications are indicated by increasing the indentation." ], "reasoning": "Cognate Morfessor outperforms BPE in both chrF-1.0 and BLEU% for Estonian, and cross-lingual segmentation is particularly beneficial.", "description": "Table 3 shows the development set results for Estonian. Cognate Morfessor outperforms the comparable BPE system according to both measures for Estonian. The cross-lingual segmentation is particularly beneficial for Estonian. For Estonian, we have two ensemble configurations one combining 3 monolingually finetuned independent runs, and one combining 5 monolingually finetuned savepoints from 4 independent runs." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Development set results for English–Estonian. character-F and BLEU scores in percentages. +/− stands for adding/removing a component. Multiple modifications are indicated by increasing the indentation.'\nProblem: 'Which ensemble configuration achieved the highest chrF-1.0 score for Estonian?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/207.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Development set results for English–Estonian. character-F and BLEU scores in percentages. +/− stands for adding/removing a component. Multiple modifications are indicated by increasing the indentation." ], "reasoning": "The table shows different configurations and their chrF-1.0 scores. The highest chrF-1.0 score is 57.75, achieved by the +ensemble-of-5 configuration.", "question": "Which ensemble configuration achieved the highest chrF-1.0 score for Estonian?", "answer": "So, the answer is {\"answer\": \"+ensemble-of-5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe monolingual configuration achieved the lowest BLEU% score for Estonian.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/207.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Development set results for English–Estonian. character-F and BLEU scores in percentages. +/− stands for adding/removing a component. Multiple modifications are indicated by increasing the indentation." ], "reasoning": "The table shows the BLEU% scores for different configurations. The monolingual configuration has a BLEU% score of 15.82, which is the lowest among the listed configurations.", "statement": "The monolingual configuration achieved the lowest BLEU% score for Estonian.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Results on UNCorpus-1.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/208.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on UNCorpus-1." ], "reasoning": "Our approach consistently outperforms Basic and Dual-0, despite the latter being trained with additional monolingual data.", "description": "Tables 1 and 2 show results on the UNCorpus datasets.. Our approach consistently outperforms Basic and Dual-0, despite the latter being trained with additional monolingual data (Sestorain et al., 2018)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Results on UNCorpus-1.'\nProblem: 'What is the average score for the supervised approach in the 'Our baselines Pivot' column?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/208.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on UNCorpus-1." ], "reasoning": "To find the average score for the supervised approach in the 'Our baselines Pivot' column, look at the 'Supervised (avg.)' row under the 'Our baselines Pivot' column.", "question": "What is the average score for the supervised approach in the 'Our baselines Pivot' column?", "answer": "So, the answer is {\"answer\": \"50.75\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'Our baselines Pivot' approach has the highest score for the \\Es → \\Fr translation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/208.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on UNCorpus-1." ], "reasoning": "To verify this statement, compare the scores in the \\Es → \\Fr row across all columns. The 'Our baselines Pivot' column has a score of 38.10, which is higher than the scores in the other columns.", "statement": "The 'Our baselines Pivot' approach has the highest score for the \\Es → \\Fr translation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results of semantic feature ablation, model trained with gold data only'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/209.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of semantic feature ablation, model trained with gold data only" ], "reasoning": "Removing node attributes has the largest negative impact on BLEU score, while removing edge features has a minimal effect.", "description": "edge properties are removed, which Table 3 shows has an effect of less than 1 BLEU point).. we ablate node (predicate) and edge attributes. number and tense. have the largest effect on the reported BLEU score.. has only a small impact on performance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Results of semantic feature ablation, model trained with gold data only'\nProblem: 'What is the BLEU score when all attributes except number and tense are removed?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/209.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of semantic feature ablation, model trained with gold data only" ], "reasoning": "The table shows the BLEU score for different ablation scenarios. When all attributes except number and tense are removed, the BLEU score is 67.34.", "question": "What is the BLEU score when all attributes except number and tense are removed?", "answer": "So, the answer is {\"answer\": \"67.34\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRemoving edge features has a significant impact on the BLEU score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/209.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of semantic feature ablation, model trained with gold data only" ], "reasoning": "The table shows that removing edge features results in a BLEU score of 71.27, which is only slightly lower than the score with all attributes (72.06). This indicates that the impact is minimal, not significant.", "statement": "Removing edge features has a significant impact on the BLEU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results on UNCorpus-2.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/210.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on UNCorpus-2." ], "reasoning": "Models trained with agreement perform comparably to Pivot, sometimes outperforming it, especially when the target is Russian. Unlike Dual-0, Agree maintains high performance in supervised directions, staying within 1 BLEU point of Basic.", "description": "We see that models trained with agreement perform comparably to Pivot, outperforming it in some cases, e.g., when the target is Russian, perhaps because it is quite different linguistically from the English pivot.. unlike Dual-0, Agree maintains high performance in the supervised directions (within 1 BLEU point compared to Basic)," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results on UNCorpus-2.'\nProblem: 'Which model maintains high performance in supervised directions and sometimes outperforms Pivot when the target is Russian?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/210.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on UNCorpus-2." ], "reasoning": "The description states that the Agree model maintains high performance in supervised directions and sometimes outperforms Pivot when the target is Russian. The table shows that Agree has a BLEU score of 28.08 for En to Ru, which is higher than Pivot's 27.34.", "question": "Which model maintains high performance in supervised directions and sometimes outperforms Pivot when the target is Russian?", "answer": "So, the answer is {\"answer\": \"Agree\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Agree model performs worse than Pivot in zero-shot translation.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/210.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on UNCorpus-2." ], "reasoning": "The table shows that in zero-shot translation, the Agree model has a BLEU score of 32.29, which is slightly lower than Pivot's 32.47.", "statement": "The Agree model performs worse than Pivot in zero-shot translation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Zero-shot results on Europarl. Note that Soft and Distill are not multilingual systems.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/211.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Zero-shot results on Europarl. Note that Soft and Distill are not multilingual systems." ], "reasoning": "Our approach outperforms Basic by 2-3 BLEU points but is slightly behind Pivot on average, except for De where it performs better.", "description": "Table 3 shows the results on the Europarl corpus.. our approach consistently outperforms Basic by 2-3 BLEU points but lags a bit behind Pivot on average (except on De where it is better). Es" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Zero-shot results on Europarl. Note that Soft and Distill are not multilingual systems.'\nProblem: 'How does our approach compare to Basic and Pivot on the Europarl corpus?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/211.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Zero-shot results on Europarl. Note that Soft and Distill are not multilingual systems." ], "reasoning": "The table shows that the results of previous work distll in Es to Fr is 33.86.", "question": "What is the results of previous work distll in Es to Fr?", "answer": { "answer": "33.86" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur approach is better than Pivot on De.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/211.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Zero-shot results on Europarl. Note that Soft and Distill are not multilingual systems." ], "reasoning": "The table shows that our approach has a higher BLEU score than Pivot for De.", "statement": "Our approach is better than Pivot on De.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Results on the official IWSLT17 multilingual task.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/212.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on the official IWSLT17 multilingual task." ], "reasoning": "The vanilla training method achieves high zero-shot performance, surpassing the Pivot method.", "description": "Table 4 presents results on the original IWSLT17 task.. the vanilla training method (Johnson et al., 2016) achieves very high zero shot performance, even outperforming Pivot." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Results on the official IWSLT17 multilingual task.'\nProblem: 'Which method achieves the highest zero-shot performance?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/212.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on the official IWSLT17 multilingual task." ], "reasoning": "By comparing the zero-shot (avg.) row, the 'Previous work SOTA' method achieves the highest performance with a score of 20.55.", "question": "Which method achieves the highest zero-shot performance?", "answer": { "answer": "Previous work SOTA" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Agree method outperforms the Previous work CPG in zero-shot performance.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/212.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on the official IWSLT17 multilingual task." ], "reasoning": "The Agree method has a zero-shot (avg.) score of 20.58, which is higher than the Previous work CPG score of 11.69.", "statement": "The Agree method outperforms the Previous work CPG in zero-shot performance.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results on our proposed IWSLT17?'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/213.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on our proposed IWSLT17?" ], "reasoning": "Agree performs better than Basic in both supervised and zero-shot settings, and is slightly worse than Pivot in zero-shot performance.", "description": "on our proposed preprocessed IWSLT17(cid:63) that eliminates the overlap and reduces the number of supervised directions (8), there is a considerable gap between the supervised and zeroshot performance of Basic.. Agree performs better than Basic and is slightly worse than Pivot." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Results on our proposed IWSLT17?'\nProblem: 'What is the zero-shot performance gap between Basic and Pivot?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/213.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on our proposed IWSLT17?" ], "reasoning": "To find the zero-shot performance gap between Basic and Pivot, subtract the zero-shot average of Basic from that of Pivot: 17.68 - 12.61.", "question": "What is the zero-shot performance gap between Basic and Pivot?", "answer": { "answer": "5.07" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nPivot performs better than Agree in zero-shot settings.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/213.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on our proposed IWSLT17?" ], "reasoning": "The table shows that Pivot has a zero-shot average of 17.68, while Agree has a zero-shot average of 15.23, indicating that Pivot performs better.", "statement": "Pivot performs better than Agree in zero-shot settings.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: BLEU scores for the bilingual test sets. Here all contexts are incorporated as InitDec for Europarl and InitDec+AddDec for Subtitles unless otherwise specified. bold: Best performance, †: Statistically significantly better than the base model, based on bootstrap resampling Clark et al. (2011) with p < 0.05.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/214.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU scores for the bilingual test sets. Here all contexts are incorporated as InitDec for Europarl and InitDec+AddDec for Subtitles unless otherwise specified. bold: Best performance, †: Statistically significantly better than the base model, based on bootstrap resampling Clark et al. (2011) with p < 0.05." ], "reasoning": "For Europarl data, InitDec and InitDec+AddDec show significant BLEU improvements for En-Et, En-De, and En-Fr, while for Subtitles data, the maximum improvement is +0.30 BLEU for InitDec+AddDec.", "description": "For the Europarl data, we see decent improvements with InitDec for En-Et (+1.11 BLEU) and En-De (+1.60 BLEU), and with InitDec+AddDec for En-Fr (+1.19 BLEU). We also observe that, for all language-pairs, both translation directions benefit from context,. On the other hand, for the Subtitles data, we see a maximum improvement of +0.30 BLEU for InitDec+AddDec.. The next set of experiments evaluates the five different approaches for computing the sourceside context. from Table 2 that for English-Estonian and English-German, our model indeed benefits from using is evident. Finally, our results with source, target and dual contexts are reported. Interestingly, just using the source context is sufficient for English-Estonian and English-German. For English-French, on the other hand, we see significant improvements for the models using the target-side conversation history over using only the source-side.. Unlike Europarl, for Subtitles, we see improvements for our Src-TgtMix dual context variant over the Src-Tgt one for En→Ru,. To summarise, for majority of the cases our Language-Specific Sentence-level Attention is a winner or a close second. Using the Target Context is useful when the base model generates reasonable-quality translations; otherwise, using the Source Context should suffice." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: BLEU scores for the bilingual test sets. Here all contexts are incorporated as InitDec for Europarl and InitDec+AddDec for Subtitles unless otherwise specified. bold: Best performance, †: Statistically significantly better than the base model, based on bootstrap resampling Clark et al. (2011) with p < 0.05.'\nProblem: 'Which model shows the highest BLEU score improvement for En→Ru in the Subtitles data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/214.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU scores for the bilingual test sets. Here all contexts are incorporated as InitDec for Europarl and InitDec+AddDec for Subtitles unless otherwise specified. bold: Best performance, †: Statistically significantly better than the base model, based on bootstrap resampling Clark et al. (2011) with p < 0.05." ], "reasoning": "From the table, the highest BLEU score improvement for En→Ru in the Subtitles data is observed with the Dual Context Src-Tgt model, which has a score of 19.88.", "question": "Which model shows the highest BLEU score improvement for En→Ru in the Subtitles data?", "answer": "So, the answer is {\"answer\": \"Dual Context Src-Tgt\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Lang-Specific S-Attention model shows the highest BLEU score for En→Et in the Europarl data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/214.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU scores for the bilingual test sets. Here all contexts are incorporated as InitDec for Europarl and InitDec+AddDec for Subtitles unless otherwise specified. bold: Best performance, †: Statistically significantly better than the base model, based on bootstrap resampling Clark et al. (2011) with p < 0.05." ], "reasoning": "From the table, the highest BLEU score for En→Et in the Europarl data is 21.84, achieved by the Lang-Specific S-Attention model.", "statement": "The Lang-Specific S-Attention model shows the highest BLEU score for En→Et in the Europarl data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: BLEU scores for the bilingual test sets. bold: Best performance, †: Statistically significantly better than the contextual baseline.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/215.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU scores for the bilingual test sets. bold: Best performance, †: Statistically significantly better than the contextual baseline." ], "reasoning": "Our model outperforms the local-context baseline for Europarl, indicating the benefit of wider context. For En-Ru, using the previous sentence is sufficient.", "description": "From Table 3, it can be seen that our model surpasses the local-context baseline for Europarl showing that the wider context is indeed beneficial. For En-Ru, it can be seen that using previous sentence is sufficient" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: BLEU scores for the bilingual test sets. bold: Best performance, †: Statistically significantly better than the contextual baseline.'\nProblem: 'What is the improvement in score for Europarl En-De when using our model compared to the previous sentence?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/215.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU scores for the bilingual test sets. bold: Best performance, †: Statistically significantly better than the contextual baseline." ], "reasoning": "The score for Europarl En-De using the previous sentence is 26.09, and the score using our model is 26.49. The improvement is 26.49 - 26.09 = 0.40.", "question": "What is the improvement in score for Europarl En-De when using our model compared to the previous sentence?", "answer": { "answer": "0.40" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur model performs better than the previous sentence baseline for Europarl En-Fr.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/215.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU scores for the bilingual test sets. bold: Best performance, †: Statistically significantly better than the contextual baseline." ], "reasoning": "The score for Europarl En-Fr using the previous sentence is 38.15, and the score using our model is 38.46. Since 38.46 is greater than 38.15, the statement is supported.", "statement": "Our model performs better than the previous sentence baseline for Europarl En-Fr.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: BLEU scores for En-De bilingual test set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/216.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: BLEU scores for En-De bilingual test set." ], "reasoning": "The ablation study shows that using the complete context yields the highest BLEU score, while using only the current language from previous turns results in a significant decrease in BLEU score.", "description": "We conduct an ablation study to validate our hypothesis of using the complete context versus using only one of the three types of contexts in a bilingual multi-speaker conversation: (i) current turn, (ii) previous turns in current language, and (iii) previous turns in the other language. The results for En-De are reported in Table 4. We see decrease in BLEU for all types of contexts with significant decrease when considering only current language from previous turns.The results show that the current turn has the most influence on translating a sentence," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: BLEU scores for En-De bilingual test set.'\nProblem: 'Which type of context yields the highest BLEU score in the En-De bilingual multi-speaker conversation?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/216.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: BLEU scores for En-De bilingual test set." ], "reasoning": "The table shows BLEU scores for different types of contexts. The highest BLEU score is 26.49, which corresponds to the complete context.", "question": "Which type of context yields the highest BLEU score in the En-De bilingual multi-speaker conversation?", "answer": "So, the answer is {\"answer\": \"Complete Context\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUsing only the current turn yields a higher BLEU score than using the complete context.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/216.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: BLEU scores for En-De bilingual test set." ], "reasoning": "The table shows that the BLEU score for the current turn is 26.39, while the BLEU score for the complete context is 26.49. Therefore, the statement is incorrect.", "statement": "Using only the current turn yields a higher BLEU score than using the complete context.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Performance comparison between different state-of-the-art approaches on SCWS, in terms of Spearman’s correlation. UTDSM refers to the projected cross-topic representation, UTDSM Random refers to the case when random words served as anchors and GMM (c) corresponds to GMM smoothing with c components.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/217.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance comparison between different state-of-the-art approaches on SCWS, in terms of Spearman’s correlation. UTDSM refers to the projected cross-topic representation, UTDSM Random refers to the case when random words served as anchors and GMM (c) corresponds to GMM smoothing with c components." ], "reasoning": "UTDSM outperforms the baseline Global-DSM in both AvgSimC and MaxSimC metrics, with GMM smoothing improving MaxSimC but lowering AvgSimC.", "description": "In Table 1 we compare our model (UTDSM) with our baseline (Global-DSM) and other state-ofthe-art multi-prototype approaches for the contextual semantic similarity task. It is clear that all different setups of UTDSM perform better than the baseline for both contextual semantic similarity metrics. Using a single Gaussian distribution (UTDSM + GMM (1)) at the smoothing step of our method produces similar results to the baseline model.. We also observe that random anchoring performs slightly worse than UTDSM with respect to AvgSimC.. Furthermore, we observe that GMM smoothing has a different effect on the MaxSimC and AvgSimC metrics. Specifically, for AvgSimC we consistently report lower results when GMM smoothing is applied for different number of components.. At the same time, our smoothing technique highly improves the performance of MaxSimC for all possible configurations.. Overall, the performance of our model is highly competitive to the state-of-the-art models in terms of AvgSimC, for 500-dimensional topic embeddings. We also achieve state-of-the-art performance for the MaxSimC metric, using smoothed topic embeddings of 300 or 500 dimensions with 2 or 3 Gaussian components." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Performance comparison between different state-of-the-art approaches on SCWS, in terms of Spearman’s correlation. UTDSM refers to the projected cross-topic representation, UTDSM Random refers to the case when random words served as anchors and GMM (c) corresponds to GMM smoothing with c components.'\nProblem: 'Which method achieves the highest MaxSimC score for 500-dimensional topic embeddings?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/217.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance comparison between different state-of-the-art approaches on SCWS, in terms of Spearman’s correlation. UTDSM refers to the projected cross-topic representation, UTDSM Random refers to the case when random words served as anchors and GMM (c) corresponds to GMM smoothing with c components." ], "reasoning": "To find the highest MaxSimC score for 500-dimensional topic embeddings, we look at the MaxSimC column under the 500-dimensions section. The highest score is 68.6, achieved by UTDSM + GMM (2).", "question": "Which method achieves the highest MaxSimC score for 500-dimensional topic embeddings?", "answer": "So, the answer is {\"answer\": \"UTDSM + GMM (2)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nUTDSM with 500-dimensional topic embeddings and 2 Gaussian components achieves the highest MaxSimC score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/217.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance comparison between different state-of-the-art approaches on SCWS, in terms of Spearman’s correlation. UTDSM refers to the projected cross-topic representation, UTDSM Random refers to the case when random words served as anchors and GMM (c) corresponds to GMM smoothing with c components." ], "reasoning": "The table shows that for 500-dimensional topic embeddings, UTDSM + GMM (2) achieves the highest MaxSimC score of 68.6.", "statement": "UTDSM with 500-dimensional topic embeddings and 2 Gaussian components achieves the highest MaxSimC score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: BLEU scores for domain match experiments'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/218.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU scores for domain match experiments" ], "reasoning": "Training with both WSJ and Gigaword data improves performance across all test domains, especially in out-of-domain tests like Brown and Wikipedia.", "description": "We evaluate the in- and out-of-domain performance of our approach by training models on either WSJ gold data only, or both WSJ gold data and Gigaword silver data, and evaluating on different domains. The results in Table 2 show that while the generator performs best on test data which matches the training domain (news), semisupervised training leads to substantial out-ofdomain improvements on the Wikipedia and the Brown corpus portions of the test set." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: BLEU scores for domain match experiments'\nProblem: 'What is the performance improvement on the Brown test domain when using WSJ + Gigaword data compared to using only WSJ data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/218.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU scores for domain match experiments" ], "reasoning": "The performance on the Brown test domain with WSJ data is 45.00, and with WSJ + Gigaword data, it is 76.99. The improvement is calculated as 76.99 - 45.00.", "question": "What is the performance improvement on the Brown test domain when using WSJ + Gigaword data compared to using only WSJ data?", "answer": { "answer": "31.99" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model performs better on the Wikipedia test domain when trained with WSJ data only.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/218.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU scores for domain match experiments" ], "reasoning": "The performance on the Wikipedia test domain with WSJ data is 35.90, while with WSJ + Gigaword data, it is 62.26. The model performs better with WSJ + Gigaword data.", "statement": "The model performs better on the Wikipedia test domain when trained with WSJ data only.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Evaluation results of multi-class text classification.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/219.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation results of multi-class text classification." ], "reasoning": "Our model outperforms the baseline across all metrics for both averaging approaches, with AvgCD showing the highest performance.", "description": "Evaluation results on text classification are presented in Table 2. We observe that our model performs better than the baseline across all metrics for both averaging approaches (AvgCD, AvgD), while the usage of dominant topics appears to have lower performance (MaxCD). Specifically, we get an improvement of 2 − 2.5% on topic-based average and 0.5 − 1% on simple average combination compared to using Global-DSM." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Evaluation results of multi-class text classification.'\nProblem: 'Which method has the highest F1-score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/219.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation results of multi-class text classification." ], "reasoning": "By comparing the F1-scores of all methods in the table, AvgCD has the highest F1-score of 64.5.", "question": "Which method has the highest F1-score?", "answer": { "answer": "AvgCD" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe MaxCD method has the highest precision.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/219.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation results of multi-class text classification." ], "reasoning": "The table shows that AvgCD has the highest precision of 64.6, not MaxCD.", "statement": "The MaxCD method has the highest precision.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Evaluation results on paraphrase detection task.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/220.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation results on paraphrase detection task." ], "reasoning": "AvgD outperforms other methods in the F1-score metric, indicating that cross-topic representations are more semantically rich than the single embeddings baseline (Global-DSM).", "description": "Results for the paraphrase identification task are presented in Table 3. AvgD yields the best results especially in F1 metric showing that cross-topic representations are semantically richer than single embeddings baseline (Global-DSM)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Evaluation results on paraphrase detection task.'\nProblem: 'Which method achieves the highest F1-score and what does this indicate about cross-topic representations?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/220.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation results on paraphrase detection task." ], "reasoning": "The table shows that AvgD has the highest F1-score of 64.0. The description indicates that this suggests cross-topic representations are semantically richer than single embeddings baseline (Global-DSM).", "question": "Which method achieves the highest F1-score and what does this indicate about cross-topic representations?", "answer": "So, the answer is {\"answer\": \"AvgD\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAvgD has the highest recall among all methods.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/220.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation results on paraphrase detection task." ], "reasoning": "The table shows that AvgD has a recall of 69.4, which is equal to the recall of AvgCD and higher than the recall of Global-DSM and MaxCD.", "statement": "AvgD has the highest recall among all methods.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Macro f1-score of unknown intent detection with different proportion (25%, 50% and 75%) of classes are treated as known intents on SNIPS and ATIS dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/221.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Macro f1-score of unknown intent detection with different proportion (25%, 50% and 75%) of classes are treated as known intents on SNIPS and ATIS dataset." ], "reasoning": "Our method outperforms all baselines in all settings, particularly excelling over LOF (Softmax). Performance on the ATIS dataset drops significantly as the percentage of known intents increases.", "description": "We show the experiment results in Table 2.. our method consistently performs better than all baselines in all settings.. our method is also better than LOF (Softmax).. we observe that on the ATIS dataset, the performance of unknown intent detection dramatically drops as the known intent increases." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Macro f1-score of unknown intent detection with different proportion (25%, 50% and 75%) of classes are treated as known intents on SNIPS and ATIS dataset.'\nProblem: 'What is the performance of the LOF (LMCL) method on the SNIPS dataset with 50% known intents?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/221.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Macro f1-score of unknown intent detection with different proportion (25%, 50% and 75%) of classes are treated as known intents on SNIPS and ATIS dataset." ], "reasoning": "To find the performance of the LOF (LMCL) method on the SNIPS dataset with 50% known intents, locate the intersection of the 'SNIPS 50%' column and the 'LOF (LMCL)' row.", "question": "What is the performance of the LOF (LMCL) method on the SNIPS dataset with 50% known intents?", "answer": "So, the answer is {\"answer\": \"84.1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe performance of the DOC method on the ATIS dataset with 75% known intents is higher than 40.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/221.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Macro f1-score of unknown intent detection with different proportion (25%, 50% and 75%) of classes are treated as known intents on SNIPS and ATIS dataset." ], "reasoning": "To verify this statement, locate the intersection of the 'ATIS 75%' column and the 'DOC' row. The value is 37.7, which is less than 40.", "statement": "The performance of the DOC method on the ATIS dataset with 75% known intents is higher than 40.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparing AL with and without its truncated average, tracking time-indexed lag ALi=gi−i−1γ when |x|=|y|=4 for a wait-3 system.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/222.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparing AL with and without its truncated average, tracking time-indexed lag ALi=gi−i−1γ when |x|=|y|=4 for a wait-3 system." ], "reasoning": "The table shows the time-indexed lags averaged to calculate AL for a wait-3 system, demonstrating how each position beyond the point where all source tokens are read reduces the lag, pulling the average lag below k.", "description": "Table 1 shows the time-indexed lags that are averaged to calculate AL for a wait-3 system. The lags make the problem clear: each position beyond the point where all source tokens have been read (gi = |x|) has its lag reduced by. , pulling the average lag below k." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Comparing AL with and without its truncated average, tracking time-indexed lag ALi=gi−i−1γ when |x|=|y|=4 for a wait-3 system.'\nProblem: 'What is the average lag (AL) when τ = |y|?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/222.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparing AL with and without its truncated average, tracking time-indexed lag ALi=gi−i−1γ when |x|=|y|=4 for a wait-3 system." ], "reasoning": "The table shows that when τ = |y|, the average lag (AL) is given as 2.25.", "question": "What is the average lag (AL) when τ = |y|?", "answer": "So, the answer is {\"answer\": \"2.25\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average lag (AL) is 3 when τ = 2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/222.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparing AL with and without its truncated average, tracking time-indexed lag ALi=gi−i−1γ when |x|=|y|=4 for a wait-3 system." ], "reasoning": "The table shows that when τ = 2, the average lag (AL) is indeed 3.", "statement": "The average lag (AL) is 3 when τ = 2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: BLEU scores for evaluating AMR and DMRS generators on an AMR test set'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/223.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: BLEU scores for evaluating AMR and DMRS generators on an AMR test set" ], "reasoning": "The results show that the DMRS generator outperforms the AMR generator significantly, even without additional attributes.", "description": "We compare our approach to AMR-to-text generation by evaluating our generator on a standard AMR test set. for models trained on gold as well as gold plus silver data.9 We evaluate DMRS models both with and without predicate and edge attributes,. The results in Table 4 show that our MRS generator performs better than the AMR generator by a large margin, even when the additional MRS attributes are excluded." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: BLEU scores for evaluating AMR and DMRS generators on an AMR test set'\nProblem: 'What is the performance difference between DMRS with all attributes and AMR when trained on gold data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/223.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: BLEU scores for evaluating AMR and DMRS generators on an AMR test set" ], "reasoning": "To find the performance difference, subtract the AMR score from the DMRS with all attributes score when trained on gold data: 56.9 - 22.0.", "question": "What is the performance difference between DMRS with all attributes and AMR when trained on gold data?", "answer": "So, the answer is {\"answer\": \"34.9\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nDMRS with no attributes performs better than AMR when trained on gold data.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/223.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: BLEU scores for evaluating AMR and DMRS generators on an AMR test set" ], "reasoning": "The table shows that DMRS with no attributes has a score of 40.1, while AMR has a score of 22.0 when trained on gold data, indicating that DMRS with no attributes performs better.", "statement": "DMRS with no attributes performs better than AMR when trained on gold data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Accuracy of the model trained only on MultiNLI on our datasets, which are used as probing datasets. The Complexity column refers to the syntactic complexity of the sentences.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/224.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy of the model trained only on MultiNLI on our datasets, which are used as probing datasets. The Complexity column refers to the syntactic complexity of the sentences." ], "reasoning": "The model performs significantly better on the MultiNLI development set compared to the dative alternation and numerical reasoning datasets, with particularly low accuracy on neutral and contradiction examples.", "description": "The model has relatively low accuracy on the entailment and contradiction examples while close to zero accuracy on the neutral ones.. the accuracy of the model on each test set.. On our dative alternation dataset, the accuracy on our test sets is substantially lower than on the MultiNLI development set (50.78% versus 84.66% respectively),. The model has very high accuracy on the entailment examples, while close to zero on the contradiction ones.. On the numerical reasoning dataset, the model also seems to fail on this inference type with test set accuracy much lower than on the MultiNLI development set," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Accuracy of the model trained only on MultiNLI on our datasets, which are used as probing datasets. The Complexity column refers to the syntactic complexity of the sentences.'\nProblem: 'What is the accuracy of the model on the simple examples of the dative alternation dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/224.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy of the model trained only on MultiNLI on our datasets, which are used as probing datasets. The Complexity column refers to the syntactic complexity of the sentences." ], "reasoning": "The table shows that the accuracy of the model on the simple examples of the dative alternation dataset is 100%.", "question": "What is the accuracy of the model on the simple examples of the dative alternation dataset?", "answer": { "answer": "100%" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe model has higher accuracy on the simple examples of the numerical reasoning dataset compared to the complex examples.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/224.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy of the model trained only on MultiNLI on our datasets, which are used as probing datasets. The Complexity column refers to the syntactic complexity of the sentences." ], "reasoning": "The table shows that the accuracy of the model on the simple examples of the numerical reasoning dataset is 45.04%, while the accuracy on the complex examples is 36.15%. Therefore, the statement is supported.", "statement": "The model has higher accuracy on the simple examples of the numerical reasoning dataset compared to the complex examples.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: BLEU and METEOR scores for the sentence-level baseline (S-NMT) vs. variants of our Document NMT model. bold: Best performance, †: Statistically significantly better than the baseline.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/225.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU and METEOR scores for the sentence-level baseline (S-NMT) vs. variants of our Document NMT model. bold: Best performance, †: Statistically significantly better than the baseline." ], "reasoning": "The document NMT model with both memories consistently shows the highest BLEU and METEOR score improvements across three language pairs compared to S-NMT, with the dual memory variant being the most effective for Estonian→English.", "description": "We consistently observe +1.15/+1.13 BLEU/METEOR score improvements across the three language pairs upon comparing our best model to S-NMT (see Table 2). Overall, our document NMT model with both memories has been the most effective variant for all of the three language pairs.. From Table 2, we consistently see +.95/+1.00 BLEU/METEOR improvements between the best variants of our model and the sentence-level baseline across the three lan. For German→English, guage pairs. For French→English, all variants of document NMT model show comparable performance when using BLEU; however, when evaluated using METEOR, the dual memory model is the best. the target memory variants give comparable results, whereas for Estonian→English, the dual memory variant proves to be the best. Overall, the Memory-toContext model variants perform better than their Memory-to-Output counterparts." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: BLEU and METEOR scores for the sentence-level baseline (S-NMT) vs. variants of our Document NMT model. bold: Best performance, †: Statistically significantly better than the baseline.'\nProblem: 'What is the BLEU score improvement for the Memory-to-Context model with both memories for German→English compared to S-NMT?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/225.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU and METEOR scores for the sentence-level baseline (S-NMT) vs. variants of our Document NMT model. bold: Best performance, †: Statistically significantly better than the baseline." ], "reasoning": "The BLEU score for the Memory-to-Context model with both memories for German→English is 6.57, and for S-NMT it is 5.24. The improvement is 6.57 - 5.24 = 1.33.", "question": "What is the BLEU score improvement for the Memory-to-Context model with both memories for German→English compared to S-NMT?", "answer": "So, the answer is {\"answer\": \"1.33\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Memory-to-Output model with both memories shows the highest METEOR score for Estonian→English.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/225.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU and METEOR scores for the sentence-level baseline (S-NMT) vs. variants of our Document NMT model. bold: Best performance, †: Statistically significantly better than the baseline." ], "reasoning": "The METEOR score for the Memory-to-Output model with both memories for Estonian→English is 26.10, which is the highest among all models for this language pair.", "statement": "The Memory-to-Output model with both memories shows the highest METEOR score for Estonian→English.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Probing tasks performance of vector averaging (AVG) and max pooling (MAX) vs. DCT embeddings with various K. Majority (baseline), Human (human-bound), and a linear classifier with sentence length as sole feature (Length) as reported in Conneau et al. (2018), respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/226.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Probing tasks performance of vector averaging (AVG) and max pooling (MAX) vs. DCT embeddings with various K. Majority (baseline), Human (human-bound), and a linear classifier with sentence length as sole feature (Length) as reported in Conneau et al. (2018), respectively." ], "reasoning": "DCT generally outperforms averaging on all tasks, with larger K improving performance in syntactic and semantic tasks, except for SOMO where performance decreases with increasing K.", "description": "We report the performance in probing tasks in Table 2.. In general, DCT yields better performance compared to averaging on all tasks, and larger K often yields improved performance in syntactic and semantic tasks. For the surface information tasks, SentLen and Word content (WC), c significantly outperforms AVG.. The performance decreased with increasing K in c[0 : K],. While increasing K has no positive effect on surface information tasks, syntactic and semantic tasks demonstrate performance gains with larger K. This trend is clearly observed in all syntactic tasks and three of the semantic tasks, where DCT performs well above AVG and the performance improves with increasing K. The only exception is SOMO, where increasing K actually results in lower performance, although all DCT results are still higher than AVG." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Probing tasks performance of vector averaging (AVG) and max pooling (MAX) vs. DCT embeddings with various K. Majority (baseline), Human (human-bound), and a linear classifier with sentence length as sole feature (Length) as reported in Conneau et al. (2018), respectively.'\nProblem: 'Which model achieves the highest performance in the Surface WC task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/226.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Probing tasks performance of vector averaging (AVG) and max pooling (MAX) vs. DCT embeddings with various K. Majority (baseline), Human (human-bound), and a linear classifier with sentence length as sole feature (Length) as reported in Conneau et al. (2018), respectively." ], "reasoning": "To find the highest performance in the Surface WC task, compare the values in the Surface WC column. The highest value is 91.11, achieved by the c[0] model.", "question": "Which model achieves the highest performance in the Surface WC task?", "answer": { "answer": "c[0]" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIncreasing K improves performance in the Syntactic TopConst task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/226.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Probing tasks performance of vector averaging (AVG) and max pooling (MAX) vs. DCT embeddings with various K. Majority (baseline), Human (human-bound), and a linear classifier with sentence length as sole feature (Length) as reported in Conneau et al. (2018), respectively." ], "reasoning": "The performance in the Syntactic TopConst task improves with increasing K, as seen in the values for c[0:1] (78.34), c[0:2] (80.41), c[0:3] (80.41), c[0:4] (81.49), c[0:5] (81.60), and c[0:6] (81.41).", "statement": "Increasing K improves performance in the Syntactic TopConst task.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: DCT embedding Performance in SentEval downstream tasks compared to vector averaging (AVG) and max pooling (MAX).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/227.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: DCT embedding Performance in SentEval downstream tasks compared to vector averaging (AVG) and max pooling (MAX)." ], "reasoning": "The table shows improvements in most tasks with increasing K, but the most significant improvement is seen in TREC question classification.", "description": "Our results in Table 3 are consistent with these observations, where we see improvements in most tasks, but the difference is not as significant as the probing tasks, except in TREC question classification where increasing K leads to much better performance." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: DCT embedding Performance in SentEval downstream tasks compared to vector averaging (AVG) and max pooling (MAX).'\nProblem: 'Which task shows the most significant improvement with increasing K?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/227.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: DCT embedding Performance in SentEval downstream tasks compared to vector averaging (AVG) and max pooling (MAX)." ], "reasoning": "By examining the table, we see that the TREC question classification task shows a significant improvement as K increases, with the highest score of 89.8 achieved at c[0:4].", "question": "Which task shows the most significant improvement with increasing K?", "answer": "Thus, the answer is {\"answer\": \"TREC question classification\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nIncreasing K leads to the highest performance in TREC question classification.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/227.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: DCT embedding Performance in SentEval downstream tasks compared to vector averaging (AVG) and max pooling (MAX)." ], "reasoning": "The table shows that the TREC question classification task achieves the highest score of 89.8 at c[0:4], indicating that increasing K leads to the highest performance in this task.", "statement": "Increasing K leads to the highest performance in TREC question classification.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Performance in text classification (20-NG, R-8) and sentiment (SST-5) tasks of various models as reported in Kayal and Tsatsaronis (2019), where DCT* refers to the implementation in Kayal and Tsatsaronis (2019). Our DCT embeddings are denoted as c[k] in the bottom row. Bold indicates the best result, and italic indicates second-best.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/228.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance in text classification (20-NG, R-8) and sentiment (SST-5) tasks of various models as reported in Kayal and Tsatsaronis (2019), where DCT* refers to the implementation in Kayal and Tsatsaronis (2019). Our DCT embeddings are denoted as c[k] in the bottom row. Bold indicates the best result, and italic indicates second-best." ], "reasoning": "Our model, c[k], achieved close to state-of-the-art performance in the 20-NG and R-8 tasks, outperforming EigenSent on all tasks and generally performing better than or on par with p-means, ELMo, BERT, and EigenSent⊕Avg.", "description": "Table 4 shows the best results for the various models as reported in Kayal and Tsat. saronis (2019), in addition to the best performance of our model denoted as c[k].. Note that the DCT-based model, DCT*, described in Kayal and Tsatsaronis (2019) performed relatively poorly in all tasks, while our model achieved close to state-of-the-art performance in both the 20-NG and R-8 tasks. Our model outperformed EignSent on all tasks and generally performed better than or on par with p-means, ELMo, BERT, and EigenSent⊕Avg on both the 20-NG and R-8. On the other hand, both EigenSent⊕Avg and ELMo performed better than all other models on SST-5." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Performance in text classification (20-NG, R-8) and sentiment (SST-5) tasks of various models as reported in Kayal and Tsatsaronis (2019), where DCT* refers to the implementation in Kayal and Tsatsaronis (2019). Our DCT embeddings are denoted as c[k] in the bottom row. Bold indicates the best result, and italic indicates second-best.'\nProblem: 'Which model performed the best on the SST-5 task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/228.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance in text classification (20-NG, R-8) and sentiment (SST-5) tasks of various models as reported in Kayal and Tsatsaronis (2019), where DCT* refers to the implementation in Kayal and Tsatsaronis (2019). Our DCT embeddings are denoted as c[k] in the bottom row. Bold indicates the best result, and italic indicates second-best." ], "reasoning": "The description states that both EigenSent⊕Avg and ELMo performed better than all other models on SST-5. By looking at the table, EigenSent⊕Avg has the highest F1 score (41.67) and ELMo has the highest precision (42.77) for SST-5.", "question": "Which model performed the best on the SST-5 task?", "answer": "So, the answer is {\"answer\": \"ELMo\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe DCT* model performed the worst on all tasks.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/228.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance in text classification (20-NG, R-8) and sentiment (SST-5) tasks of various models as reported in Kayal and Tsatsaronis (2019), where DCT* refers to the implementation in Kayal and Tsatsaronis (2019). Our DCT embeddings are denoted as c[k] in the bottom row. Bold indicates the best result, and italic indicates second-best." ], "reasoning": "The description mentions that the DCT* model performed relatively poorly in all tasks. By examining the table, DCT* has the lowest scores in most tasks, but not all. For example, PCA has lower scores in the 20-NG tasks.", "statement": "The DCT* model performed the worst on all tasks.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Coverage of words from the manual transcripts in the DSTC2 development set of different batch ASR output types (%). In the pruned cnet interjections and hypotheses with scores below 0.001 were removed.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/229.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Coverage of words from the manual transcripts in the DSTC2 development set of different batch ASR output types (%). In the pruned cnet interjections and hypotheses with scores below 0.001 were removed." ], "reasoning": "Pruning the cnet reduces its size to an average of seven timesteps with two hypotheses, while maintaining a high accuracy.", "description": "As shown in Table 1, this does not discard too many correct hypotheses but markedly reduces the size of the cnet to an average of seven timesteps with two hypotheses." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Coverage of words from the manual transcripts in the DSTC2 development set of different batch ASR output types (%). In the pruned cnet interjections and hypotheses with scores below 0.001 were removed.'\nProblem: 'What is the accuracy of the pruned cnet for all words?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/229.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Coverage of words from the manual transcripts in the DSTC2 development set of different batch ASR output types (%). In the pruned cnet interjections and hypotheses with scores below 0.001 were removed." ], "reasoning": "The table shows the accuracy for different configurations. For the pruned cnet under 'all words', the accuracy is 83.1.", "question": "What is the accuracy of the pruned cnet for all words?", "answer": { "answer": "83.1" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe accuracy of the 10-best configuration for slots/values is higher than that for all words.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/229.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Coverage of words from the manual transcripts in the DSTC2 development set of different batch ASR output types (%). In the pruned cnet interjections and hypotheses with scores below 0.001 were removed." ], "reasoning": "The table shows that the accuracy for the 10-best configuration is 75.6 for slots/values and 78.6 for all words. Therefore, the statement is refuted.", "statement": "The accuracy of the 10-best configuration for slots/values is higher than that for all words.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: DSTC2 test set accuracy of ten runs with different random seeds in the format average maximumminimum. ⋆ denotes a statistically significant higher result than the baseline (p<0.05, Wilcoxon signed-rank test with Bonferroni correction for ten repeated comparisons). The cnet ensemble corresponds to the best cnet model with pruning threshold 0.001 and weighted pooling.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/230.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: DSTC2 test set accuracy of ten runs with different random seeds in the format average maximumminimum. ⋆ denotes a statistically significant higher result than the baseline (p<0.05, Wilcoxon signed-rank test with Bonferroni correction for ten repeated comparisons). The cnet ensemble corresponds to the best cnet model with pruning threshold 0.001 and weighted pooling." ], "reasoning": "Weighted pooling with a pruning threshold of 0.001 achieves the highest joint goals accuracy, improving by 1.6 percentage points over the baseline, and ensemble models outperform Mrksic et al. (2017) for joint requests.", "description": "Table 3 displays the results for our model evaluated on cnets for increasingly aggressive pruning levels (discarding only interjections, additionally discarding hypotheses with scores below 0.001 and 0.01, respectively). As can be seen, using the full cnet except for interjections does not improve over the baseline.. However, when pruning low-probability hypotheses both pooling strategies improve over the baseline. Yet, average pooling performs worse for the lower pruning threshold, which shows that the model is still affected by noise among the hypotheses.. Weighted pooling performs better for the lower pruning threshold of 0.001 with which we obtain the highest result overall, improving the joint goals accuracy by 1.6 percentage points compared to the baseline.. Moreover, we see that an ensemble model that averages the predictions of ten cnet models trained with different random seeds also outperforms an ensemble of ten baseline models.. Our ensemble models outperform Mrksic et al. (2017) for the joint requests but are a bit worse for the joint goals." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: DSTC2 test set accuracy of ten runs with different random seeds in the format average maximumminimum. ⋆ denotes a statistically significant higher result than the baseline (p<0.05, Wilcoxon signed-rank test with Bonferroni correction for ten repeated comparisons). The cnet ensemble corresponds to the best cnet model with pruning threshold 0.001 and weighted pooling.'\nProblem: 'Which method achieves the highest joint goals accuracy and by how many percentage points does it improve over the baseline?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/230.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: DSTC2 test set accuracy of ten runs with different random seeds in the format average maximumminimum. ⋆ denotes a statistically significant higher result than the baseline (p<0.05, Wilcoxon signed-rank test with Bonferroni correction for ten repeated comparisons). The cnet ensemble corresponds to the best cnet model with pruning threshold 0.001 and weighted pooling." ], "reasoning": "The table shows that the highest joint goals accuracy is achieved by the cnet method with a score threshold of 0.001 using weighted pooling, which has an accuracy of 71.4. The baseline accuracy is 69.7, so the improvement is 71.4 - 69.7 = 1.7 percentage points.", "question": "Which method achieves the highest joint goals accuracy and by how many percentage points does it improve over the baseline?", "answer": "So, the answer is {\"answer\": \"1.7\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe ensemble models outperform Mrksic et al. (2017) for joint goals.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/230.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: DSTC2 test set accuracy of ten runs with different random seeds in the format average maximumminimum. ⋆ denotes a statistically significant higher result than the baseline (p<0.05, Wilcoxon signed-rank test with Bonferroni correction for ten repeated comparisons). The cnet ensemble corresponds to the best cnet model with pruning threshold 0.001 and weighted pooling." ], "reasoning": "The table shows that the ensemble models have a joint goals accuracy of 69.7, while Mrksic et al. (2017) have a joint goals accuracy of 73.4. Therefore, the statement is refuted.", "statement": "The ensemble models outperform Mrksic et al. (2017) for joint goals.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: DSTC2 test set accuracy for 1-best ASR outputs of ten runs with different random seeds in the format average maximumminimum.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/231.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: DSTC2 test set accuracy for 1-best ASR outputs of ten runs with different random seeds in the format average maximumminimum." ], "reasoning": "DST accuracy improves with higher-quality live ASR outputs and significantly increases with manual transcripts.", "description": "As can be seen from Table 2, the DST accuracy slightly increases for the higher-quality live ASR outputs. More importantly, the DST performance drastically increases, when we evaluate on the manual transcripts that reflect the true user utterances nearly perfectly." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: DSTC2 test set accuracy for 1-best ASR outputs of ten runs with different random seeds in the format average maximumminimum.'\nProblem: 'What is the DST performance for goals when evaluated on manual transcripts?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/231.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: DSTC2 test set accuracy for 1-best ASR outputs of ten runs with different random seeds in the format average maximumminimum." ], "reasoning": "The table shows that the DST performance for goals when evaluated on manual transcripts is 82.474.3.", "question": "What is the DST performance for goals when evaluated on manual transcripts?", "answer": { "answer": "82.474.3" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe DST performance for requests is highest when evaluated on manual transcripts.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/231.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: DSTC2 test set accuracy for 1-best ASR outputs of ten runs with different random seeds in the format average maximumminimum." ], "reasoning": "The table indicates that the DST performance for requests is highest at 99.098.0 when evaluated on manual transcripts.", "statement": "The DST performance for requests is highest when evaluated on manual transcripts.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Sentences/clauses after sentence splitting.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/232.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Sentences/clauses after sentence splitting." ], "reasoning": "The table shows the distribution of sentences/clauses in six gold classes across training, development, and test datasets, with a total of 31,545 training, 8,036 development, and 5,563 test sentences/clauses.", "description": "Fourth, we introduce finer classes (Tables 1–2), which fit better the target task, where nested clauses are frequent.. The splitter produced 31,545 training, 8,036 development, and 5,563 test sentences/clauses.3 Table 2 shows their distribution in the six gold (correct) classes.. cf. Table 2)," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Sentences/clauses after sentence splitting.'\nProblem: 'What is the total number of sentences/clauses in the 'Obligation List Item' class across all datasets?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/232.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Sentences/clauses after sentence splitting." ], "reasoning": "To find the total number of sentences/clauses in the 'Obligation List Item' class, sum the values in the 'Train', 'Dev', and 'Test' columns for this class: 2888 (Train) + 726 (Dev) + 255 (Test).", "question": "What is the total number of sentences/clauses in the 'Obligation List Item' class across all datasets?", "answer": "So, the answer is {\"answer\": \"3869\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'Prohibition' class has more sentences/clauses in the training dataset than the 'Obligation List Intro' class.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/232.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Sentences/clauses after sentence splitting." ], "reasoning": "The table shows that the 'Prohibition' class has 1,172 sentences/clauses in the training dataset, while the 'Obligation List Intro' class has 828 sentences/clauses in the training dataset. Therefore, the statement is supported.", "statement": "The 'Prohibition' class has more sentences/clauses in the training dataset than the 'Obligation List Intro' class.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Manually aligned News Commentary data.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/233.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Manually aligned News Commentary data." ], "reasoning": "The dataset consists of 1,654 segments in trilingual, bilingual, and monolingual formats. A test set of 600 trilingual sentences was created, and the remaining trilingual and bilingual sentences were used for development sets.", "description": "As a result, we obtained 1,654 lines of data comprising trilingual, bilingual, and monolingual segments (mainly sentences) as summarized in Table 1. Finally, for the sake of comparability, we randomly chose 600 trilingual sentences to create a test set, and concatenated the rest of them and bilingual sentences to form development sets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Manually aligned News Commentary data.'\nProblem: 'How many trilingual sentences were used for the development set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/233.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Manually aligned News Commentary data." ], "reasoning": "The table shows that there are 913 trilingual sentences in total. Out of these, 600 were used for the test set. Therefore, the remaining 313 trilingual sentences were used for the development set.", "question": "How many trilingual sentences were used for the development set?", "answer": { "answer": "313" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThere are 287 bilingual sentences in the dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/233.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Manually aligned News Commentary data." ], "reasoning": "The table shows that there are 287 bilingual sentences (Ru and En) in the dataset.", "statement": "There are 287 bilingual sentences in the dataset.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Precision, recall, f1, and auc scores, with the best results in bold and gray background.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/234.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Precision, recall, f1, and auc scores, with the best results in bold and gray background." ], "reasoning": "The hierarchical H-BILSTM-ATT model outperforms the other models in terms of macro and micro F1 and AUC scores, supporting the hypothesis that considering entire sections and allowing sentence embeddings to interact is beneficial.", "description": "Table 3 reports the precision, recall, F1 score, area under the precision-recall curve (AUC) per class, as well as micro- and macro-averages.. The self-attention mechanism (BILSTM-ATT) leads to clear overall improvements (in macro and micro F1 and AUC, Table 3) comparing to the plain BILSTM, supporting the hypothesis that selfattention allows the classifier to focus on indicative tokens. Allowing the BILSTM to consider tokens of neighboring sentences (X-BILSTM-ATT) does not lead to any clear overall improvements.. The hierarchical H-BILSTM-ATT clearly outperforms the other three methods, supporting the hypothesis that considering entire sections and allowing the sentence embeddings to interact in the upper BILSTM (Fig. 3) is beneficial." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Precision, recall, f1, and auc scores, with the best results in bold and gray background.'\nProblem: 'Which model achieves the highest macro-average F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/234.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Precision, recall, f1, and auc scores, with the best results in bold and gray background." ], "reasoning": "To find the model with the highest macro-average F1 score, compare the macro-average F1 scores of all models. The H-BILSTM-ATT model has the highest macro-average F1 score of 0.89.", "question": "Which model achieves the highest macro-average F1 score?", "answer": { "answer": "H-BILSTM-ATT" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BILSTM-ATT model achieves a higher micro-average AUC score than the H-BILSTM-ATT model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/234.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Precision, recall, f1, and auc scores, with the best results in bold and gray background." ], "reasoning": "To verify this statement, compare the micro-average AUC scores of the BILSTM-ATT and H-BILSTM-ATT models. The BILSTM-ATT model has a micro-average AUC score of 0.98, while the H-BILSTM-ATT model has a micro-average AUC score of 0.98. Therefore, the statement is refuted.", "statement": "The BILSTM-ATT model achieves a higher micro-average AUC score than the H-BILSTM-ATT model.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: BLEU scores of M2M Transformer NMT systems trained on the mixture of given parallel corpus and pseudo-parallel data generated by back-translation using (b3). Six “X∗→Y” columns show whether the pseudo-parallel data for each translation direction is involved. Bold indicates the scores higher than (b3) and “∙” indicates statistical significance of the improvement.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/235.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: BLEU scores of M2M Transformer NMT systems trained on the mixture of given parallel corpus and pseudo-parallel data generated by back-translation using (b3). Six “X∗→Y” columns show whether the pseudo-parallel data for each translation direction is involved. Bold indicates the scores higher than (b3) and “∙” indicates statistical significance of the improvement." ], "reasoning": "The use of all six-way pseudo-parallel data (#10) significantly improved the base model for all translation directions except En→Ru, while Ja→Ru and Ru→Ja tasks did not exceed 10 BLEU points.", "description": "Table 8 shows the BLEU scores achieved by several reasonable combinations of six-way pseudo-parallel data. We observed that the use of all six-way pseudo-parallel data (#10) significantly improved the base model for all the translation directions, except En→Ru. A translation direction often benefited when the pseudo-parallel data for that specific direction was used.. However, the resulting BLEU scores for Ja→Ru and Ru→Ja tasks do not exceed 10 BLEU points, implying the inherent limitation of the in-domain data as well as the difficulty of these translation directions.. models of our multistage fine-tuning, i.e., V and VII, achieved significantly higher BLEU scores than (b3) in Table 5, a weak baseline without using any monolingual data, and #10 in Table 8, a strong baseline established with monolingual data." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 8: BLEU scores of M2M Transformer NMT systems trained on the mixture of given parallel corpus and pseudo-parallel data generated by back-translation using (b3). Six “X∗→Y” columns show whether the pseudo-parallel data for each translation direction is involved. Bold indicates the scores higher than (b3) and “∙” indicates statistical significance of the improvement.'\nProblem: 'Which translation direction did not benefit from the use of all six-way pseudo-parallel data (#10)?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/235.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: BLEU scores of M2M Transformer NMT systems trained on the mixture of given parallel corpus and pseudo-parallel data generated by back-translation using (b3). Six “X∗→Y” columns show whether the pseudo-parallel data for each translation direction is involved. Bold indicates the scores higher than (b3) and “∙” indicates statistical significance of the improvement." ], "reasoning": "The description states that the use of all six-way pseudo-parallel data (#10) significantly improved the base model for all translation directions except En→Ru.", "question": "Which translation direction did not benefit from the use of all six-way pseudo-parallel data (#10)?", "answer": "So, the answer is {\"answer\": \"En→Ru\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BLEU score for Ja→Ru using all six-way pseudo-parallel data (#10) is below 10.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/235.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: BLEU scores of M2M Transformer NMT systems trained on the mixture of given parallel corpus and pseudo-parallel data generated by back-translation using (b3). Six “X∗→Y” columns show whether the pseudo-parallel data for each translation direction is involved. Bold indicates the scores higher than (b3) and “∙” indicates statistical significance of the improvement." ], "reasoning": "The table shows that the BLEU score for Ja→Ru using all six-way pseudo-parallel data (#10) is 4.43, which is below 10.", "statement": "The BLEU score for Ja→Ru using all six-way pseudo-parallel data (#10) is below 10.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Accuracies (%) on few-shot DA. “On 1.0” represents the results on the original FewRel dataset and “On 2.0” represents the results on the new test set. The models with “-ADV” use adversarial training described in Section 3.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/236.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracies (%) on few-shot DA. “On 1.0” represents the results on the original FewRel dataset and “On 2.0” represents the results on the new test set. The models with “-ADV” use adversarial training described in Section 3." ], "reasoning": "Few-shot models show significant performance drops in different domains, with BERT-PAIR consistently outperforming other models.", "description": "Table 2 demonstrates the evaluation results of few-shot DA on the existing FewRel test set and the new test set.. (1) All few-shot models suffer dramatic perfor mance falls when tested on a different domain. (2) Adversarial training does improve the results on the new test domain, yet still has large space for growth. (3) BERT-PAIR outperforms all other few-shot models on both 1.0 and 2.0 test set." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Accuracies (%) on few-shot DA. “On 1.0” represents the results on the original FewRel dataset and “On 2.0” represents the results on the new test set. The models with “-ADV” use adversarial training described in Section 3.'\nProblem: 'Which model has the highest performance on the 5-Way 5-Shot on 1.0 test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/236.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracies (%) on few-shot DA. “On 1.0” represents the results on the original FewRel dataset and “On 2.0” represents the results on the new test set. The models with “-ADV” use adversarial training described in Section 3." ], "reasoning": "By examining the 5-Way 5-Shot on 1.0 column, BERT-PAIR has the highest score of 93.22±0.13.", "question": "Which model has the highest performance on the 5-Way 5-Shot on 1.0 test set?", "answer": "So, the answer is {\"answer\": \"BERT-PAIR\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nProto (BERT) has the highest performance on the 10-Way 1-Shot on 2.0 test set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/236.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracies (%) on few-shot DA. “On 1.0” represents the results on the original FewRel dataset and “On 2.0” represents the results on the new test set. The models with “-ADV” use adversarial training described in Section 3." ], "reasoning": "By examining the 10-Way 1-Shot on 2.0 column, BERT-PAIR has the highest score of 43.64±0.46, not Proto (BERT).", "statement": "Proto (BERT) has the highest performance on the 10-Way 1-Shot on 2.0 test set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Accuracies (%) on few-shot NOTA. Models with * simply ignore the NOTA setting and assume all queries can be classified as one of the given relations.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/237.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracies (%) on few-shot NOTA. Models with * simply ignore the NOTA setting and assume all queries can be classified as one of the given relations." ], "reasoning": "BERT-PAIR performs better under the NOTA setting and remains stable with increasing NOTA rates, while Proto models show a significant drop in performance as the NOTA rate increases.", "description": "All models are trained given 50% NOTA queries and tested under four different NOTA rates: 0%, 15%, 30%, 50%.. For detailed numbers of results on fewshot NOTA, please refer to Table 3.. (1) Treating NOTA as the. + 1 relation is beneficial for handling Few-Shot NOTA, though the results still fall fast when the NOTA rate increases. (2) BERT-PAIR works better under the NOTA setting for its binary-classification style model, and stays stable with rising NOTA rate. (3) Though BERT-PAIR achieves promising results, huge gaps still exist between the conventional (0% NOTA rate) and NOTA settings (gaps of 8 points for 5-way 1-shot and 7 points for 5way 5-shot with 50% NOTA rate), which calls for further research to address the challenge." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Accuracies (%) on few-shot NOTA. Models with * simply ignore the NOTA setting and assume all queries can be classified as one of the given relations.'\nProblem: 'Which model has the highest performance under the 5-Way-1-Shot 50% NOTA setting?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/237.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracies (%) on few-shot NOTA. Models with * simply ignore the NOTA setting and assume all queries can be classified as one of the given relations." ], "reasoning": "By examining the table, we see that under the 5-Way-1-Shot 50% NOTA setting, BERT-PAIR* has the highest performance with a score of 80.31±0.12.", "question": "Which model has the highest performance under the 5-Way-1-Shot 50% NOTA setting?", "answer": "So, the answer is {\"answer\": \"BERT-PAIR*\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nProto (BERT) has a higher performance than Proto (CNN) under the 5-Way-1-Shot 30% NOTA setting.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/237.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracies (%) on few-shot NOTA. Models with * simply ignore the NOTA setting and assume all queries can be classified as one of the given relations." ], "reasoning": "According to the table, under the 5-Way-1-Shot 30% NOTA setting, Proto (BERT) has a performance of 61.08±0.28, while Proto (CNN) has a performance of 53.18±0.12. Therefore, Proto (BERT) has a higher performance.", "statement": "Proto (BERT) has a higher performance than Proto (CNN) under the 5-Way-1-Shot 30% NOTA setting.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Performance analysis of entity classification methods on the three datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/238.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance analysis of entity classification methods on the three datasets." ], "reasoning": "Table 2 shows the results of the proposed method, its variants, and the baseline methods, including feature level and model level transfer-learning.", "description": "These results are shown in table 2 as feature level transfer-learning.. These results are shown in table 2 as model level transfer learning.. Table 2 shows the results of the proposed method, its variants and the baseline methods." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Performance analysis of entity classification methods on the three datasets.'\nProblem: 'Which method achieved the highest BBN Mi-F1 score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/238.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance analysis of entity classification methods on the three datasets." ], "reasoning": "To find the method with the highest BBN Mi-F1 score, look at the BBN Mi-F1 column and identify the highest value, which is 0.795, achieved by model level transfer-learning.", "question": "Which method achieved the highest BBN Mi-F1 score?", "answer": { "answer": "model level transfer-learning" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe AFET method achieved the highest Wiki/Figer(gold) Ma-F1 score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/238.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance analysis of entity classification methods on the three datasets." ], "reasoning": "To verify this statement, look at the Wiki/Figer(gold) Ma-F1 column. The highest score is 0.812, achieved by 'our†', not AFET.", "statement": "The AFET method achieved the highest Wiki/Figer(gold) Ma-F1 score.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Performance analysis of the proposed model and AFET on top 10 (in terms of type frequency) types present in OntoNotes dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/239.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance analysis of the proposed model and AFET on top 10 (in terms of type frequency) types present in OntoNotes dataset." ], "reasoning": "The proposed model outperforms AFET in all top-10 frequent types except for the 'other' type.", "description": "Results are shown in Table 4.. Compared to AFET, the proposed model performs better in all types except other in the top-10 frequent types." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Performance analysis of the proposed model and AFET on top 10 (in terms of type frequency) types present in OntoNotes dataset.'\nProblem: 'Which label type has the highest F-1 score for the proposed model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/239.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance analysis of the proposed model and AFET on top 10 (in terms of type frequency) types present in OntoNotes dataset." ], "reasoning": "To find the label type with the highest F-1 score for the proposed model, compare the 'our F-1' column values. The highest value is 0.823 for the 'other' label type.", "question": "Which label type has the highest F-1 score for the proposed model?", "answer": "So, the answer is {\"answer\": \"other\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe proposed model has a higher precision than AFET for the 'organization' label type.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/239.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance analysis of the proposed model and AFET on top 10 (in terms of type frequency) types present in OntoNotes dataset." ], "reasoning": "Compare the 'our Prec.' and 'AFET Prec.' values for the 'organization' label type. The proposed model's precision is 0.588, which is lower than AFET's precision of 0.903.", "statement": "The proposed model has a higher precision than AFET for the 'organization' label type.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Performance of different approaches in relation to the average cosine similarity of words associated with a property (cos). The last row shows the Spearman Rank correlation between f1-scores and average cosine similarity. Property types are listed under type (p = part, vp = visual-perceptual, op = other-perceptual, e = encyclopaedic, f = functional, t = taxonomic).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/240.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of different approaches in relation to the average cosine similarity of words associated with a property (cos). The last row shows the Spearman Rank correlation between f1-scores and average cosine similarity. Property types are listed under type (p = part, vp = visual-perceptual, op = other-perceptual, e = encyclopaedic, f = functional, t = taxonomic)." ], "reasoning": "The table shows that nearest neighbors' scores correlate more strongly with average cosine similarity than supervised classification approaches.", "description": "Table 2 shows a selection of the f1-scores achieved on properties in the CSLB dataset in relation to the average cosine similarity of the associated words. A high average cosine similarity means that the concepts overall have similar vector representations and can thus be seen as having a low diversity. The results of the Spearman Rank correlation clearly indicate that scores achieved by nearest neighbors correlate more strongly with the average cosine than the two supervised classification approaches." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Performance of different approaches in relation to the average cosine similarity of words associated with a property (cos). The last row shows the Spearman Rank correlation between f1-scores and average cosine similarity. Property types are listed under type (p = part, vp = visual-perceptual, op = other-perceptual, e = encyclopaedic, f = functional, t = taxonomic).'\nProblem: 'Which feature has the highest f1-net score and what is its average cosine similarity?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/240.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of different approaches in relation to the average cosine similarity of words associated with a property (cos). The last row shows the Spearman Rank correlation between f1-scores and average cosine similarity. Property types are listed under type (p = part, vp = visual-perceptual, op = other-perceptual, e = encyclopaedic, f = functional, t = taxonomic)." ], "reasoning": "By examining the table, the feature 'does_make_music' has the highest f1-net score of 0.95. Its average cosine similarity is 0.55.", "question": "Which feature has the highest f1-net score and what is its average cosine similarity?", "answer": { "answer": "0.55" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe feature 'is_heavy' has a higher f1-lr score than 'is_strong'.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/240.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of different approaches in relation to the average cosine similarity of words associated with a property (cos). The last row shows the Spearman Rank correlation between f1-scores and average cosine similarity. Property types are listed under type (p = part, vp = visual-perceptual, op = other-perceptual, e = encyclopaedic, f = functional, t = taxonomic)." ], "reasoning": "The table shows that 'is_heavy' has an f1-lr score of 0.17, while 'is_strong' has an f1-lr score of 0.13. Therefore, 'is_heavy' has a higher f1-lr score than 'is_strong'.", "statement": "The feature 'is_heavy' has a higher f1-lr score than 'is_strong'.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Training times and parameters to learn.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/241.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Training times and parameters to learn." ], "reasoning": "H-BILSTM-ATT has more parameters but trains faster than BILSTM and BILSTM-ATT due to quicker convergence.", "description": "H-BILSTM-ATT is also much faster to train than BILSTM and BILSTM-ATT (Table 4), even though it has more parameters, because it converges faster (5-7 epochs vs. 12-15)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Training times and parameters to learn.'\nProblem: 'Which network has the shortest training time despite having the highest number of parameters?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/241.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Training times and parameters to learn." ], "reasoning": "H-BILSTM-ATT has the highest number of parameters (1,837M) and the shortest training time (2h 30m).", "question": "Which network has the shortest training time despite having the highest number of parameters?", "answer": "So, the answer is {\"answer\": \"h-bilstm-att\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nH-BILSTM-ATT has the highest number of parameters and the shortest training time.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/241.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Training times and parameters to learn." ], "reasoning": "The table shows that H-BILSTM-ATT has 1,837M parameters and a training time of 2h 30m, which is the highest number of parameters and the shortest training time among the listed networks.", "statement": "H-BILSTM-ATT has the highest number of parameters and the shortest training time.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: F1 scores achieved by logistic regression (lr) two runs of a neural net classifier (net1 and net2 and the n-best nearest neighbors evaluated with leave-one-out on the full datasets (marked as full_ and the crow-only sets (marked as crowd_).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/242.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F1 scores achieved by logistic regression (lr) two runs of a neural net classifier (net1 and net2 and the n-best nearest neighbors evaluated with leave-one-out on the full datasets (marked as full_ and the crow-only sets (marked as crowd_)." ], "reasoning": "Classifiers trained on pure animal and food words perform highly when tested on polysemous animal and food words, with a significant difference to nearest neighbors.", "description": "Table 4 shows the f1-scores on the full clean datasets.. For polysemy between food and animals (Table 4), we observe that when trained on pure animal and food words and tested on polysemous animal and food words, the classifiers perform highly with a large difference to nearest neighbors." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: F1 scores achieved by logistic regression (lr) two runs of a neural net classifier (net1 and net2 and the n-best nearest neighbors evaluated with leave-one-out on the full datasets (marked as full_ and the crow-only sets (marked as crowd_).'\nProblem: 'What is the f1-score for 'full_is_found_in_seas' using the 'net1' classifier?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/242.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F1 scores achieved by logistic regression (lr) two runs of a neural net classifier (net1 and net2 and the n-best nearest neighbors evaluated with leave-one-out on the full datasets (marked as full_ and the crow-only sets (marked as crowd_)." ], "reasoning": "To find the f1-score for 'full_is_found_in_seas' using the 'net1' classifier, locate the row for 'full_is_found_in_seas' and then find the value under the 'net1' column.", "question": "What is the f1-score for 'full_is_found_in_seas' using the 'net1' classifier?", "answer": "So, the answer is {\"answer\": \"0.97\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 'neigh' classifier has the highest f1-score for 'full_has_wheels'.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/242.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F1 scores achieved by logistic regression (lr) two runs of a neural net classifier (net1 and net2 and the n-best nearest neighbors evaluated with leave-one-out on the full datasets (marked as full_ and the crow-only sets (marked as crowd_)." ], "reasoning": "To verify this statement, locate the row for 'full_has_wheels' and compare the f1-scores across all classifiers. The 'neigh' classifier has a score of 0.90, which is the highest among all classifiers for this property.", "statement": "The 'neigh' classifier has the highest f1-score for 'full_has_wheels'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Word-level evaluation results. Each value corresponds to average Pearson correlation of 7 identical models initialized with different random seeds. Correlations were scaled to the [−100;100] range for easier reading. Bold values represent the best method per training dataset, per task; underlined values represent the best-performing method per task, independent of training dataset. For each task and dataset, every best-performing method was significantly different to other methods (p<0.05), except for w trained in SNLI at the MTurk287 task. Statistical significance was obtained with a two-sided Welch’s t-test for two independent samples without assuming equal variance (Welch, 1947).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/243.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Word-level evaluation results. Each value corresponds to average Pearson correlation of 7 identical models initialized with different random seeds. Correlations were scaled to the [−100;100] range for easier reading. Bold values represent the best method per training dataset, per task; underlined values represent the best-performing method per task, independent of training dataset. For each task and dataset, every best-performing method was significantly different to other methods (p<0.05), except for w trained in SNLI at the MTurk287 task. Statistical significance was obtained with a two-sided Welch’s t-test for two independent samples without assuming equal variance (Welch, 1947)." ], "reasoning": "Character-only models performed significantly worse than other models, while vector gates generally outperformed other methods regardless of the training dataset.", "description": "Table 1 shows the quality of word representations in terms of the correlation between word similarity. scores obtained by the proposed models and word similarity scores defined by humans.. for that can see First, we each task, character only models had significantly worse performance than every other model trained on the same dataset.. Further, bold results show the overall trend that vector gates outperformed the other methods regardless of training dataset.. Additionally, results from the MNLI row in general, and underlined results in particular, show that training on MultiNLI produces word representations better at capturing word similarity.. Exceptions to the previous rule are models evaluated in MEN and RW.. More notably, in the RareWords dataset (Luthe word only, concat, ong et al., 2013), and scalar gate methods performed equally, despite having been trained in different datasets (p > 0.1), and the char only method performed significantly worse when trained in MultiNLI.. The vector gate, however, performed significantly better than its counterpart trained in SNLI." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Word-level evaluation results. Each value corresponds to average Pearson correlation of 7 identical models initialized with different random seeds. Correlations were scaled to the [−100;100] range for easier reading. Bold values represent the best method per training dataset, per task; underlined values represent the best-performing method per task, independent of training dataset. For each task and dataset, every best-performing method was significantly different to other methods (p<0.05), except for w trained in SNLI at the MTurk287 task. Statistical significance was obtained with a two-sided Welch’s t-test for two independent samples without assuming equal variance (Welch, 1947).'\nProblem: 'Which model performed the best on the WS353S dataset when trained on MultiNLI?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/243.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Word-level evaluation results. Each value corresponds to average Pearson correlation of 7 identical models initialized with different random seeds. Correlations were scaled to the [−100;100] range for easier reading. Bold values represent the best method per training dataset, per task; underlined values represent the best-performing method per task, independent of training dataset. For each task and dataset, every best-performing method was significantly different to other methods (p<0.05), except for w trained in SNLI at the MTurk287 task. Statistical significance was obtained with a two-sided Welch’s t-test for two independent samples without assuming equal variance (Welch, 1947)." ], "reasoning": "To find the best-performing model on the WS353S dataset when trained on MultiNLI, look at the WS353S column in the MNLI row. The vector gate (vg) model has the highest score of 77.23.", "question": "Which model performed the best on the WS353S dataset when trained on MultiNLI?", "answer": { "answer": "vector gate (vg)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe character-only model performed better than the scalar gate model on the MTurk771 dataset when trained on SNLI.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/243.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Word-level evaluation results. Each value corresponds to average Pearson correlation of 7 identical models initialized with different random seeds. Correlations were scaled to the [−100;100] range for easier reading. Bold values represent the best method per training dataset, per task; underlined values represent the best-performing method per task, independent of training dataset. For each task and dataset, every best-performing method was significantly different to other methods (p<0.05), except for w trained in SNLI at the MTurk287 task. Statistical significance was obtained with a two-sided Welch’s t-test for two independent samples without assuming equal variance (Welch, 1947)." ], "reasoning": "To verify this statement, compare the scores of the character-only (c) and scalar gate (sg) models on the MTurk771 dataset in the SNLI row. The character-only model scored 0.82, while the scalar gate model scored 46.15. Therefore, the statement is refuted.", "statement": "The character-only model performed better than the scalar gate model on the MTurk771 dataset when trained on SNLI.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Experimental results. Each value shown in the table is the average result of 7 identical models initialized with different random seeds. Values represent accuracy (%) unless indicated by †, in which case they represent Pearson correlation scaled to the range [−100,100] for easier reading. Bold values represent the best method per training dataset, per task; underlined values represent the best-performing method per task, independent of training dataset. Values marked with an asterisk (∗) are significantly different to the average performance of the best model trained on the same dataset (p<0.05). Results for every best-performing method trained on one dataset are significantly different to the best-performing method trained on the other. Statistical significance was obtained in the same way as described in table 1.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/244.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experimental results. Each value shown in the table is the average result of 7 identical models initialized with different random seeds. Values represent accuracy (%) unless indicated by †, in which case they represent Pearson correlation scaled to the range [−100,100] for easier reading. Bold values represent the best method per training dataset, per task; underlined values represent the best-performing method per task, independent of training dataset. Values marked with an asterisk (∗) are significantly different to the average performance of the best model trained on the same dataset (p<0.05). Results for every best-performing method trained on one dataset are significantly different to the best-performing method trained on the other. Statistical significance was obtained in the same way as described in table 1." ], "reasoning": "Models trained on MultiNLI generally performed better than those trained on SNLI, except for SICKE and SICKR. No method significantly outperformed the word-only baseline in classification tasks, but the vector gate method excelled in the STSB task.", "description": "Table 2 shows the impact that different methods for combining character and word-level word representations have in the quality of the sentence representations produced by our models.. We can observe the same trend mentioned in section 4.1, and highlighted by the difference between bold values, that models trained in MultiNLI performed better than those trained in. SNLI at a statistically significant level,. The two exceptions to the previous trend, SICKE and SICKR, benefited more from models trained on SNLI.. Additionally, there was no method that significantly outperformed the word only baseline in classification tasks.. On the other hand, the vector gate significantly outperformed every other method in the STSB task when trained in both datasets, and in the STS16 task when trained in SNLI." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Experimental results. Each value shown in the table is the average result of 7 identical models initialized with different random seeds. Values represent accuracy (%) unless indicated by †, in which case they represent Pearson correlation scaled to the range [−100,100] for easier reading. Bold values represent the best method per training dataset, per task; underlined values represent the best-performing method per task, independent of training dataset. Values marked with an asterisk (∗) are significantly different to the average performance of the best model trained on the same dataset (p<0.05). Results for every best-performing method trained on one dataset are significantly different to the best-performing method trained on the other. Statistical significance was obtained in the same way as described in table 1.'\nProblem: 'Which method significantly outperformed others in the STSB task when trained on both datasets?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/244.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experimental results. Each value shown in the table is the average result of 7 identical models initialized with different random seeds. Values represent accuracy (%) unless indicated by †, in which case they represent Pearson correlation scaled to the range [−100,100] for easier reading. Bold values represent the best method per training dataset, per task; underlined values represent the best-performing method per task, independent of training dataset. Values marked with an asterisk (∗) are significantly different to the average performance of the best model trained on the same dataset (p<0.05). Results for every best-performing method trained on one dataset are significantly different to the best-performing method trained on the other. Statistical significance was obtained in the same way as described in table 1." ], "reasoning": "The description states that the vector gate method significantly outperformed every other method in the STSB task when trained on both datasets. By examining the table, we can confirm that the vector gate method (vg) has the highest values in the STSB columns for both SNLI (71.99) and MultiNLI (72.82).", "question": "Which method significantly outperformed others in the STSB task when trained on both datasets?", "answer": { "answer": "vector gate" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe concatenation method outperformed the word-only baseline in the SST2 classification task when trained on SNLI.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/244.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experimental results. Each value shown in the table is the average result of 7 identical models initialized with different random seeds. Values represent accuracy (%) unless indicated by †, in which case they represent Pearson correlation scaled to the range [−100,100] for easier reading. Bold values represent the best method per training dataset, per task; underlined values represent the best-performing method per task, independent of training dataset. Values marked with an asterisk (∗) are significantly different to the average performance of the best model trained on the same dataset (p<0.05). Results for every best-performing method trained on one dataset are significantly different to the best-performing method trained on the other. Statistical significance was obtained in the same way as described in table 1." ], "reasoning": "The table shows that for the SST2 classification task, the word-only baseline (w) trained on SNLI scored 78.86, while the concatenation method (cat) scored 78.37. Since 78.37 is lower than 78.86, the statement is refuted.", "statement": "The concatenation method outperformed the word-only baseline in the SST2 classification task when trained on SNLI.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: System accuracy (%) bucketed by gender and difficulty (so-called “gotchas,” shaded in purple). For female pronouns, a “gotcha” sentence is one where either (1) the correct answer is occupation but the occupation is <50% female (according to BLS); or (2) the occupation is ≥50% female but the correct answer is participant; this is reversed for male pronouns. Systems do uniformly worse on “gotchas.”'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/245.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: System accuracy (%) bucketed by gender and difficulty (so-called “gotchas,” shaded in purple). For female pronouns, a “gotcha” sentence is one where either (1) the correct answer is occupation but the occupation is <50% female (according to BLS); or (2) the occupation is ≥50% female but the correct answer is participant; this is reversed for male pronouns. Systems do uniformly worse on “gotchas.”" ], "reasoning": "Systems perform worse on 'gotcha' sentences where the pronoun gender does not match the occupation's majority gender.", "description": "We also identify so-called \"gotcha\" sentences in which pronoun gender does not match the occupation's majority gender (BLS) if OCCUPATION is the correct answer; all systems perform worse on these \"gotchas.\"8 (See Table 2.)" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: System accuracy (%) bucketed by gender and difficulty (so-called “gotchas,” shaded in purple). For female pronouns, a “gotcha” sentence is one where either (1) the correct answer is occupation but the occupation is <50% female (according to BLS); or (2) the occupation is ≥50% female but the correct answer is participant; this is reversed for male pronouns. Systems do uniformly worse on “gotchas.”'\nProblem: 'Which system had the highest male score when 'gotcha' sentences were present?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/245.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: System accuracy (%) bucketed by gender and difficulty (so-called “gotchas,” shaded in purple). For female pronouns, a “gotcha” sentence is one where either (1) the correct answer is occupation but the occupation is <50% female (according to BLS); or (2) the occupation is ≥50% female but the correct answer is participant; this is reversed for male pronouns. Systems do uniformly worse on “gotchas.”" ], "reasoning": "To find the system with the highest male score when 'gotcha' sentences were present, look for the highest value in the 'Male' column where 'Gotcha' is 'yes'. The highest value is 46.7 for the Neural system.", "question": "Which system had the highest male score when 'gotcha' sentences were present?", "answer": "So, the answer is {\"answer\": \"Neural\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Rule system had the lowest female score when 'gotcha' sentences were present.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/245.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: System accuracy (%) bucketed by gender and difficulty (so-called “gotchas,” shaded in purple). For female pronouns, a “gotcha” sentence is one where either (1) the correct answer is occupation but the occupation is <50% female (according to BLS); or (2) the occupation is ≥50% female but the correct answer is participant; this is reversed for male pronouns. Systems do uniformly worse on “gotchas.”" ], "reasoning": "To verify this statement, look at the 'Female' scores for the Rule system when 'Gotcha' is 'yes'. The score is 10.0, which is indeed the lowest among all systems.", "statement": "The Rule system had the lowest female score when 'gotcha' sentences were present.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Automatic evaluation results of different models. For a detailed explanation of the baseline models, please refer to Section 3.2. The best performing model for each column is highlighted in boldface.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/246.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Automatic evaluation results of different models. For a detailed explanation of the baseline models, please refer to Section 3.2. The best performing model for each column is highlighted in boldface." ], "reasoning": "The Facts-to-seq model performs adequately, but with an additional attention mechanism, the results improve significantly. Our model outperforms all others, including the DMN+ and Static Memory approaches.", "description": "The results of the experiments are reported in Table 1.. A facts-to-seq model exploiting our positional fact encoding performs adequately. With an additional attention mechanism (Facts-to-seq w. Attention), the results are even better.. The analysis of the Static Memory approach amounts to an ablation study,. the DMN+ is even outperformed by our Facts-to-seq baseline." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Automatic evaluation results of different models. For a detailed explanation of the baseline models, please refer to Section 3.2. The best performing model for each column is highlighted in boldface.'\nProblem: 'Which model has the highest ROUGE-L score and how does it compare to the Facts-to-seq w. Attention model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/246.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Automatic evaluation results of different models. For a detailed explanation of the baseline models, please refer to Section 3.2. The best performing model for each column is highlighted in boldface." ], "reasoning": "The table shows that 'Our Model' has the highest ROUGE-L score of 0.641. The Facts-to-seq w. Attention model has a ROUGE-L score of 0.512, which is lower than 'Our Model'.", "question": "Which model has the highest ROUGE-L score and how does it compare to the Facts-to-seq w. Attention model?", "answer": { "answer": "Our Model" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Static Memory model has a higher B-1 score than the Facts-to-seq model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/246.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Automatic evaluation results of different models. For a detailed explanation of the baseline models, please refer to Section 3.2. The best performing model for each column is highlighted in boldface." ], "reasoning": "The table shows that the Static Memory model has a B-1 score of 0.374, while the Facts-to-seq model has a B-1 score of 0.404. Therefore, the statement is refuted.", "statement": "The Static Memory model has a higher B-1 score than the Facts-to-seq model.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Accuracies for the approaches. Baseline refers to the unmodified, non-adversarial InferSent.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/247.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracies for the approaches. Baseline refers to the unmodified, non-adversarial InferSent." ], "reasoning": "Both adversarial training methods perform worse than the non-adversarial InferSent baseline on the SNLI test set, with AdvCls slightly outperforming InferSent on the validation set.", "description": "Table 2 reports the results on SNLI, with the configurations that performed best on the validation set for each of the adversarial methods.. both training methods perform worse than our unmodified, non-adversarial InferSent baseline on SNLI's test set, since they remove biases that may be useful for performing this task. The difference for AdvCls is minimal, and it even slightly outperforms InferSent on the validation set. While AdvDat's results are noticeably lower than the non-adversarial InferSent, the drops are still less than 6% points." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Accuracies for the approaches. Baseline refers to the unmodified, non-adversarial InferSent.'\nProblem: 'Which model performed the best on the validation set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/247.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracies for the approaches. Baseline refers to the unmodified, non-adversarial InferSent." ], "reasoning": "The table shows the validation scores for each model. AdvCls has the highest validation score of 84.58.", "question": "Which model performed the best on the validation set?", "answer": { "answer": "AdvCls" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nAdvDat performed better than the Baseline on the test set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/247.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracies for the approaches. Baseline refers to the unmodified, non-adversarial InferSent." ], "reasoning": "The table shows that AdvDat has a test score of 78.30, which is lower than the Baseline's test score of 84.22.", "statement": "AdvDat performed better than the Baseline on the test set.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Indicator words and how correlated they are with CONTRADICTION predictions. The parentheses indicate hyper-parameter values: (λLoss,λEnc) for AdvCls and (λRand,λEnc) for AdvDat. Baseline refers to the unmodified InferSent.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/248.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Indicator words and how correlated they are with CONTRADICTION predictions. The parentheses indicate hyper-parameter values: (λLoss,λEnc) for AdvCls and (λRand,λEnc) for AdvDat. Baseline refers to the unmodified InferSent." ], "reasoning": "The table shows the top 10 most biased words in SNLI associated with the CONTRADICTION label, their frequency, empirical correlation, baseline score, and percentage decrease in correlations with CONTRADICTION predictions by three configurations of methods.", "description": "For each of the most biased words in SNLI associated with the CONTRADICTION label, we computed the probability that a model predicts an example as a contradiction, given that the hypothesis contains the word. Table 3 shows the top 10 examples in the training set. For each word w, we give its frequency in SNLI, its empirical correlation with the label and with InferSent's prediction, and the percentage decrease in correlations with CONTRADICTION predictions by three configurations of our methods. Generally, the baseline correlations are more uniform than the empirical ones (ˆp(l|w)),. However, we still observed small skews towards CONTRADICTION. Thus, we investigate whether our methods reduce the probability of predicting CONTRADICTION when a hypothesis contains an indicator word. The model trained with AdvDat (where λRand = 0.4, λEnc = 1) predicts contradiction much less frequently than InferSent on examples with these words." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Indicator words and how correlated they are with CONTRADICTION predictions. The parentheses indicate hyper-parameter values: (λLoss,λEnc) for AdvCls and (λRand,λEnc) for AdvDat. Baseline refers to the unmodified InferSent.'\nProblem: 'Which word has the highest empirical correlation with the CONTRADICTION label and what is its percentage decrease from baseline using AdvDat (0.4,1)?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/248.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Indicator words and how correlated they are with CONTRADICTION predictions. The parentheses indicate hyper-parameter values: (λLoss,λEnc) for AdvCls and (λRand,λEnc) for AdvDat. Baseline refers to the unmodified InferSent." ], "reasoning": "The word 'Nobody' has the highest empirical correlation with the CONTRADICTION label at 1.0. The percentage decrease from baseline using AdvDat (0.4,1) for 'Nobody' is 42.86.", "question": "Which word has the highest empirical correlation with the CONTRADICTION label and what is its percentage decrease from baseline using AdvDat (0.4,1)?", "answer": "So, the answer is {\"answer\": \"42.86\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe word 'asleep' has a higher baseline score than 'driving'.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/248.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Indicator words and how correlated they are with CONTRADICTION predictions. The parentheses indicate hyper-parameter values: (λLoss,λEnc) for AdvCls and (λRand,λEnc) for AdvDat. Baseline refers to the unmodified InferSent." ], "reasoning": "The baseline score for 'asleep' is -0.39, while the baseline score for 'driving' is 0.32. Therefore, 'asleep' does not have a higher baseline score than 'driving'.", "statement": "The word 'asleep' has a higher baseline score than 'driving'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: NEI F1 scores. There are two language models: one for entity, and one for non-entity. All LMs are trained on the standard Train split. At test time, the tag associated with the language model which gives the lowest perplexity is used as prediction. Consecutive tokens with the same tag are joined into a phrase. Scores are Phrase-level F1.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/249.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: NEI F1 scores. There are two language models: one for entity, and one for non-entity. All LMs are trained on the standard Train split. At test time, the tag associated with the language model which gives the lowest perplexity is used as prediction. Consecutive tokens with the same tag are joined into a phrase. Scores are Phrase-level F1." ], "reasoning": "The dataset sizes vary significantly, with Farsi having the smallest number of entity and non-entity tokens, and English having the largest.", "description": "Our datasets vary in size of entity and non-entity tokens, as shown in Table 1. The smallest, Farsi, has 4.5K entity and 50K non-entity tokens; the largest, English, has 29K entity and 170K nonentity tokens." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: NEI F1 scores. There are two language models: one for entity, and one for non-entity. All LMs are trained on the standard Train split. At test time, the tag associated with the language model which gives the lowest perplexity is used as prediction. Consecutive tokens with the same tag are joined into a phrase. Scores are Phrase-level F1.'\nProblem: 'Which language has the highest average capacity score across all models?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/249.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: NEI F1 scores. There are two language models: one for entity, and one for non-entity. All LMs are trained on the standard Train split. At test time, the tag associated with the language model which gives the lowest perplexity is used as prediction. Consecutive tokens with the same tag are joined into a phrase. Scores are Phrase-level F1." ], "reasoning": "To find the language with the highest average capacity score, we need to compare the average scores for each language across all models. The table shows that the highest average score is 53.1 for the language 'avg'.", "question": "Which language has the highest average capacity score across all models?", "answer": "So, the answer is {\"answer\": \"avg\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Skip-gram model has a higher capacity score for Farsi than the SRILM model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/249.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: NEI F1 scores. There are two language models: one for entity, and one for non-entity. All LMs are trained on the standard Train split. At test time, the tag associated with the language model which gives the lowest perplexity is used as prediction. Consecutive tokens with the same tag are joined into a phrase. Scores are Phrase-level F1." ], "reasoning": "The table shows that the Skip-gram model has a capacity score of 15.4 for Farsi, while the SRILM model has a capacity score of 38.2 for Farsi. Therefore, the statement is refuted.", "statement": "The Skip-gram model has a higher capacity score for Farsi than the SRILM model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Comparison with existing datasets. (D documents or news clusters; L labeled documents or news clusters; T event types; S slots.)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/250.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison with existing datasets. (D documents or news clusters; L labeled documents or news clusters; T event types; S slots.)" ], "reasoning": "GNBusiness has a significantly larger number of documents compared to other datasets, with a comparable number of labeled documents.", "description": "a comparison with existing event extraction and event schema induction datasets, including ASTRE (Nguyen et al., 2016a), MUC 4, ACE 20052 and ERE3, is shown in Table 2. Compared with the other datasets, GNBusiness has a much larger number of documents (i.e., news clusters in GNBusiness), and a comparable number of labeled documents." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Comparison with existing datasets. (D documents or news clusters; L labeled documents or news clusters; T event types; S slots.)'\nProblem: 'Which dataset has the highest number of labeled documents?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/250.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison with existing datasets. (D documents or news clusters; L labeled documents or news clusters; T event types; S slots.)" ], "reasoning": "By comparing the number of labeled documents (#L) across all datasets, GNBusiness has 680 labeled documents, which is the highest among the listed datasets.", "question": "Which dataset has the highest number of labeled documents?", "answer": "So, the answer is {\"answer\": \"GNBusiness\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nGNBusiness has the highest number of labeled documents among the listed datasets.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/250.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison with existing datasets. (D documents or news clusters; L labeled documents or news clusters; T event types; S slots.)" ], "reasoning": "The table shows that GNBusiness has 680 labeled documents, which is higher than the labeled documents in MUC 4, ACE 2005, ERE, and ASTRE.", "statement": "GNBusiness has the highest number of labeled documents among the listed datasets.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Varying MILk’s λ with and without mass preservation on the DeEn development set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/251.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Varying MILk’s λ with and without mass preservation on the DeEn development set." ], "reasoning": "MILk with λ = 0 showed a significant reduction in latency from 27.9, and training destabilized quickly for λ as low as 0.2.", "description": "Before preservation, MILk with a latency weight λ = 0 still showed a substantial reduction in latency from the maximum value of 27.9, indicating an intrinsic latency incentive. Furthermore, training quickly destabilized, resulting in very poor trade-offs for λs as low as 0.2." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Varying MILk’s λ with and without mass preservation on the DeEn development set.'\nProblem: 'What is the preserved DAL value for λ = 0.1?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/251.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Varying MILk’s λ with and without mass preservation on the DeEn development set." ], "reasoning": "To find the preserved DAL value for λ = 0.1, locate the row where λ = 0.1 and then find the value under the 'preserved DAL' column.", "question": "What is the preserved DAL value for λ = 0.1?", "answer": "So, the answer is {\"answer\": \"10.5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe unpreserved BLEU score for λ = 0.2 is higher than for λ = 0.1.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/251.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Varying MILk’s λ with and without mass preservation on the DeEn development set." ], "reasoning": "To verify this statement, compare the unpreserved BLEU scores for λ = 0.2 and λ = 0.1. The unpreserved BLEU score for λ = 0.2 is 25.7, which is lower than the score of 27.0 for λ = 0.1.", "statement": "The unpreserved BLEU score for λ = 0.2 is higher than for λ = 0.1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Overall performance of schema matching.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/252.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Overall performance of schema matching." ], "reasoning": "ODEE-FER achieves the highest F1 score among all methods, and ODEE-FE shows a 2% gain in F1 score over ODEE-F.", "description": "Table 4 shows the overall performance of schema matching on GNBusinessTest. From the table, we can see that ODEE-FER achieves the best F1 scores among all the methods. By comparing Nguyen et al. (2015) and ODEEF (p = 0.01), we can see that using continuous contextual features gives better performance than discrete features.. Among ODEE models, ODEE-FE gives a 2% gain in F1 score against ODEE-F,. there is a 1% gain in F" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Overall performance of schema matching.'\nProblem: 'Which method achieves the highest F1 score in schema matching?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/252.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Overall performance of schema matching." ], "reasoning": "By examining the F1 scores in the table, ODEE-FER has the highest F1 score of 49.8%.", "question": "Which method achieves the highest F1 score in schema matching?", "answer": "So, the answer is {\"answer\": \"ODEE-FER\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nODEE-FE achieves a higher F1 score than Clustering.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/252.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Overall performance of schema matching." ], "reasoning": "The table shows that ODEE-FE has an F1 score of 48.3%, while Clustering has an F1 score of 45.4%.", "statement": "ODEE-FE achieves a higher F1 score than Clustering.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Averaged slot coherence results.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/253.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Averaged slot coherence results." ], "reasoning": "ODEE-FER has the highest averaged slot coherence, while ODEE-FE and ODEE-FER have higher scores than ODEE-F.", "description": "Table 5 shows the comparison of averaged slot coherence results over all the slots in the schemas.. The averaged slot coherence of ODEE-FER is the highest,. The averaged slot coherence. of ODEE-F is comparable to that of Nguyen et al. (2015) (p = 0.3415),. The scores of ODEE-FE (p = 0.06) and ODEE-FER (p = 10−5) are both higher than that of ODEE-F," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Averaged slot coherence results.'\nProblem: 'Which method has the highest averaged slot coherence?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/253.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Averaged slot coherence results." ], "reasoning": "By comparing the averaged slot coherence values in the table, ODEE-FER has the highest value of 0.18.", "question": "Which method has the highest averaged slot coherence?", "answer": "So, the answer is {\"answer\": \"ODEE-FER\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nODEE-FE has a higher averaged slot coherence than ODEE-F.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/253.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Averaged slot coherence results." ], "reasoning": "The table shows that ODEE-FE has an averaged slot coherence of 0.16, which is higher than ODEE-F's 0.10.", "statement": "ODEE-FE has a higher averaged slot coherence than ODEE-F.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Method Comparison (%).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/254.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Method Comparison (%)." ], "reasoning": "Table 2 shows that the ED(2) method achieves the highest METEOR and ROUGE-L scores, and is strongly favored by human judges.", "description": "Table 2 presents METEOR (Denkowski and Lavie, 2014) and ROUGE-L (Lin, 2004) scores for each method, where we can see score gains on. both metrics from the Editing Mechanism.. Table 2 shows that the human judges strongly favor the abstracts from our ED(2) method." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Method Comparison (%).'\nProblem: 'Which method has the highest human preference score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/254.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Method Comparison (%)." ], "reasoning": "By examining the 'HUMAN PREFERENCE' column, we see that the ED(2) method has the highest score of 48.", "question": "Which method has the highest human preference score?", "answer": { "answer": "ED(2)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LSTM-LM method has the highest METEOR score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/254.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Method Comparison (%)." ], "reasoning": "The table shows that the LSTM-LM method has a METEOR score of 8.7, which is lower than the scores of the other methods.", "statement": "The LSTM-LM method has the highest METEOR score.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Plagiarism Check: Percentage (%) of n-grams in test abstracts generated by system/human which appeared in training data.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/255.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Plagiarism Check: Percentage (%) of n-grams in test abstracts generated by system/human which appeared in training data." ], "reasoning": "Table 3 shows that 93.4% of 6-grams generated by ED(2) did not appear in the training data, indicating the model's originality.", "description": "We also conduct a plagiarism check in Table 3, which shows that 93.4% of 6-grams generated by ED(2) did not appear in the training data, indicating that our model is not simply copying." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Plagiarism Check: Percentage (%) of n-grams in test abstracts generated by system/human which appeared in training data.'\nProblem: 'What percentage of 6-grams generated by the system did not appear in the training data?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/255.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Plagiarism Check: Percentage (%) of n-grams in test abstracts generated by system/human which appeared in training data." ], "reasoning": "The table shows that 6.6% of 6-grams generated by the system appeared in the training data. Therefore, 100% - 6.6% = 93.4% did not appear in the training data.", "question": "What percentage of 6-grams generated by the system did not appear in the training data?", "answer": "So, the answer is {\"answer\": \"93.4%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe system generated 6-grams that appeared in the training data 6.6% of the time.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/255.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Plagiarism Check: Percentage (%) of n-grams in test abstracts generated by system/human which appeared in training data." ], "reasoning": "The table shows that 6.6% of 6-grams generated by the system appeared in the training data.", "statement": "The system generated 6-grams that appeared in the training data 6.6% of the time.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Iteration comparison (%)'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/256.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Iteration comparison (%)" ], "reasoning": "The second iteration produced the best results for both METEOR and ROUGE-L scores.", "description": "We trained and evaluated our editing approach with 1-6 iterations and the experimental results (Table 5) showed that the second iteration produced the best results." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Iteration comparison (%)'\nProblem: 'Which iteration produced the highest METEOR and ROUGE-L scores?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/256.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Iteration comparison (%)" ], "reasoning": "By examining the table, the highest METEOR score is 14.0 and the highest ROUGE-L score is 20.3, both occurring at the second iteration.", "question": "Which iteration produced the highest METEOR and ROUGE-L scores?", "answer": "So, the answer is {\"answer\": \"second iteration\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe third iteration produced the best results.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/256.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Iteration comparison (%)" ], "reasoning": "The table shows that the second iteration produced the highest scores for both METEOR and ROUGE-L, not the third iteration.", "statement": "The third iteration produced the best results.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Turing Test Passing Rates.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/257.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Turing Test Passing Rates." ], "reasoning": "Non-CS judges are more easily deceived, failing more than half of the 1-to-1 sets, while domain experts fail less often, especially when more abstracts are provided.", "description": "As expected, Table 4 shows that people with less domain knowledge are more easily deceived. Specifically, non-CS human judges fail at more than half of the 1-to-1 sets for the same titles, which suggests that most of our system generated abstracts follow correct grammar and consistent writing style. Domain experts fail on 1 or 2 sets, mostly because the human written abstracts in those sets don't seem very topically relevant. Additionally, the more abstracts that we provided to human judges, the easier it is to conceal the system generated abstract amongst human generated ones." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Turing Test Passing Rates.'\nProblem: 'What percentage of non-expert Non-CS judges failed the 1-to-1 sets for the same title with 2 choices per test?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/257.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Turing Test Passing Rates." ], "reasoning": "The table shows that for the same title with 2 choices per test, 54% of non-expert Non-CS judges failed.", "question": "What percentage of non-expert Non-CS judges failed the 1-to-1 sets for the same title with 2 choices per test?", "answer": "So, the answer is {\"answer\": \"54%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nNLP Expert Seniors never failed the tests with different titles.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/257.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Turing Test Passing Rates." ], "reasoning": "The table shows that NLP Expert Seniors had a 0% failure rate for tests with different titles and 2 choices per test, but a 20% failure rate for tests with different titles and 5 choices per test.", "statement": "NLP Expert Seniors never failed the tests with different titles.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Unimodal sentiment analysis results on the CMU-MOSI test set. Numbers in bold are the best results on each modality.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/258.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Unimodal sentiment analysis results on the CMU-MOSI test set. Numbers in bold are the best results on each modality." ], "reasoning": "The S+P model achieved the best performance on the verbal modality, while the S+I model achieved the best performance on the visual modality. The S+P+I model achieved the best performance on the vocal modality.", "description": "The results of unimodal sentiment prediction experiments are shown in Table 2.. The verbal models have the best performance here,. On each modality, the best performance is achieved by a multi-task learning model.. All unimodal models have significantly different performance. p = 0.009 for S+P and S+P+I Visual models, p << 0.001 for Visual and Vocal S+I models.. In multi-task learning, the main task gains additional information from the auxillary tasks. Compared to the S model, the S+P model has increased focus on the polarity of sentiment, while the S+I model has increased focus on the intensity of sentiment. On the verbal modality, the S+P model achieved the best performance, while on the visual modality the S+I model achieved the best performance.. For the vocal modality, the S+P+I model achieved the best performance, and the S+P model yielded improved performance over that of the S model." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Unimodal sentiment analysis results on the CMU-MOSI test set. Numbers in bold are the best results on each modality.'\nProblem: 'Which model achieved the best performance on the vocal modality in terms of CC?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/258.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Unimodal sentiment analysis results on the CMU-MOSI test set. Numbers in bold are the best results on each modality." ], "reasoning": "The table shows the CC values for different models on various modalities. The highest CC value for the vocal modality is 0.153, achieved by the S+P+I model.", "question": "Which model achieved the best performance on the vocal modality in terms of CC?", "answer": "So, the answer is {\"answer\": \"S+P+I\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe S+P model achieved the best performance on the visual modality in terms of MAE.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/258.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Unimodal sentiment analysis results on the CMU-MOSI test set. Numbers in bold are the best results on each modality." ], "reasoning": "The table shows the MAE values for different models on various modalities. The lowest MAE value for the visual modality is 1.439, achieved by the S+P model.", "statement": "The S+P model achieved the best performance on the visual modality in terms of MAE.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Multimodal sentiment analysis results on the CMU-MOSI test set. Numbers in bold are the best results for each fusion strategy in each row.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/259.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Multimodal sentiment analysis results on the CMU-MOSI test set. Numbers in bold are the best results for each fusion strategy in each row." ], "reasoning": "The EF model outperforms the TFN model, and the multimodal models generally perform better than the unimodal models.", "description": "of results the multimodal The experiments are shown in Table 3.. We find that EF>HF>TFN>LF.. 4. Unlike Zadeh et al. (2017), here the EF model outperforms the TFN model. However, the TFN model achieved the best performance on the training and validation sets.. Compared to the feature concatenation used in EF, the Cartesian product used in TFN results in higher dimensionality of the multimodal input vector,5 which in turn increases the complexity of the model. Similarly, the HF model has worse performance than the EF model here, unlike in Tian et al. (2016).. In general, the multimodal models have better performance than the unimodal models.. In fact, the HF and LF models have better performance using single-task learning. For the TFN models, only the S+P model outperforms the S model, although the improvement is not significant.7 For the EF models, multi-task learning results in better performance.. Dimension of the EF input is 420, for TFN is 65,536. 6Except that the LF models often have worse performance than the verbal S+P model. p << 0.001 for TFN S+P and verbal S+P, p = 0.017 for verbal S+P and LF S. 7p = 0.105 for S TFN and S+P TFN. 8p = 0.888 for S EF and S+P EF, p = 0.029 for S EF and S+I EF, p = 0.009 for S EF and S+P+I EF." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Multimodal sentiment analysis results on the CMU-MOSI test set. Numbers in bold are the best results for each fusion strategy in each row.'\nProblem: 'Which model has the lowest MAE for the S+P+I configuration?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/259.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Multimodal sentiment analysis results on the CMU-MOSI test set. Numbers in bold are the best results for each fusion strategy in each row." ], "reasoning": "By examining the MAE values for the S+P+I configuration, the EF model has the lowest MAE of 1.172.", "question": "Which model has the lowest MAE for the S+P+I configuration?", "answer": "So, the answer is {\"answer\": \"EF\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe TFN model has the highest CC for the S+P configuration.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/259.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Multimodal sentiment analysis results on the CMU-MOSI test set. Numbers in bold are the best results for each fusion strategy in each row." ], "reasoning": "By examining the CC values for the S+P configuration, the TFN model has a CC of 0.461, which is the highest among the models listed.", "statement": "The TFN model has the highest CC for the S+P configuration.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: The BioBERT performance on the MedNLI task. Each model is trained on three different combinations of PMC and PubMed datasets (top score marked as bold).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/260.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The BioBERT performance on the MedNLI task. Each model is trained on three different combinations of PMC and PubMed datasets (top score marked as bold)." ], "reasoning": "BioBERT trained on PubMed+PMC achieves the highest accuracy in both development and test datasets.", "description": "As shown in table 2, BioBERT trained on PubMed+PMC performs the best." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: The BioBERT performance on the MedNLI task. Each model is trained on three different combinations of PMC and PubMed datasets (top score marked as bold).'\nProblem: 'Which dataset combination results in the highest test accuracy for BioBERT?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/260.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The BioBERT performance on the MedNLI task. Each model is trained on three different combinations of PMC and PubMed datasets (top score marked as bold)." ], "reasoning": "By comparing the test accuracy values for each dataset combination, the highest test accuracy is found in the +PubMed+PMC row with a value of 79.04.", "question": "Which dataset combination results in the highest test accuracy for BioBERT?", "answer": "So, the answer is {\"answer\": \"+PubMed+PMC\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBioBERT trained on +PMC alone performs better than when trained on +PubMedd alone.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/260.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The BioBERT performance on the MedNLI task. Each model is trained on three different combinations of PMC and PubMed datasets (top score marked as bold)." ], "reasoning": "By comparing the accuracy values for +PMC and +PubMedd, it is evident that +PubMedd has higher accuracy in both development (81.14 vs. 80.50) and test (78.83 vs. 78.97) datasets.", "statement": "BioBERT trained on +PMC alone performs better than when trained on +PubMedd alone.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Classification performance of the compared methods on various topics, measured by the averaged macro F1-score over ten runs on the test data.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/261.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Classification performance of the compared methods on various topics, measured by the averaged macro F1-score over ten runs on the test data." ], "reasoning": "The RCN method consistently outperformed all baselines across all topics, with statistically significant improvements at p < 0.05.", "description": "Table 2 shows the results of our method and all the baselines on tasks with different topics.. We can first observe that the proposed RCN consistently outperformed all the baselines across all topics. Despite being modest, all the improvements of RCN over the baselines are statistically significant at p < 0.05 with a two-tailed t-test.. BiLSTM performed the worst, showing that only using the RNN encoder for sequence encoding is not sufficient for obtaining optimal results.. DeAT and BiMPM performed similarly well;. RCN performed the best, with relative improvements from 2.1% to 10.4% over the second best." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Classification performance of the compared methods on various topics, measured by the averaged macro F1-score over ten runs on the test data.'\nProblem: 'Which method showed the highest performance on the FM topic?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/261.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Classification performance of the compared methods on various topics, measured by the averaged macro F1-score over ten runs on the test data." ], "reasoning": "The table shows the performance of different methods on various topics. By comparing the values for the FM topic, RCN has the highest score of 64.4±0.5.", "question": "Which method showed the highest performance on the FM topic?", "answer": "So, the answer is {\"answer\": \"RCN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBiLSTM performed the worst on the HC topic.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/261.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Classification performance of the compared methods on various topics, measured by the averaged macro F1-score over ten runs on the test data." ], "reasoning": "The table shows the performance of BiLSTM on the HC topic as 52.5±0.6, which is the lowest among all methods for this topic.", "statement": "BiLSTM performed the worst on the HC topic.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Accuracy on Test set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/262.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy on Test set." ], "reasoning": "Fine-tuning a pre-trained BERT model with an increased number of tokens significantly improves accuracy in classifying the order of paragraphs, achieving up to 84.3% accuracy.", "description": "Different approaches have been used to solve this task. The best result belongs to classifying order of paragraphs using pre-trained BERT model. It achieves around 84% accuracy on test set which outperforms other models significantly.. First, each paragraph is encoded with LSTM. The hidden state at the end of each sentence is extracted, and the resulting matrix is going through gated CNN (Dauphin et al., 2017) for extraction of single encoding for each paragraph. The accuracy is barely above 50%, which depicts that this method is not very promising.. We have used a pre-trained BERT in two different ways. First, as a feature extractor without fine-tuning, and second, by fine-tuning the weights during training.. In the case of fine-tuning, we have used different numbers for maximum sequence length to test the capability of BERT in this task.. we increased the number of tokens and accuracy respectively increases.. We found this method very promising and the accuracy significantly increases with respect to previous methods (Table 3). This result reveals fine-tuning pre-trained BERT can approximately learn the order of the paragraphs and arrow of the time in the stories." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Accuracy on Test set.'\nProblem: 'Which model achieved the highest accuracy in classifying the order of paragraphs?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/262.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy on Test set." ], "reasoning": "The table shows different models and their accuracies. The BERT Classifier with 256 tokens (128 tokens from each paragraph) achieved the highest accuracy of 0.843.", "question": "Which model achieved the highest accuracy in classifying the order of paragraphs?", "answer": "So, the answer is {\"answer\": \"BERT Classifier (256 tokens / 128 tokens from each paragraph)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe LSTM+Feed-Forward model achieved an accuracy above 0.52.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/262.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy on Test set." ], "reasoning": "The table shows that the LSTM+Feed-Forward model achieved an accuracy of 0.518, which is below 0.52.", "statement": "The LSTM+Feed-Forward model achieved an accuracy above 0.52.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Average performance across all models depending on the window position.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/263.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average performance across all models depending on the window position." ], "reasoning": "Symmetric windows generally outperform single-sided ones on analogy tasks due to their larger context, but right windows perform similarly to symmetric ones on the SimLex999 task.", "description": "Table 2 shows how the position of the context window influences the average model performance. Note that symmetric windows of, for instance, 10 are in fact 2 times larger than the 'left' or 'right'. windows of the same size, as they consider 10 words both to the left and to the right of the focus word. This is most likely why symmetric windows consistently outperform 'single-sided' ones on the analogy task, as they are able to include twice as much contextual input. However, the average performance on the semantic similarity task (as indicated by the Spearman correlation with the SimLex999 test set) does not exhibit the same trend. 'Left' windows are indeed worse than symmetric ones, but 'right' windows are on par with the symmetric windows for OpenSubtitles and only one percent point behind them for Gigaword. It means that in many cases (at least with English texts) taking into account only n context words to the right of the focus word is sufficient to achieve the same performance with SimLex999 as by using a model which additionally considers n words to the left, and thus requires significantly more training time." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Average performance across all models depending on the window position.'\nProblem: 'Which window position has the highest average performance on the analogy task?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/263.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average performance across all models depending on the window position." ], "reasoning": "To find the highest average performance on the analogy task, compare the values in the 'Analogies' column. The highest value is 0.68, which corresponds to the GW symmetric window position.", "question": "Which window position has the highest average performance on the analogy task?", "answer": "So, the answer is {\"answer\": \"GW symmetric\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe OS right window position performs better than the OS symmetric window position on the SimLex999 task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/263.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average performance across all models depending on the window position." ], "reasoning": "The table shows that the OS right window position has a SimLex999 score of 0.43, which is equal to the OS symmetric window position's score of 0.43. Therefore, the statement is refuted.", "statement": "The OS right window position performs better than the OS symmetric window position on the SimLex999 task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Average performance across all models with and without cross-sentential contexts.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/264.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average performance across all models with and without cross-sentential contexts." ], "reasoning": "Cross-sentential contexts improve analogy tasks, especially for short sentences like those in OpenSubtitles, but can be detrimental for similarity tasks with large window sizes.", "description": "For similarity tasks, cross-sentential contexts do not seem useful, and can even be detrimental for large window sizes. However, for analogy tasks, crosssentential contexts lead to improved results thanks to the increased window it provides. This is especially pronounced for corpora with short sentences such as OpenSubtitles (see Table 3)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Average performance across all models with and without cross-sentential contexts.'\nProblem: 'What is the effect of cross-sentential contexts on analogy tasks for OpenSubtitles?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/264.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average performance across all models with and without cross-sentential contexts." ], "reasoning": "The table shows that for OpenSubtitles (OS), the analogy score improves from 0.34 to 0.43 when cross-sentential contexts are used.", "question": "What is the effect of cross-sentential contexts on analogy tasks for OpenSubtitles?", "answer": "So, the answer is {\"answer\": \"0.43\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nCross-sentential contexts do not improve analogy tasks for OpenSubtitles.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/264.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average performance across all models with and without cross-sentential contexts." ], "reasoning": "The table shows that for OpenSubtitles (OS), the analogy score improves from 0.34 to 0.43 when cross-sentential contexts are used.", "statement": "Cross-sentential contexts do not improve analogy tasks for OpenSubtitles.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Average performance across all models depending on the removal of stop words.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/265.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Average performance across all models depending on the removal of stop words." ], "reasoning": "Removing stop words does not significantly affect the average model performance for the semantic similarity task, but it substantially improves performance for the analogy task.", "description": "As shown in Table 4, the removal of stop words does not really influence the average model performance for the semantic similarity task. The analogy task, however, benefits substantially from this filtering, for both corpora." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Average performance across all models depending on the removal of stop words.'\nProblem: 'How does the removal of stop words affect the performance on the analogy task for both corpora?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/265.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Average performance across all models depending on the removal of stop words." ], "reasoning": "The table shows that for the analogy task, the performance improves from 0.34 to 0.43 for the OS corpus and from 0.64 to 0.68 for the GW corpus when stop words are removed.", "question": "How does the removal of stop words affect the performance on the analogy task for both corpora?", "answer": "So, the answer is {\"answer\": \"improves\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nRemoving stop words improves the performance on the SimLex999 task for the GW corpus.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/265.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Average performance across all models depending on the removal of stop words." ], "reasoning": "The table shows that the performance on the SimLex999 task for the GW corpus remains the same (0.44) whether stop words are removed or not.", "statement": "Removing stop words improves the performance on the SimLex999 task for the GW corpus.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Mean matched validation accuracies (%) broken down by type of pooling method and presence or absence of character embeddings. Confidence intervals are calculated at 95% confidence over 10 runs for each method.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/266.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Mean matched validation accuracies (%) broken down by type of pooling method and presence or absence of character embeddings. Confidence intervals are calculated at 95% confidence over 10 runs for each method." ], "reasoning": "Both the mean method and picking the last hidden state performed slightly better than the other strategies, but none of the methods are statistically different from one another at 95% confidence.", "description": "Table 1 presents the results of using different pooling strategies for generating a raw sentence representation vector from the word vectors.. We can observe that that both the mean method, and picking the last hidden state for both directions performed slightly better than the two other strategies, however at 95% confidence we cannot assert that any of these methods is statistically different from one another.. Another interesting result, as shown by Table 1 and Table 2, is that the model seemed to be insensitive to the usage of character embeddings," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Mean matched validation accuracies (%) broken down by type of pooling method and presence or absence of character embeddings. Confidence intervals are calculated at 95% confidence over 10 runs for each method.'\nProblem: 'Which pooling strategy performed the best without using character embeddings?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/266.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Mean matched validation accuracies (%) broken down by type of pooling method and presence or absence of character embeddings. Confidence intervals are calculated at 95% confidence over 10 runs for each method." ], "reasoning": "By comparing the scores of different pooling strategies without character embeddings, the mean method has the highest score of 71.3.", "question": "Which pooling strategy performed the best without using character embeddings?", "answer": "So, the answer is {\"answer\": \"mean\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe sum method performed the worst with character embeddings.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/266.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Mean matched validation accuracies (%) broken down by type of pooling method and presence or absence of character embeddings. Confidence intervals are calculated at 95% confidence over 10 runs for each method." ], "reasoning": "By comparing the scores of different pooling strategies with character embeddings, the sum method has a score of 70.9, which is not the lowest. The max method has the lowest score of 71.0.", "statement": "The sum method performed the worst with character embeddings.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Validation accuracies (%) for our best model broken down by genre. Both CBOW and ESIM results are reported as in (Williams et al., 2017).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/267.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Validation accuracies (%) for our best model broken down by genre. Both CBOW and ESIM results are reported as in (Williams et al., 2017)." ], "reasoning": "The best model achieved high accuracies in both matched and mismatched genres, performing similarly to ESIM overall.", "description": "In Table 3 we report the accuracies obtained by our best model in both matched (first 5 genres) and mismatched (last 5 genres) development sets.. We can observe that our implementation performed like ESIM overall," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Validation accuracies (%) for our best model broken down by genre. Both CBOW and ESIM results are reported as in (Williams et al., 2017).'\nProblem: 'What is the accuracy of the best model in the Government genre?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/267.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Validation accuracies (%) for our best model broken down by genre. Both CBOW and ESIM results are reported as in (Williams et al., 2017)." ], "reasoning": "The best model's accuracy in the Government genre can be found under the InnerAtt column for the Government row.", "question": "What is the accuracy of the best model in the Government genre?", "answer": "So, the answer is {\"answer\": \"75.2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe best model achieved the highest accuracy in the Letters genre.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/267.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Validation accuracies (%) for our best model broken down by genre. Both CBOW and ESIM results are reported as in (Williams et al., 2017)." ], "reasoning": "The highest accuracy for the best model (InnerAtt) is in the Letters genre with a score of 75.4.", "statement": "The best model achieved the highest accuracy in the Letters genre.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: GLUE task performance of BERT models with different initialization. We report the scores on the validation, rather than test data, so these results differ from the original BERT paper.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/268.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: GLUE task performance of BERT models with different initialization. We report the scores on the validation, rather than test data, so these results differ from the original BERT paper." ], "reasoning": "Finetuned BERT significantly outperforms pre-trained BERT on all tasks, while BERT initialized with random weights performs worse, especially on STS-B and QNLI.", "description": "Table 1 shows that finetuned BERT outperforms pre-trained BERT by a significant margin on all the tasks (with an average of 35.9 points of absolute difference).. BERT with weights initialized from normal distribution and further fine-tuned for a given task consistently produces lower scores than the ones achieved with pre-trained BERT. In fact, for some tasks (STS-B and QNLI), initialization with random weights yields worse performance than pre-trained BERT without fine-tuning." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: GLUE task performance of BERT models with different initialization. We report the scores on the validation, rather than test data, so these results differ from the original BERT paper.'\nProblem: 'What is the average absolute difference in performance between pre-trained BERT and fine-tuned BERT initialized with pre-trained weights across all tasks?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/268.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: GLUE task performance of BERT models with different initialization. We report the scores on the validation, rather than test data, so these results differ from the original BERT paper." ], "reasoning": "To find the average absolute difference, calculate the difference for each task between pre-trained BERT and fine-tuned BERT initialized with pre-trained weights, then average these differences.", "question": "What is the average absolute difference in performance between pre-trained BERT and fine-tuned BERT initialized with pre-trained weights across all tasks?", "answer": "So, the answer is {\"answer\": \"35.9\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFine-tuned BERT initialized with normal distribution performs better than pre-trained BERT on the SST-2 task.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/268.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: GLUE task performance of BERT models with different initialization. We report the scores on the validation, rather than test data, so these results differ from the original BERT paper." ], "reasoning": "The table shows that on the SST-2 task, fine-tuned BERT initialized with normal distribution scores 80.5, while pre-trained BERT scores 49.1, indicating better performance.", "statement": "Fine-tuned BERT initialized with normal distribution performs better than pre-trained BERT on the SST-2 task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparisons with some recent points in the literature. Scores are tokenized BLEU.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/269.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparisons with some recent points in the literature. Scores are tokenized BLEU." ], "reasoning": "Our EnFr results are close to GNMT (Wu et al., 2016) and within 2 BLEU points of Chen et al. (2018)'s RNN and Transformer models, while exceeding Lee et al. (2017) by 3-6 points.", "description": "First, from the EnFr results in Table 3, we are in line with GNMT (Wu et al., 2016), and within 2 BLEU points of the RNN and Transformer models investigated by Chen et al. (2018).. we compare quite favorably with Lee et al. (2017), exceeding their reported scores by 3-6 points," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Comparisons with some recent points in the literature. Scores are tokenized BLEU.'\nProblem: 'How do our EnFr results compare to those of Wu et al. (2016) and Lee et al. (2017)?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/269.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparisons with some recent points in the literature. Scores are tokenized BLEU." ], "reasoning": "Our EnFr results are 38.8 BLEU points, which is close to Wu et al. (2016) with 39.0 BLEU points, and exceed Lee et al. (2017) by 3-6 points.", "question": "How do our EnFr results compare to those of Wu et al. (2016) and Lee et al. (2017)?", "answer": { "answer": "38.8" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nOur EnFr results are within 2 BLEU points of Wu et al. (2016).\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/269.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparisons with some recent points in the literature. Scores are tokenized BLEU." ], "reasoning": "The table shows that our EnFr results are 38.8 BLEU points, while Wu et al. (2016) has 39.0 BLEU points, which is within 2 BLEU points.", "statement": "Our EnFr results are within 2 BLEU points of Wu et al. (2016).", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Character versus BPE translation.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/270.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Character versus BPE translation." ], "reasoning": "Character-level systems consistently outperform BPE across all language pairs in terms of Tokenized BLEU scores.", "description": "Table 2 clearly shows the characterlevel systems outperforming BPE for all language pairs." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Character versus BPE translation.'\nProblem: 'Which language pair shows the highest improvement in Tokenized BLEU score when using character-level systems over BPE?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/270.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Character versus BPE translation." ], "reasoning": "By comparing the 'Tokenized BLEU Delta' column, the highest improvement is observed for the DeEn language pair with a delta of 1.9.", "question": "Which language pair shows the highest improvement in Tokenized BLEU score when using character-level systems over BPE?", "answer": { "answer": "DeEn" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe FiEn language pair has the lowest Tokenized BLEU score using character-level systems.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/270.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Character versus BPE translation." ], "reasoning": "By examining the 'Tokenized BLEU Char' column, the FiEn language pair has the lowest score of 19.3.", "statement": "The FiEn language pair has the lowest Tokenized BLEU score using character-level systems.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Error counts out of 100 randomly sampled examples from the DeEn test set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/271.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Error counts out of 100 randomly sampled examples from the DeEn test set." ], "reasoning": "BPE and character systems differ significantly in lexical choice errors and dropped content, with BPE struggling more with German compound nouns.", "description": "BPE and character sys. tems differ most in the number of lexical choice errors, and in the extent to which they drop content.. Regarding lexical choice, the two systems differ not only in the number of errors, but in the nature of those errors. In particular, the BPE model had more trouble handling German compound nouns.. We also found that both systems occasionally mistranslate proper names." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Error counts out of 100 randomly sampled examples from the DeEn test set.'\nProblem: 'Which error type shows the greatest difference between BPE and character systems?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/271.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Error counts out of 100 randomly sampled examples from the DeEn test set." ], "reasoning": "To find the error type with the greatest difference, subtract the number of errors in the character system from the BPE system for each error type. The largest difference is in 'Lexical Choice' with a difference of 11 errors (19 - 8).", "question": "Which error type shows the greatest difference between BPE and character systems?", "answer": "So, the answer is {\"answer\": \"Lexical Choice\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BPE system has more trouble with German compound nouns than the character system.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/271.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Error counts out of 100 randomly sampled examples from the DeEn test set." ], "reasoning": "The table shows that the BPE system has 13 errors in 'Compounds' while the character system has only 1 error, supporting the statement.", "statement": "The BPE system has more trouble with German compound nouns than the character system.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Compression results on WMT15 DeEn. The Comp. column shows the ratio of total computations carried out in the encoder.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/272.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Compression results on WMT15 DeEn. The Comp. column shows the ratio of total computations carried out in the encoder." ], "reasoning": "The 3-HM configuration achieves better compression and slightly better performance than 2-HM, while HM gating results in less compression but better performance than fixed-stride techniques.", "description": "Unfortunately, even at just 1k vocabulary items, BPE has already lost a BLEU point with respect to the character model.. Comparing the performance of our Pooled BiLSTM model against BPE, we notice that for a comparable level of compression (BPE size of 1k), BPE out-performs the pooled model by around 0.5 BLEU points. At a similar level of performance (BPE size of 4k), BPE has significantly shorter sequences.. As shown in table 6, the 3-HM configuration achieves much better compression even when this is accounted for, and also gives slightly better performance than 2-HM. In general, HM gating results in less compression but better performance than the fixed-stride techniques." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Compression results on WMT15 DeEn. The Comp. column shows the ratio of total computations carried out in the encoder.'\nProblem: 'Which configuration achieves the best compression while maintaining a BLEU score above 30?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/272.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Compression results on WMT15 DeEn. The Comp. column shows the ratio of total computations carried out in the encoder." ], "reasoning": "From the table, the HM, 2-layer configuration has a compression of 0.89 and a BLEU score of 30.9, which is the best compression while maintaining a BLEU score above 30.", "question": "Which configuration achieves the best compression while maintaining a BLEU score above 30?", "answer": { "answer": "HM, 2-layer" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BiLSTM model with a BPE size of 1k has a BLEU score higher than 30.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/272.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Compression results on WMT15 DeEn. The Comp. column shows the ratio of total computations carried out in the encoder." ], "reasoning": "According to the table, the BiLSTM model with a BPE size of 1k has a BLEU score of 30.5, which is higher than 30.", "statement": "The BiLSTM model with a BPE size of 1k has a BLEU score higher than 30.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: micro-F1 (μF1) and ensembled F1 (eF1) performance of each system on the PHEME dataset. Performance is averaged across the five splits of Zubiaga et al. (2017). Results show the mean, standard deviation, and ensembled score across 15 seeds. Bold indicates best performance, underline indicates second best.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/273.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: micro-F1 (μF1) and ensembled F1 (eF1) performance of each system on the PHEME dataset. Performance is averaged across the five splits of Zubiaga et al. (2017). Results show the mean, standard deviation, and ensembled score across 15 seeds. Bold indicates best performance, underline indicates second best." ], "reasoning": "BERT-based models showed significant improvements over the baseline and BiLSTM, with PU learning further enhancing performance, especially when ensembled.", "description": "The results for the tested systems are given in Table 2. Again we saw large gains from the BERT. based models over the baseline from (Zubiaga et al., 2017) and the 2-layer BiLSTM. Compared to training solely on PHEME, fine tuning from basic citation needed detection saw very little improvement (0.1 F1 points). However, fine tuning with a model trained using PU learning led to an increase of 1 F1 point over the non-PU learning model, indicating that PU learning enables the Wikipedia data to be useful for transferring to rumour detection. For PUC, we saw an improvement of 0.7 F1 points over the baseline and lower overall variance than vanilla PU learning, meaning that the results with PUC are more consistent across runs. When models are ensembled, pretraining with vanilla PU learning improved over no pretraining by almost 2 F1 points." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: micro-F1 (μF1) and ensembled F1 (eF1) performance of each system on the PHEME dataset. Performance is averaged across the five splits of Zubiaga et al. (2017). Results show the mean, standard deviation, and ensembled score across 15 seeds. Bold indicates best performance, underline indicates second best.'\nProblem: 'Which method showed the highest eF1 score and what was the value?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/273.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: micro-F1 (μF1) and ensembled F1 (eF1) performance of each system on the PHEME dataset. Performance is averaged across the five splits of Zubiaga et al. (2017). Results show the mean, standard deviation, and ensembled score across 15 seeds. Bold indicates best performance, underline indicates second best." ], "reasoning": "By examining the eF1 column, the highest value is 0.682, achieved by the 'BERT + Wiki + PU' method.", "question": "Which method showed the highest eF1 score and what was the value?", "answer": "So, the answer is {\"answer\": \"BERT + Wiki + PU\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BERT + Wiki + PU method achieved the highest eF1 score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/273.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: micro-F1 (μF1) and ensembled F1 (eF1) performance of each system on the PHEME dataset. Performance is averaged across the five splits of Zubiaga et al. (2017). Results show the mean, standard deviation, and ensembled score across 15 seeds. Bold indicates best performance, underline indicates second best." ], "reasoning": "The table shows that the 'BERT + Wiki + PU' method has the highest eF1 score of 0.682, which is higher than any other method listed.", "statement": "The BERT + Wiki + PU method achieved the highest eF1 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Mean average precision (MAP) of models on political speeches. Bold indicates best performance, underline indicates second best.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/274.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Mean average precision (MAP) of models on political speeches. Bold indicates best performance, underline indicates second best." ], "reasoning": "The vanilla BERT model achieved the highest MAP score, while the BERT + Wiki + PUC model had the lowest performance.", "description": "The results for political speech check-worthiness detection are given in Table 3. We found that the vanilla BERT model performed the best of all models. As we added transfer learning and PU learning, the performance steadily dropped, with the worst performing model being the one using PUC." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Mean average precision (MAP) of models on political speeches. Bold indicates best performance, underline indicates second best.'\nProblem: 'Which model had the lowest MAP score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/274.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Mean average precision (MAP) of models on political speeches. Bold indicates best performance, underline indicates second best." ], "reasoning": "The table shows that the BERT + Wiki + PUC model had the lowest MAP score of 0.321 ± 0.031.", "question": "Which model had the lowest MAP score?", "answer": "So, the answer is {\"answer\": \"BERT + Wiki + PUC\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BERT + Wiki model outperformed the Hansen et al. 2019 model.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/274.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Mean average precision (MAP) of models on political speeches. Bold indicates best performance, underline indicates second best." ], "reasoning": "The table shows that the BERT + Wiki model had a MAP score of 0.339 ± 0.025, which is higher than the Hansen et al. 2019 model's MAP score of 0.302.", "statement": "The BERT + Wiki model outperformed the Hansen et al. 2019 model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: F1 score comparing manual relabelling of the top 100 predictions by PUC model with the original labels in each dataset by two different annotators. Italics are average value between the two annotators.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/275.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F1 score comparing manual relabelling of the top 100 predictions by PUC model with the original labels in each dataset by two different annotators. Italics are average value between the two annotators." ], "reasoning": "The Wikipedia and Twitter datasets had high F1 scores (> 0.8) from both annotators, indicating more general labels. For political speeches, human annotators found more check-worthy examples than those labeled in the dataset.", "description": "Our results are given in Table 4.. We found that the Wikipedia and Twitter datasets contained labels which were more general, evidenced by similar high F1 scores from both annotators (> 0.8). For political speeches, we observed that the human annotators both found many more examples to be check-worthy than were labelled in the dataset." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: F1 score comparing manual relabelling of the top 100 predictions by PUC model with the original labels in each dataset by two different annotators. Italics are average value between the two annotators.'\nProblem: 'Which dataset had the highest recall score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/275.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F1 score comparing manual relabelling of the top 100 predictions by PUC model with the original labels in each dataset by two different annotators. Italics are average value between the two annotators." ], "reasoning": "By examining the recall (R) column, the highest recall score is found in the Politics dataset with a value of 1.0.", "question": "Which dataset had the highest recall score?", "answer": "So, the answer is {\"answer\": \"Politics\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Twitter dataset had the highest precision score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/275.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F1 score comparing manual relabelling of the top 100 predictions by PUC model with the original labels in each dataset by two different annotators. Italics are average value between the two annotators." ], "reasoning": "By examining the precision (P) column, the highest precision score is found in the Wikipedia dataset with a value of 0.875, not in the Twitter dataset.", "statement": "The Twitter dataset had the highest precision score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: The comparison result of the number of corrected prediction, precision, recall, and f1-score'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/276.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: The comparison result of the number of corrected prediction, precision, recall, and f1-score" ], "reasoning": "Our method using n-gram auto-sklearn achieved the highest number of correct predictions and F1 values across all datasets compared to other tools.", "description": "Table. shows the number of correct predictions, precision, recall, and F1 values with all tools including our method (n-gram auto-sklearn).. We can see that the number of correct predictions are higher with our method in all three datasets, and our method achieved the highest F1 values for all three positive, all three negative, and one neutral.. In summary, our method using n-gram IDF and automated machine learning (auto-sklearn) largely outperformed existing sentiment analysis tools." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE I: The comparison result of the number of corrected prediction, precision, recall, and f1-score'\nProblem: 'Which tool achieved the highest positive F1 value for the App reviews dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/276.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: The comparison result of the number of corrected prediction, precision, recall, and f1-score" ], "reasoning": "By examining the App reviews dataset, we see that the highest positive F1 value is 0.853, achieved by the N-gram auto-sklearn tool.", "question": "Which tool achieved the highest positive F1 value for the App reviews dataset?", "answer": "So, the answer is {\"answer\": \"N-gram auto-sklearn\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nN-gram auto-sklearn achieved the highest neutral recall for the Stack Overflow dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/276.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: The comparison result of the number of corrected prediction, precision, recall, and f1-score" ], "reasoning": "By examining the Stack Overflow dataset, we see that the highest neutral recall value is 0.941, achieved by NLTK, not N-gram auto-sklearn.", "statement": "N-gram auto-sklearn achieved the highest neutral recall for the Stack Overflow dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Correlation results for different configurations of our proposed approach and a competitor baseline based on cosine similarity of word embeddings.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/277.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Correlation results for different configurations of our proposed approach and a competitor baseline based on cosine similarity of word embeddings." ], "reasoning": "The 10rvw variant has higher scores in most metrics compared to the word-level baseline.", "description": "Table 3 shows that the 10rvw variant consistently outperforms the word-level baseline." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Correlation results for different configurations of our proposed approach and a competitor baseline based on cosine similarity of word embeddings.'\nProblem: 'Which variant has the highest average score in the 'semeval17' category?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/277.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Correlation results for different configurations of our proposed approach and a competitor baseline based on cosine similarity of word embeddings." ], "reasoning": "To find the variant with the highest average score in the 'semeval17' category, compare the 'semeval17 avg.' column for all variants. The highest value is 72.6, which corresponds to the 1800rvr variant.", "question": "Which variant has the highest average score in the 'semeval17' category?", "answer": { "answer": "1800rvr" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe 10rvw variant has the highest score in the 'mc s' category.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/277.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Correlation results for different configurations of our proposed approach and a competitor baseline based on cosine similarity of word embeddings." ], "reasoning": "To verify this statement, compare the 'mc s' scores for all variants. The highest score is 80.2, which corresponds to the 10rvw variant.", "statement": "The 10rvw variant has the highest score in the 'mc s' category.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Results on the NIST Chinese-English translation task. “Params” denotes the number of model parameters. “Emb.” represents the number of parameters used for word representation. “Red.” represents the reduction rate of the standard size. The results of SMT* and RNNsearch* are reported by Kuang et al. Kuang et al. (2018) with the same datasets and vocabulary settings. “↑” indicates the result is significantly better than that of the vanilla Transformer (p<0.01), while “⇑” indicates the result is significantly better than that of all other Transformer models (p<0.01). All significance tests are measured by paired bootstrap resampling Koehn (2004).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/278.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on the NIST Chinese-English translation task. “Params” denotes the number of model parameters. “Emb.” represents the number of parameters used for word representation. “Red.” represents the reduction rate of the standard size. The results of SMT* and RNNsearch* are reported by Kuang et al. Kuang et al. (2018) with the same datasets and vocabulary settings. “↑” indicates the result is significantly better than that of the vanilla Transformer (p<0.01), while “⇑” indicates the result is significantly better than that of all other Transformer models (p<0.01). All significance tests are measured by paired bootstrap resampling Koehn (2004)." ], "reasoning": "Transformer models outperform SMT and RNNsearch models, and the proposed method further improves performance while using fewer parameters.", "description": "Table 1: Results on the NIST Chinese-English translation task. \"Params\" denotes the number of model parameters. \"Emb.\" represents the number of parameters used for word representation. \"Red.\" represents the reduction rate of the standard size. The results of SMT* and RNNsearch* are reported by Kuang et al. (2018) with the same datasets and vocabulary settings. \"↑\" indicates the result is significantly better than that of the vanilla Transformer (p < 0.01), while \"⇑\" indicates the result is significantly better than that of all other Transformer models (p < 0.01). All significance tests are measured by paired bootstrap resampling (Koehn, 2004).. Table 1 reports the results on the NIST ChineseEnglish test sets. It is observed that the Transformer models significantly outperform SMT and RNNsearch models. Therefore, we decide to implement all of our experiments based on Transformer architecture. The direct bridging model can further improve the translation quality of the Transformer baseline. The decoder WT model improves the translation quality while reducing the number of parameters for the word representation. This improved performance happens because there are fewer model parameters, which prevents over-fitting (Press and Wolf, 2017). Finally, the performance is further improved by the proposed method while using even fewer parameters than other models." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Results on the NIST Chinese-English translation task. “Params” denotes the number of model parameters. “Emb.” represents the number of parameters used for word representation. “Red.” represents the reduction rate of the standard size. The results of SMT* and RNNsearch* are reported by Kuang et al. Kuang et al. (2018) with the same datasets and vocabulary settings. “↑” indicates the result is significantly better than that of the vanilla Transformer (p<0.01), while “⇑” indicates the result is significantly better than that of all other Transformer models (p<0.01). All significance tests are measured by paired bootstrap resampling Koehn (2004).'\nProblem: 'Which model has the highest overall score while using the fewest parameters?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/278.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on the NIST Chinese-English translation task. “Params” denotes the number of model parameters. “Emb.” represents the number of parameters used for word representation. “Red.” represents the reduction rate of the standard size. The results of SMT* and RNNsearch* are reported by Kuang et al. Kuang et al. (2018) with the same datasets and vocabulary settings. “↑” indicates the result is significantly better than that of the vanilla Transformer (p<0.01), while “⇑” indicates the result is significantly better than that of all other Transformer models (p<0.01). All significance tests are measured by paired bootstrap resampling Koehn (2004)." ], "reasoning": "The Shared-private Transformer model has the highest overall score (41.61) and uses the fewest parameters (62.8M).", "question": "Which model has the highest overall score while using the fewest parameters?", "answer": { "answer": "Shared-private Transformer" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Vanilla Transformer model has the highest score on the MT04 test set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/278.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on the NIST Chinese-English translation task. “Params” denotes the number of model parameters. “Emb.” represents the number of parameters used for word representation. “Red.” represents the reduction rate of the standard size. The results of SMT* and RNNsearch* are reported by Kuang et al. Kuang et al. (2018) with the same datasets and vocabulary settings. “↑” indicates the result is significantly better than that of the vanilla Transformer (p<0.01), while “⇑” indicates the result is significantly better than that of all other Transformer models (p<0.01). All significance tests are measured by paired bootstrap resampling Koehn (2004)." ], "reasoning": "The Shared-private Transformer model has the highest score on the MT04 test set (44.53), not the Vanilla Transformer model.", "statement": "The Vanilla Transformer model has the highest score on the MT04 test set.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results on the WMT English-German translation task. “‡” indicates the result is significantly better than the vanilla Transformer model (p<0.05).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/279.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on the WMT English-German translation task. “‡” indicates the result is significantly better than the vanilla Transformer model (p<0.05)." ], "reasoning": "The Shared-private model achieves a significantly better BLEU score than the vanilla Transformer model on the WMT English-German translation task.", "description": "Table 2: Results on the WMT English-German translation task. \"‡\" indicates the result is significantly better than the vanilla Transformer model (p < 0.05).. Similar observations are obtained on the English-German translation task, as shown in Table 2." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results on the WMT English-German translation task. “‡” indicates the result is significantly better than the vanilla Transformer model (p<0.05).'\nProblem: 'Which model achieves the highest BLEU score and how does it compare to the vanilla Transformer model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/279.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on the WMT English-German translation task. “‡” indicates the result is significantly better than the vanilla Transformer model (p<0.05)." ], "reasoning": "The Shared-private model achieves the highest BLEU score of 28.06, which is significantly better than the vanilla Transformer's BLEU score of 27.62.", "question": "Which model achieves the highest BLEU score and how does it compare to the vanilla Transformer model?", "answer": "So, the answer is {\"answer\": \"Shared-private\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Decoder WT model has the highest parameter count among all models.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/279.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on the WMT English-German translation task. “‡” indicates the result is significantly better than the vanilla Transformer model (p<0.05)." ], "reasoning": "The table shows that the Decoder WT model has 80.4M parameters, which is less than the Vanilla model (98.7M) and the Direct bridging model (98.9M).", "statement": "The Decoder WT model has the highest parameter count among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results on the IWSLT {Ar, Ja, Ko, Zh}-to-En translation tasks. These distant language pairs belonging to 5 different language families and written in 5 different alphabets.“↑” indicates the result is significantly better than that of the vanilla Transformer (p<0.01).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/280.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the IWSLT {Ar, Ja, Ko, Zh}-to-En translation tasks. These distant language pairs belonging to 5 different language families and written in 5 different alphabets.“↑” indicates the result is significantly better than that of the vanilla Transformer (p<0.01)." ], "reasoning": "The proposed method consistently outperforms the vanilla Transformer model on IWSLT translation tasks for distant language pairs.", "description": "Table 3: Results on the IWSLT {Ar, Ja, Ko, Zh}-to-En translation tasks. These distant language pairs belonging to 5 different language families and written in 5 different alphabets.\"↑\" indicates the result is significantly better than that of the vanilla Transformer (p < 0.01).. Table 3 shows the results on the small-scale IWSLT translation tasks. We observe that the proposed method stays consistently better than the vanilla model on these distant language pairs." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Results on the IWSLT {Ar, Ja, Ko, Zh}-to-En translation tasks. These distant language pairs belonging to 5 different language families and written in 5 different alphabets.“↑” indicates the result is significantly better than that of the vanilla Transformer (p<0.01).'\nProblem: 'Which language pair shows the highest BLEU score improvement with the shared-private model compared to the vanilla model?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/280.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the IWSLT {Ar, Ja, Ko, Zh}-to-En translation tasks. These distant language pairs belonging to 5 different language families and written in 5 different alphabets.“↑” indicates the result is significantly better than that of the vanilla Transformer (p<0.01)." ], "reasoning": "By comparing the BLEU scores of the shared-private model and the vanilla model for each language pair, we see that Ar⇒En has the highest improvement from 28.36 to 29.71.", "question": "Which language pair shows the highest BLEU score improvement with the shared-private model compared to the vanilla model?", "answer": "So, the answer is {\"answer\": \"Ar⇒En\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe shared-private model reduces the embedding size by more than 50% for all language pairs.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/280.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the IWSLT {Ar, Ja, Ko, Zh}-to-En translation tasks. These distant language pairs belonging to 5 different language families and written in 5 different alphabets.“↑” indicates the result is significantly better than that of the vanilla Transformer (p<0.01)." ], "reasoning": "By examining the 'Emb. Red.' column, we see that the reduction percentages for Ar⇒En, Ja⇒En, Ko⇒En, and Zh⇒En are 50%, 48.0%, 47.4%, and 49.6% respectively, which are all less than or equal to 50%.", "statement": "The shared-private model reduces the embedding size by more than 50% for all language pairs.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Performance of models using different sharing coefficients on the validation set of the NIST Chinese-English translation task.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/281.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of models using different sharing coefficients on the validation set of the NIST Chinese-English translation task." ], "reasoning": "The proposed method uses different sharing coefficients to balance parameter reduction and translation quality improvement, achieving the highest BLEU score with λ = (0.9, 0.7, 0.5).", "description": "Table 4: Performance of models using different sharing coefficients on the validation set of the NIST ChineseEnglish translation task.. As shown in Table 4, the decoder WT model can be seen as a kind of shared-private method where zero features are shared between the source and target word embeddings. For the proposed method, λ = (0.5, 0.5, 0.5) and λ = (1, 1, 1) are, respectively, used for sharing half and all features between the embeddings of all categories of words. This allows the model to significantly reduce the number of parameters and also improve the translation quality. For comparison purpose, we also consider sharing a large part of the features among the unrelated words by setting s3 to 0.9, i.e. λ = (0.5, 0.7, 0.9). We argue that it is hard for" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Performance of models using different sharing coefficients on the validation set of the NIST Chinese-English translation task.'\nProblem: 'Which model configuration achieved the highest BLEU score and what was the corresponding embedding size?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/281.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of models using different sharing coefficients on the validation set of the NIST Chinese-English translation task." ], "reasoning": "By examining the table, the highest BLEU score is 42.57, achieved by the shared-private model with λ = (0.9, 0.7, 0.5) and an embedding size of 18.7M.", "question": "Which model configuration achieved the highest BLEU score and what was the corresponding embedding size?", "answer": "So, the answer is {\"answer\": \"18.7\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe shared-private model with λ = (1, 1, 1) achieved the highest BLEU score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/281.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of models using different sharing coefficients on the validation set of the NIST Chinese-English translation task." ], "reasoning": "The table shows that the highest BLEU score is 42.57, achieved by the shared-private model with λ = (0.9, 0.7, 0.5), not λ = (1, 1, 1).", "statement": "The shared-private model with λ = (1, 1, 1) achieved the highest BLEU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Average time for users to set up the tool and identify verbs in a 623 word news article. Only one participant managed to install and use brat, taking 18 minutes on Ubuntu. The differences between GATE and either slate or YEDDA are significant at the 0.01 level according to a t-test.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/282.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average time for users to set up the tool and identify verbs in a 623 word news article. Only one participant managed to install and use brat, taking 18 minutes on Ubuntu. The differences between GATE and either slate or YEDDA are significant at the 0.01 level according to a t-test." ], "reasoning": "SLATE and YEDDA are easy to install with minimal dependencies, while brat is difficult to install, with only two participants managing to do so.", "description": "Table 2 presents the time required to install each tool and complete the first annotation task.. SLATE and YEDDA are comparable in effort, which fits with their common design as simple tools with minimal dependencies. Participants had great difficulty with brat, with only two managing to install it, one just as their time finished." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Average time for users to set up the tool and identify verbs in a 623 word news article. Only one participant managed to install and use brat, taking 18 minutes on Ubuntu. The differences between GATE and either slate or YEDDA are significant at the 0.01 level according to a t-test.'\nProblem: 'Which tool took the longest time to install on macOS?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/282.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average time for users to set up the tool and identify verbs in a 623 word news article. Only one participant managed to install and use brat, taking 18 minutes on Ubuntu. The differences between GATE and either slate or YEDDA are significant at the 0.01 level according to a t-test." ], "reasoning": "By comparing the installation times on macOS for each tool, GATE took the longest time at 22 minutes.", "question": "Which tool took the longest time to install on macOS?", "answer": "So, the answer is {\"answer\": \"GATE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSLATE took less time to install on Ubuntu than on macOS.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/282.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average time for users to set up the tool and identify verbs in a 623 word news article. Only one participant managed to install and use brat, taking 18 minutes on Ubuntu. The differences between GATE and either slate or YEDDA are significant at the 0.01 level according to a t-test." ], "reasoning": "The table shows that SLATE took 10 minutes to install on Ubuntu and 16 minutes on macOS, confirming the statement.", "statement": "SLATE took less time to install on Ubuntu than on macOS.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Perplexity Scores'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/283.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Perplexity Scores" ], "reasoning": "Syntree2vec generally has lower perplexity scores than word2vec and node2vec across most data sizes, indicating better performance.", "description": "Since the loss, perplexity of syntree2vec is lower than word2vec, node2vec over most of the data sizes given below we say that the syntree2vec performs slightly better than both of them.. There is a clear margin of difference between the perplexity scores of node2vec and syntree2vec." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Perplexity Scores'\nProblem: 'Which model has the lowest perplexity score for the smallest data size?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/283.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Perplexity Scores" ], "reasoning": "For the smallest data size (0.01MB), compare the perplexity scores of node2vec, syntree2vec, and word2vec. Syntree2vec has the lowest score of 20.11.", "question": "Which model has the lowest perplexity score for the smallest data size?", "answer": "So, the answer is {\"answer\": \"syntree2vec\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nSyntree2vec has a lower perplexity score than word2vec for the largest data size.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/283.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Perplexity Scores" ], "reasoning": "For the largest data size (0.15MB), compare the perplexity scores of syntree2vec and word2vec. Both have the same score of 71.01.", "statement": "Syntree2vec has a lower perplexity score than word2vec for the largest data size.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Label description for ASNQ. Here S, LA, SA refer to answer sentence, long answer passage and short answer phrase respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/284.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Label description for ASNQ. Here S, LA, SA refer to answer sentence, long answer passage and short answer phrase respectively." ], "reasoning": "ASNQ has 57,242 distinct questions in the training set and 2,672 distinct questions in the dev set, with statistics reported in Table 1.", "description": "For each question in ASNQ, the positive candidate answers are those sentences that occur in the long answer paragraphs in NQ and contain annotated short answers. The remaining sentences from the document are labeled as negative for the target question.. while the ASNQ statistics are reported in Table 1.. ASNQ contains 57,242 distinct questions in the training set and 2,672 distinct questions in the dev. set," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Label description for ASNQ. Here S, LA, SA refer to answer sentence, long answer passage and short answer phrase respectively.'\nProblem: 'How many training examples are there for sentences that are part of the long answer but do not contain short answers?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/284.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Label description for ASNQ. Here S, LA, SA refer to answer sentence, long answer passage and short answer phrase respectively." ], "reasoning": "To find the number of training examples for sentences that are part of the long answer but do not contain short answers, look at the row where S ∈ LA is 'Yes' and SA ∈ S is 'No'. The corresponding value in the '# Train' column is 442,140.", "question": "How many training examples are there for sentences that are part of the long answer but do not contain short answers?", "answer": { "answer": "442,140" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThere are more than 60,000 training examples for sentences that are part of the long answer and contain short answers.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/284.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Label description for ASNQ. Here S, LA, SA refer to answer sentence, long answer passage and short answer phrase respectively." ], "reasoning": "To verify this statement, look at the row where both S ∈ LA and SA ∈ S are 'Yes'. The corresponding value in the '# Train' column is 61,186, which is more than 60,000.", "statement": "There are more than 60,000 training examples for sentences that are part of the long answer and contain short answers.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Performance of different models on WikiQA dataset. Here Comp-Agg + LM + LC refers to a Compare-Aggregate model with Language Modeling and Latent Clustering as proposed by Yoon et al. DBLP:journals/corr/abs-1905-12897. TL(QNLI) refers to Transfer Learning from the QNLI corpus. L and B stand for Large and Base, respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/285.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of different models on WikiQA dataset. Here Comp-Agg + LM + LC refers to a Compare-Aggregate model with Language Modeling and Latent Clustering as proposed by Yoon et al. DBLP:journals/corr/abs-1905-12897. TL(QNLI) refers to Transfer Learning from the QNLI corpus. L and B stand for Large and Base, respectively." ], "reasoning": "The table compares the performance of different pre-trained transformer models on the WikiQA dataset using MAP and MRR metrics for standard fine-tuning and TANDA methods.", "description": "Table 3: Performance of different models on WikiQA dataset. Here Comp-Agg + LM + LC refers to a CompareAggregate model with Language Modeling and Latent Clustering as proposed by Yoon et al. (2019). TL(QNLI) refers to Transfer Learning from the QNLI corpus. L and B stand for Large and Base, respectively.. Table 3 reports the MAP and MRR of different pre-trained transformers models for two methods: standard fine-tuning (FT) and TANDA. The latter takes two arguments that we indicate as transfer dataset → adapt dataset." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Performance of different models on WikiQA dataset. Here Comp-Agg + LM + LC refers to a Compare-Aggregate model with Language Modeling and Latent Clustering as proposed by Yoon et al. DBLP:journals/corr/abs-1905-12897. TL(QNLI) refers to Transfer Learning from the QNLI corpus. L and B stand for Large and Base, respectively.'\nProblem: 'Which model and method combination achieved the highest MRR on the WikiQA dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/285.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of different models on WikiQA dataset. Here Comp-Agg + LM + LC refers to a Compare-Aggregate model with Language Modeling and Latent Clustering as proposed by Yoon et al. DBLP:journals/corr/abs-1905-12897. TL(QNLI) refers to Transfer Learning from the QNLI corpus. L and B stand for Large and Base, respectively." ], "reasoning": "To find the highest MRR, look at the MRR column and identify the highest value. The highest MRR is 0.933, achieved by the RoBERTa-L model using the TANDA method (ASNQ → WikiQA).", "question": "Which model and method combination achieved the highest MRR on the WikiQA dataset?", "answer": { "answer": "RoBERTa-L TANDA (ASNQ → WikiQA)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BERT-B model with TANDA (ASNQ → WikiQA) achieved an MRR of 0.903.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/285.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of different models on WikiQA dataset. Here Comp-Agg + LM + LC refers to a Compare-Aggregate model with Language Modeling and Latent Clustering as proposed by Yoon et al. DBLP:journals/corr/abs-1905-12897. TL(QNLI) refers to Transfer Learning from the QNLI corpus. L and B stand for Large and Base, respectively." ], "reasoning": "Check the MRR value for the BERT-B model with TANDA (ASNQ → WikiQA) in the table. The MRR value is 0.903, which matches the statement.", "statement": "The BERT-B model with TANDA (ASNQ → WikiQA) achieved an MRR of 0.903.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Performance of different models on TREC-QA dataset. Here Comp-Agg + LM + LC refers to a Compare-Aggregate model with Language Modeling and Latent Clustering as proposed in [25]. TL(QNLI) refers to Transfer Learning from the QNLI corpus. L and B stand for Large and Base, respectively.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/286.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of different models on TREC-QA dataset. Here Comp-Agg + LM + LC refers to a Compare-Aggregate model with Language Modeling and Latent Clustering as proposed in [25]. TL(QNLI) refers to Transfer Learning from the QNLI corpus. L and B stand for Large and Base, respectively." ], "reasoning": "The table compares the performance of various models on the TREC-QA dataset, highlighting the highest MAP and MRR scores achieved by RoBERTa-L TandA (ASQN → TREC-QA).", "description": "Table 4: Performance of different models on TREC-QA dataset. Here Comp-Agg + LM + LC refers to a CompareAggregate model with Language Modeling and Latent Clustering as proposed in (Yoon et al. 2019). TL(QNLI) refers to Transfer Learning from the QNLI corpus. L and B stand for Large and Base, respectively.. Table 4 reports the results of our experiments with TREC-QA." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 4: Performance of different models on TREC-QA dataset. Here Comp-Agg + LM + LC refers to a Compare-Aggregate model with Language Modeling and Latent Clustering as proposed in [25]. TL(QNLI) refers to Transfer Learning from the QNLI corpus. L and B stand for Large and Base, respectively.'\nProblem: 'Which model achieved the highest MRR score on the TREC-QA dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/286.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of different models on TREC-QA dataset. Here Comp-Agg + LM + LC refers to a Compare-Aggregate model with Language Modeling and Latent Clustering as proposed in [25]. TL(QNLI) refers to Transfer Learning from the QNLI corpus. L and B stand for Large and Base, respectively." ], "reasoning": "By examining the MRR column, the highest score is 0.974, achieved by the RoBERTa-L TandA (ASQN → TREC-QA) model.", "question": "Which model achieved the highest MRR score on the TREC-QA dataset?", "answer": { "answer": "RoBERTa-L TandA (ASQN → TREC-QA)" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe BERT-B TandA (ASQN → TREC-QA) model achieved the highest MAP score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/286.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of different models on TREC-QA dataset. Here Comp-Agg + LM + LC refers to a Compare-Aggregate model with Language Modeling and Latent Clustering as proposed in [25]. TL(QNLI) refers to Transfer Learning from the QNLI corpus. L and B stand for Large and Base, respectively." ], "reasoning": "The highest MAP score in the table is 0.943, which is achieved by the RoBERTa-L TandA (ASQN → TREC-QA) model, not the BERT-B TandA (ASQN → TREC-QA) model.", "statement": "The BERT-B TandA (ASQN → TREC-QA) model achieved the highest MAP score.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Model accuracy when noise is injected into WikiQA and TREC-QA datasets. ∗ indicates the target dataset for the second step of fine-tuning (adapt step).'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/287.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Model accuracy when noise is injected into WikiQA and TREC-QA datasets. ∗ indicates the target dataset for the second step of fine-tuning (adapt step)." ], "reasoning": "The table shows the MAP and MRR of BERTBase using FT and TANDA, indicating the drop percentage in accuracy due to noise injection in WikiQA and TREC-QA datasets.", "description": "Table 5: Model accuracy when noise is injected into WikiQA and TREC-QA datasets. ∗ indicates the target dataset for the second step of fine-tuning (adapt step).. Table 5 shows the MAP and MRR of BERTBase using FT and TANDA, also indicating the drop percentage (% ) in accuracy due to the injection of noise." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Model accuracy when noise is injected into WikiQA and TREC-QA datasets. ∗ indicates the target dataset for the second step of fine-tuning (adapt step).'\nProblem: 'What is the MAP for WikiQA with 20% noise using TANDA?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/287.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Model accuracy when noise is injected into WikiQA and TREC-QA datasets. ∗ indicates the target dataset for the second step of fine-tuning (adapt step)." ], "reasoning": "To find the MAP for WikiQA with 20% noise using TANDA, locate the row labeled '20% noise TandA (ASNQ → *)' and the column labeled 'WikiQA MAP'. The value is 0.870.", "question": "What is the MAP for WikiQA with 20% noise using TANDA?", "answer": "So, the answer is {\"answer\": \"0.870\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe accuracy drop for TREC-QA MRR with 20% noise using fine-tuning is higher than 10%.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/287.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Model accuracy when noise is injected into WikiQA and TREC-QA datasets. ∗ indicates the target dataset for the second step of fine-tuning (adapt step)." ], "reasoning": "To verify this statement, locate the row labeled '20% noise Fine-tuning' and the column labeled 'TREC-QA % Drop'. The value is 10.03%, which is higher than 10%.", "statement": "The accuracy drop for TREC-QA MRR with 20% noise using fine-tuning is higher than 10%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Impact of different labels of ASNQ on fine-tuning BERT for answer sentence selection. Neg and Pos refers to question-answer (QA) pairs of that particular label being chosen for fine-tuning.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/288.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Impact of different labels of ASNQ on fine-tuning BERT for answer sentence selection. Neg and Pos refers to question-answer (QA) pairs of that particular label being chosen for fine-tuning." ], "reasoning": "Fine-tuning BERTBase on ASNQ with different negative labels shows that Label 3 is the most effective for answer sentence selection, as indicated by the highest scores in WikiQA and TREC-QA metrics.", "description": "Table 6: Impact of different labels of ASNQ on fine-tuning BERT for answer sentence selection. Neg and Pos refers to question-answer (QA) pairs of that particular label being chosen for fine-tuning.. We carried out experiments by fine-tuning BERTBase on ASNQ with specific label categories assigned to the negative class. Table 6 shows the results: Label 3 is the most effective negative type of the three," }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 6: Impact of different labels of ASNQ on fine-tuning BERT for answer sentence selection. Neg and Pos refers to question-answer (QA) pairs of that particular label being chosen for fine-tuning.'\nProblem: 'Which negative label category resulted in the highest TREC-QA MRR score?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/288.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Impact of different labels of ASNQ on fine-tuning BERT for answer sentence selection. Neg and Pos refers to question-answer (QA) pairs of that particular label being chosen for fine-tuning." ], "reasoning": "By examining the table, the highest TREC-QA MRR score is 0.872, which corresponds to the model fine-tuned with Neg: 1,2,3 Pos: 4.", "question": "Which negative label category resulted in the highest TREC-QA MRR score?", "answer": "So, the answer is {\"answer\": \"Neg: 1,2,3 Pos: 4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nLabel 3 as the negative type resulted in the highest WikiQA MAP score.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/288.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Impact of different labels of ASNQ on fine-tuning BERT for answer sentence selection. Neg and Pos refers to question-answer (QA) pairs of that particular label being chosen for fine-tuning." ], "reasoning": "The table shows that the highest WikiQA MAP score is 0.884, which corresponds to the model fine-tuned with Neg: 1,2,3 Pos: 4, not just Label 3 alone.", "statement": "Label 3 as the negative type resulted in the highest WikiQA MAP score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Comparison of TandA with ASNQ and QNLI'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/289.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Comparison of TandA with ASNQ and QNLI" ], "reasoning": "Both FT and TANDA using ASNQ outperform QNLI on the WikiQA dataset.", "description": "Table 7 shows that both FT and TANDA using ASNQ provide significantly better performance than QNLI on the WikiQA dataset." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 7: Comparison of TandA with ASNQ and QNLI'\nProblem: 'Which method provides the highest performance on the WikiQA dataset?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/289.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Comparison of TandA with ASNQ and QNLI" ], "reasoning": "By comparing the WikiQA MAP and MRR values for all methods, TANDA (ASNQ) has the highest values of 0.893 and 0.903 respectively.", "question": "Which method provides the highest performance on the WikiQA dataset?", "answer": "So, the answer is {\"answer\": \"TANDA (ASNQ)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nFT ASNQ provides better performance than FT QNLI on the WikiQA dataset.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/289.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Comparison of TandA with ASNQ and QNLI" ], "reasoning": "The table shows that FT ASNQ has higher WikiQA MAP (0.884) and MRR (0.898) values compared to FT QNLI (0.760 and 0.766 respectively).", "statement": "FT ASNQ provides better performance than FT QNLI on the WikiQA dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: Comparison between FT and TandA on real-world datasets derived from Alexa Virtual Assistant traffic'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/290.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Comparison between FT and TandA on real-world datasets derived from Alexa Virtual Assistant traffic" ], "reasoning": "The table compares the performance of FT and TANDA on NAD dataset using samples 1, 2, and 3, with ASNQ used for the transfer step.", "description": "Table 8: Comparison between FT and TANDA on real-world datasets derived from Alexa Virtual Assistant traffic. In these experiments, we used, as usual, ASNQ for the transfer step, and NAD as our target dataset for the adapt step. Table 8 reports the comparative results using simple FT on NAD (denoted simply by NAD) and tested on samples 1, 2 and 3." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 8: Comparison between FT and TandA on real-world datasets derived from Alexa Virtual Assistant traffic'\nProblem: 'Which model and configuration achieved the highest Sample 3 MRR?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/290.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Comparison between FT and TandA on real-world datasets derived from Alexa Virtual Assistant traffic" ], "reasoning": "To find the highest Sample 3 MRR, we need to look at the Sample 3 MRR column and identify the highest value. The highest Sample 3 MRR is 0.699, achieved by RoBERTa Large with TANDA (ASNQ → NAD).", "question": "Which model and configuration achieved the highest Sample 3 MRR?", "answer": "So, the answer is {\"answer\": \"RoBERTa Large with TANDA (ASNQ → NAD)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBERT Base with TANDA (ASNQ → NAD) achieved a higher Sample 1 MAP than RoBERTa Base with TANDA (ASNQ → NAD).\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/290.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Comparison between FT and TandA on real-world datasets derived from Alexa Virtual Assistant traffic" ], "reasoning": "To verify this statement, we compare the Sample 1 MAP values for BERT Base with TANDA (ASNQ → NAD) and RoBERTa Base with TANDA (ASNQ → NAD). BERT Base with TANDA (ASNQ → NAD) has a Sample 1 MAP of 0.585, while RoBERTa Base with TANDA (ASNQ → NAD) has a Sample 1 MAP of 0.623. Therefore, the statement is refuted.", "statement": "BERT Base with TANDA (ASNQ → NAD) achieved a higher Sample 1 MAP than RoBERTa Base with TANDA (ASNQ → NAD).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Top ten and bottom ten features used in successful IC classifications using vocabulary features. Differentiation and integration terms are prefixed with dif and int, while has_dif and has_int are the binary features for whether any differentiation/integration terms are present at all. The bias term is the averaged sum of the value associated with each root node in the ensemble.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/291.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Top ten and bottom ten features used in successful IC classifications using vocabulary features. Differentiation and integration terms are prefixed with dif and int, while has_dif and has_int are the binary features for whether any differentiation/integration terms are present at all. The bias term is the averaged sum of the value associated with each root node in the ensemble." ], "reasoning": "The Vocabulary model correctly classified test items in IC bands 1, 2, and 3.", "description": "The Vocabulary model correctly classified test items in three IC bands only (Table 5)." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 5: Top ten and bottom ten features used in successful IC classifications using vocabulary features. Differentiation and integration terms are prefixed with dif and int, while has_dif and has_int are the binary features for whether any differentiation/integration terms are present at all. The bias term is the averaged sum of the value associated with each root node in the ensemble.'\nProblem: 'Which feature had the highest contribution in IC = 2?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/291.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Top ten and bottom ten features used in successful IC classifications using vocabulary features. Differentiation and integration terms are prefixed with dif and int, while has_dif and has_int are the binary features for whether any differentiation/integration terms are present at all. The bias term is the averaged sum of the value associated with each root node in the ensemble." ], "reasoning": "To find the feature with the highest contribution in IC = 2, look at the 'IC = 2 Contribution' column and identify the highest value.", "question": "Which feature had the highest contribution in IC = 2?", "answer": "So, the answer is {\"answer\": \"+1.155\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe feature 'dif_too' had the highest contribution in IC = 2.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/291.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Top ten and bottom ten features used in successful IC classifications using vocabulary features. Differentiation and integration terms are prefixed with dif and int, while has_dif and has_int are the binary features for whether any differentiation/integration terms are present at all. The bias term is the averaged sum of the value associated with each root node in the ensemble." ], "reasoning": "The table shows that 'dif_too' has the highest contribution in IC = 2 with a value of +1.155.", "statement": "The feature 'dif_too' had the highest contribution in IC = 2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Figure 1, Listing 1 & Table 1: (Right code snippet) Implementation of Kiperwasser and Goldberg (2016)’s neural parser in only a few lines using UniParse. (Right table and left figure) Seconds a decoder takes to decode an entire dataset, given a set of scores. Score matrix entries are generated uniformly on [0, 1]. The random generated data has an impact on CLE since worst-case performance depends on the sorting bottleneck; the figure demonstrates this by the increasingly broad standard deviation band. Experiments are run on an Ubuntu machine with an Intel Xeon E5-2660, 2.60GHz CPU.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/292.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 1, Listing 1 & Table 1: (Right code snippet) Implementation of Kiperwasser and Goldberg (2016)’s neural parser in only a few lines using UniParse. (Right table and left figure) Seconds a decoder takes to decode an entire dataset, given a set of scores. Score matrix entries are generated uniformly on [0, 1]. The random generated data has an impact on CLE since worst-case performance depends on the sorting bottleneck; the figure demonstrates this by the increasingly broad standard deviation band. Experiments are run on an Ubuntu machine with an Intel Xeon E5-2660, 2.60GHz CPU." ], "reasoning": "The table compares the decoding speed of different algorithms, showing that the Cython implementations are significantly faster.", "description": "Seconds a decoder takes to decode an entire dataset, given a set of scores. Score matrix entries are. We compare Cython implementations in Table 1 and Figure 1 over randomised score input. Note that our implementations are significantly faster." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Figure 1, Listing 1 & Table 1: (Right code snippet) Implementation of Kiperwasser and Goldberg (2016)’s neural parser in only a few lines using UniParse. (Right table and left figure) Seconds a decoder takes to decode an entire dataset, given a set of scores. Score matrix entries are generated uniformly on [0, 1]. The random generated data has an impact on CLE since worst-case performance depends on the sorting bottleneck; the figure demonstrates this by the increasingly broad standard deviation band. Experiments are run on an Ubuntu machine with an Intel Xeon E5-2660, 2.60GHz CPU.'\nProblem: 'Which algorithm and implementation combination has the highest decoding speed in sentences per second?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/292.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 1, Listing 1 & Table 1: (Right code snippet) Implementation of Kiperwasser and Goldberg (2016)’s neural parser in only a few lines using UniParse. (Right table and left figure) Seconds a decoder takes to decode an entire dataset, given a set of scores. Score matrix entries are generated uniformly on [0, 1]. The random generated data has an impact on CLE since worst-case performance depends on the sorting bottleneck; the figure demonstrates this by the increasingly broad standard deviation band. Experiments are run on an Ubuntu machine with an Intel Xeon E5-2660, 2.60GHz CPU." ], "reasoning": "By comparing the 'sents/s' column, the highest value is 6009, which corresponds to the Eisner (ours) implementation.", "question": "Which algorithm and implementation combination has the highest decoding speed in sentences per second?", "answer": "So, the answer is {\"answer\": \"Eisner (ours)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe generic implementation of CLE is faster than the generic implementation of Eisner.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/292.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 1, Listing 1 & Table 1: (Right code snippet) Implementation of Kiperwasser and Goldberg (2016)’s neural parser in only a few lines using UniParse. (Right table and left figure) Seconds a decoder takes to decode an entire dataset, given a set of scores. Score matrix entries are generated uniformly on [0, 1]. The random generated data has an impact on CLE since worst-case performance depends on the sorting bottleneck; the figure demonstrates this by the increasingly broad standard deviation band. Experiments are run on an Ubuntu machine with an Intel Xeon E5-2660, 2.60GHz CPU." ], "reasoning": "By comparing the 'sents/s' column for the generic implementations, CLE (generic) has 404 sents/s, while Eisner (generic) has 80 sents/s, indicating that CLE (generic) is faster.", "statement": "The generic implementation of CLE is faster than the generic implementation of Eisner.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results of form clustering, measured in % of 1−vmeasure (expressing the error, i.e. lower is better). Baseline (either full form or prefix of form of length 5), our system, and oracle upper bound. Last column is error reduction on the scale from baseline to upper bound, in %.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/293.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of form clustering, measured in % of 1−vmeasure (expressing the error, i.e. lower is better). Baseline (either full form or prefix of form of length 5), our system, and oracle upper bound. Last column is error reduction on the scale from baseline to upper bound, in %." ], "reasoning": "Our method outperforms the baseline on 23 of the 28 datasets, with a median error reduction of 23%, especially excelling in Slavic languages.", "description": "Our experiments on 23 languages show our approach to be promising, surpassing the baseline on 23 of the 28 evaluation datasets.. We evaluate our setup on 28 datasets for 23 languages, finding that it outperforms the baseline on 23 of the datasets,. This results in a set of 28 treebanks for 23 languages. listed in Table 2.. The results are listed in Table 2.. the oracle does not reach 100%;. We also express the performance of our method as error reduction on the scale from baseline (0%) to upper bound (100%).. For 23 of the 28 datasets, our method achieves a positive error reduction; the median error reduction is 23%. Because of the extreme result for Korean, the average does not make much sense here. The results are worst for Korean and Japanese,. the \"form\" baseline very close to the upper bound. our results are very low here. We also observe deteriorations for a treebank of Italian Tweets and for treebanks of historical Latin,. On all other datasets, we observe an improvement over the baseline, with an error reduction typically between 10% and 35%. The performance is especially good for Slavic languages (cs, hr, pl, sk, uk), where the error reduction is often around 50%.. The evaluation showed the approach to be promising, surpassing the baseline on most of the evaluation datasets." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Results of form clustering, measured in % of 1−vmeasure (expressing the error, i.e. lower is better). Baseline (either full form or prefix of form of length 5), our system, and oracle upper bound. Last column is error reduction on the scale from baseline to upper bound, in %.'\nProblem: 'Which treebank had the highest error reduction and what was the percentage?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/293.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of form clustering, measured in % of 1−vmeasure (expressing the error, i.e. lower is better). Baseline (either full form or prefix of form of length 5), our system, and oracle upper bound. Last column is error reduction on the scale from baseline to upper bound, in %." ], "reasoning": "To find the treebank with the highest error reduction, look at the 'Err. red.' column and identify the highest value. The highest error reduction is 54.0% for the cs_cac treebank.", "question": "Which treebank had the highest error reduction?", "answer": { "answer": "cs_cac" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe error reduction for the Korean treebank ko_kaist is the lowest among all datasets.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/293.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of form clustering, measured in % of 1−vmeasure (expressing the error, i.e. lower is better). Baseline (either full form or prefix of form of length 5), our system, and oracle upper bound. Last column is error reduction on the scale from baseline to upper bound, in %." ], "reasoning": "The table shows that the error reduction for the ko_kaist treebank is -6392.8%, which is the lowest value in the 'Err. red.' column.", "statement": "The error reduction for the Korean treebank ko_kaist is the lowest among all datasets.", "answer": { "answer": "supports" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison of the word form similarities, in % of 1−vmeasure of the clustering. Average and median over the 28 datasets.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/294.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of the word form similarities, in % of 1−vmeasure of the clustering. Average and median over the 28 datasets." ], "reasoning": "Combining edit distance with embedding similarity yields better results than using either measure alone, with embedding similarity alone outperforming edit distance alone.", "description": "In Table 3, we compare the combined distance measure with each of the two components used alone. The results show that combining the edit distance with the embedding similarity is stronger than using any of the measures alone.. The embedding similarity alone performs much better than the edit distance alone." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Comparison of the word form similarities, in % of 1−vmeasure of the clustering. Average and median over the 28 datasets.'\nProblem: 'What is the median value when combining JW and cos measures?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/294.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of the word form similarities, in % of 1−vmeasure of the clustering. Average and median over the 28 datasets." ], "reasoning": "The table shows the median values for JW, cos, and their combination. The median value for the combination (JW·cos) is 3.40.", "question": "What is the median value when combining JW and cos measures?", "answer": "So, the answer is {\"answer\": \"3.40\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe average value of the cos measure is higher than the median value of the JW measure.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/294.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of the word form similarities, in % of 1−vmeasure of the clustering. Average and median over the 28 datasets." ], "reasoning": "The table shows that the average value of the cos measure is 4.39, while the median value of the JW measure is 7.92. Therefore, the statement is refuted.", "statement": "The average value of the cos measure is higher than the median value of the JW measure.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Performance comparison of different models with pre-trained embedding'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/295.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Performance comparison of different models with pre-trained embedding" ], "reasoning": "The performance of character-level models is competitive with word-level models, especially with larger datasets, and fastText's Skip-gram implementation performs best across various author counts.", "description": "Various subsets of authors were chosen and the dataset was truncated to each author having the same number of samples.. The classification was carried out with 2 author attribution datasets, one with 6 authors and our dataset with maximum 14 authors. The larger dataset was trained with 6,8,10,12 and 14 authors to analyze the effects of increasing classes on the proposed model.. We evaluate the performance of the proposed architecture in terms of accuracy,. We also try to infer how the character-level model compares with the word level models. All models are compared for the increasing number of authors(classes) on the corpus mentioned to assess the quality of the models.. both word and character levels are summarized in Table II.. From the accuracy comparisons shown in Table II we see that Skip-gram implemented by fastText performs well in the given datasets.. Character level model performs reasonably well in competition with subword level as long as the dataset is big enough.. With larger datasets, this model will be able to perform significantly better compare character embeddings with word embeddings showing that character embeddings perform almost as good as the best word embedding model." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'TABLE II: Performance comparison of different models with pre-trained embedding'\nProblem: 'Which model achieved the highest accuracy with 10 authors?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/295.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Performance comparison of different models with pre-trained embedding" ], "reasoning": "From the table, we see that with 10 authors, the highest accuracy is achieved by fastText(Skip) with an accuracy of 95.2.", "question": "Which model achieved the highest accuracy with 10 authors?", "answer": "So, the answer is {\"answer\": \"fastText(Skip)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe Char-CNN model achieved the highest accuracy with 6 authors.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/295.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Performance comparison of different models with pre-trained embedding" ], "reasoning": "From the table, we see that with 6 authors, the highest accuracy is achieved by fastText(Skip) with an accuracy of 86, not Char-CNN.", "statement": "The Char-CNN model achieved the highest accuracy with 6 authors.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison of the Label accuracy on Development set.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/296.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of the Label accuracy on Development set." ], "reasoning": "Our unsupervised model achieves a label accuracy of 80.20 for φ = 0.76, which is comparable to the supervised HexaF model's accuracy of 80.18.", "description": "Although our unsupervised model doesn't support all the labels, to show the effectiveness of the approach, we compare the label accuracy of \"SUPPORTS\" label against a supervised approach – HexaF.. Results from Table 3 suggests that our approach is comparable to HexaF4 for φ = 0.76." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 3: Comparison of the Label accuracy on Development set.'\nProblem: 'What is the label accuracy of our model for φ = 0.67?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/296.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of the Label accuracy on Development set." ], "reasoning": "The table shows the label accuracy of our model (BERT) for φ = 0.67 as 86.7.", "question": "What is the label accuracy of our model for φ = 0.67?", "answer": "So, the answer is {\"answer\": \"86.7\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nHexaF-UCL has a higher label accuracy than our model for φ = 0.67.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/296.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of the Label accuracy on Development set." ], "reasoning": "The table shows that HexaF-UCL has a label accuracy of 80.18 for φ = 0.67, while our model has a label accuracy of 86.7 for the same φ value. Therefore, our model has a higher accuracy.", "statement": "HexaF-UCL has a higher label accuracy than our model for φ = 0.67.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Performance of the question generation system on FEVER Dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/297.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of the question generation system on FEVER Dataset." ], "reasoning": "The table shows the conversion accuracy of claims to questions across different FEVER sets, with the training set having the highest accuracy.", "description": "For the subtask of question generation, the results in Table 1 show that the system is able to generate questions given a claim with considerably good accuracy. The conversion accuracy is defined as the ratio of the number of claims in which the named entities are extracted to the number of claims. The results also support our assumption that the claims generally feature information about one or more entities.. Ta" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Performance of the question generation system on FEVER Dataset.'\nProblem: 'Which FEVER set has the highest number of total questions and what is its conversion accuracy?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/297.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of the question generation system on FEVER Dataset." ], "reasoning": "The training set has the highest number of total questions at 395717 and a conversion accuracy of 90.73.", "question": "Which FEVER set has the highest number of total questions and what is its conversion accuracy?", "answer": { "answer": "Training Set" } }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe test set has a conversion accuracy higher than the development set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/297.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of the question generation system on FEVER Dataset." ], "reasoning": "The table shows that the test set has a conversion accuracy of 88.63, which is lower than the development set's accuracy of 88.75.", "statement": "The test set has a conversion accuracy higher than the development set.", "answer": { "answer": "refutes" } }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Performance of the question generation system on FEVER Dataset.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/298.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of the question generation system on FEVER Dataset." ], "reasoning": "The performance of the Fact Checking system is better with a classification threshold of φ = 0.67 across all sets, but φ = 0.76 reduces false positives.", "description": "Table 2 shows the performance of our Fact Checking system on the \"SUPPORTS\" label, the output of our system. We compare the results against two different classification thresholds.. Here, φ = 0.76 suggests that at least 3 out of the 4 questions have to be answered correctly while φ = 0.67 suggests that at least 2 out of the 3 questions has to be answered correctly for the claim to be classified as \"SUPPORTS\".. In contrast to the results reported in Table 2, here we consider φ = 0.76 to be a better classification threshold as it improvises over False Positives considerably over the entire dataset.. From the results, we conclude that it is possible to verify the facts with the right kind of factoid questions." }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 2: Performance of the question generation system on FEVER Dataset.'\nProblem: 'What is the label accuracy for the Development Set when φ = 0.76?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/298.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of the question generation system on FEVER Dataset." ], "reasoning": "To find the label accuracy for the Development Set when φ = 0.76, look at the column labeled 'Label Accuracy (φ = 0.76)' and find the corresponding row for the Development Set.", "question": "What is the label accuracy for the Development Set when φ = 0.76?", "answer": "So, the answer is {\"answer\": \"80.20\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nThe label accuracy for the Training Set is higher when φ = 0.67 compared to φ = 0.76.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/298.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of the question generation system on FEVER Dataset." ], "reasoning": "Compare the label accuracy values for the Training Set under both thresholds. The value for φ = 0.67 is 88.05, which is higher than 81.52 for φ = 0.76.", "statement": "The label accuracy for the Training Set is higher when φ = 0.67 compared to φ = 0.76.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Evaluation of trained models on all test sets. Gap and WinoBias (abbreviated WB) are additionally split into subsets, as introduced in Section 5. Double lines in the table separate results from three different scenarios: when no training data is available, when additional training data exists, and the transductive scenario. The table is further split into sections separated with single horizontal lines. Each section contains a model that has been trained on WikiCREM and models that have not been. The best result in each section is in bold. The best overall result is underlined. Scores on Gap are measured as F1-scores, while the performance on other datasets is given in accuracy. The source of each SOTA is listed in Section 5.'. Formulate a statement describing the table. \n " }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/299.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation of trained models on all test sets. Gap and WinoBias (abbreviated WB) are additionally split into subsets, as introduced in Section 5. Double lines in the table separate results from three different scenarios: when no training data is available, when additional training data exists, and the transductive scenario. The table is further split into sections separated with single horizontal lines. Each section contains a model that has been trained on WikiCREM and models that have not been. The best result in each section is in bold. The best overall result is underlined. Scores on Gap are measured as F1-scores, while the performance on other datasets is given in accuracy. The source of each SOTA is listed in Section 5." ], "reasoning": "BERT WIKICREM consistently outperforms both baselines on all tests, showing that WIKICREM can be used as a standalone dataset. The best results are achieved when all available data is combined, as shown by BERT WIKICREM ALL.", "description": "The results of the evaluation of the models on the test sets are shown in Table 1. We notice that additional training on WIKICREM consistently improves the performance of the models in all scenarios and on most tests. Due to the small size of some test sets, some of the results are subject to deviation. This especially applies to PDP (60 test samples) and WNLI (145 test samples). We observe that BERT WIKIRAND generally performs worse than BERT, with GAP and PDP being notable exceptions. This shows that BERT is a strong baseline and that improved performance of BERT WIKICREM is not a consequence of training on shorter sentences or with different loss function. BERT WIKICREM consistently outperforms both baselines on all tests, showing that WIKICREM can be used as a standalone dataset. We observe that training on the data from the target distribution improves the performance the most. Models trained on GAP-train usually show more than a 20% increase in their F1-score on GAP-test. Still, BERT WIKICREM GAP shows. a consistent improvement over BERT GAP on all subsets of the GAP test set. This confirms that WIKICREM works not just as a standalone dataset, but also as an additional pre-training in the transductive scenario. Similarly, BERT WIKICREM DPR outpertasks, forms BERT DPR on the majority of showing the applicability of WIKICREM to the scenario where additional training data is available. However, good results of BERT GAP DPR show that additional training on a manually constructed dataset, such as GAP, can yield similar results as additional training on WIKICREM. The reason behind this difference is the impact of the data distribution. GAP, DPR, and WIKICREM contain data that follows different distributions which strongly impacts the trained models. This. can be seen when we fine-tune BERT GAP on DPR to obtain BERT GAP DPR, as the model's performance on GAP-test drops by 8.2%. WIKICREM's data distribution strongly differs from the test sets' as described in Section 3. the achieves However, the best results are achieved when all available data is combined, as shown by the models BERT ALL and BERT WIKICREM ALL. BERT WIKICREM ALL highest performance on GAP, DPR, WNLI, and WINOBIAS among the models, and sets the new state-of-the-art result on GAP, DPR, and WINOBIAS. result on the WINOGENDER dataset is achieved by the BERT WIKICREM DPR model, while BERT WIKICREM ALL and BERT GAP DPR set the new state-of-the-art on the PDP dataset. The new state-of-the-art" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {'answer': ''}.\nTable title: 'Table 1: Evaluation of trained models on all test sets. Gap and WinoBias (abbreviated WB) are additionally split into subsets, as introduced in Section 5. Double lines in the table separate results from three different scenarios: when no training data is available, when additional training data exists, and the transductive scenario. The table is further split into sections separated with single horizontal lines. Each section contains a model that has been trained on WikiCREM and models that have not been. The best result in each section is in bold. The best overall result is underlined. Scores on Gap are measured as F1-scores, while the performance on other datasets is given in accuracy. The source of each SOTA is listed in Section 5.'\nProblem: 'Which model achieves the highest performance on the WNLI test set?'\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/299.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation of trained models on all test sets. Gap and WinoBias (abbreviated WB) are additionally split into subsets, as introduced in Section 5. Double lines in the table separate results from three different scenarios: when no training data is available, when additional training data exists, and the transductive scenario. The table is further split into sections separated with single horizontal lines. Each section contains a model that has been trained on WikiCREM and models that have not been. The best result in each section is in bold. The best overall result is underlined. Scores on Gap are measured as F1-scores, while the performance on other datasets is given in accuracy. The source of each SOTA is listed in Section 5." ], "reasoning": "To find the model with the highest performance on the WNLI test set, we look at the WNLI column in the table. The highest value in this column is 74.7%, achieved by both BERT WIKICREM DPR and BERT WIKICREM ALL.", "question": "Which model achieves the highest performance on the WNLI test set?", "answer": "So, the answer is {\"answer\": \"BERT_WikiCREM_Dpr\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {'answer': ''}.\nBERT WIKICREM ALL achieves the highest performance on the GAP test set.\n" }, { "role": "assistant", "content": "" } ], "images": [ "scigen/test-CL/299.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation of trained models on all test sets. Gap and WinoBias (abbreviated WB) are additionally split into subsets, as introduced in Section 5. Double lines in the table separate results from three different scenarios: when no training data is available, when additional training data exists, and the transductive scenario. The table is further split into sections separated with single horizontal lines. Each section contains a model that has been trained on WikiCREM and models that have not been. The best result in each section is in bold. The best overall result is underlined. Scores on Gap are measured as F1-scores, while the performance on other datasets is given in accuracy. The source of each SOTA is listed in Section 5." ], "reasoning": "To verify this statement, we look at the GAP columns (F1, FF1, FM1) in the table. BERT WIKICREM ALL achieves the highest performance in all three GAP metrics: 78.0% (F1), 79.4% (FF1), and 76.7% (FM1).", "statement": "BERT WIKICREM ALL achieves the highest performance on the GAP test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." } ]