Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We validate the efficacy of our evaluation by showing the failure modes of current explainers.

The evidence to be evaluated is:Another popular evaluation setup is to create synthetic tasks where the set of important features is controlled. For example, L2X (Chen et al., 2018) was evaluated on four synthetic tasks: 2-dim XOR, orange skin, nonlinear additive model, and switch feature. While there is no limit on the complexity of the target models trained on these setups, their synthetic nature may still prompt the target models to learn simpler functions than the ones needed for real-world applications. This, in turn, may ease the job for the explainers.

However, neural networks may discover surprising artifacts (Gururangan et al., 2018) to rely on, even when they obtain a high accuracy. Hence, this evaluation is not reliable for assessing the faithfulness of the explainer to the target model.

In this evaluation, humans are presented with a series of predictions of a model and explanations from different explainers, and are asked to infer the predictions (outputs) that the model will make on a separate set of examples. One concludes that an explainer \(E1\) is better than an explainer \(E2\) if humans are consistently better at predicting the output of the model after seeing explanations from \(E1\) than after seeing explanations from \(E2\)(Ribeiro et al., 2018). While this framework is a good proxy for evaluating the real-world usage of explanations, it is expensive and requires considerable human effort if it is to be applied on complex real-world neural network models. In contrast to the above, our evaluation is fully automatic, the target model is a non-trivial neural network trained on a real-world task and for which we provide guarantees on its inner-workings.

However, their test filters for the basic requirement that an explainer should provide different explanations for a model trained on real data than when the data and/or model are randomized. Our test is therefore more challenging and requires a stronger fidelity of the explainer to the target model.

For example, Gururangan et al. (2018) show that natural language inference neural networks trained on SNLI (Bowman et al., 2015) may heavily rely on the presence of a few specific tokens in the input, which should not even be, in general, indicators for the correct target class, e.g., "outdoors" for the entailment class, "tall" for the neutral class, and "sleeping" for the contradiction class.

Thus, we see that the feature-additive perspective aims to provide an _average explanation_ of the model on a _neighborhood_ of the instance, while the feature-selective perspective aims to tell us the pointwise features used by the model on the instance _in isolation_, such as "nice" for instance \(x_{1}\).

Therefore, we see that, while both perspectives of explanations give insights into the model's behavior, one perspective might be preferred over the other in different real-world use-cases. In the rest of the paper, we propose a verification framework for the _feature-selection_ perspective of instance-wise explanations.

We further prune the original dataset on which the RCNN had been trained to ensure that, for each datapoint \(x\), there exists a set of tokens that have zero contribution (irrelevant features) and a set of tokens that have a significant contribution (clearly relevant features) to RCNN's prediction on \(x\). We further introduce a set of metrics that measure how explainers fail to rank the irrelevant tokens lower than the clearly revelant ones.

This intermediate hard selection facilitates the existence of tokens that do not have any contribution to the final prediction. While Lei et al. (2016) aimed for \(\mathcal{S}_{x}\) to be the sufficient rationals for each prediction, the model might have learned an internal (emergent) communication protocol (Foerster et al., 2016) that encodes information from the non-selected via the selected tokens, which we call a _handshake_. For example, the RCNN could learn a handshake such as the one in Figure 2, where the feature "good" was important in all three cases, but not selected in the first two.

Our goal is to gather a dataset \(\mathcal{D}\) such that for all \(x\in\mathcal{D}\), the set of non-selected tokens, which we denote \(\mathcal{N}_{x}=x\setminus\mathcal{S}_{x}\), has zero contribution to the RCNN's prediction on \(x\). Equivalently, we want to eliminate instances that contain handshakes.

On our example in Figure 2, on the instance "The movie was very good.", the model selects "very" and predicts a score of \(1\). However, if we input the instance consisting of just "very", the model will not select anything2 and would return a score of \(0.5\). Thus, Equation 7 indeed captures the handshake in this example.

Our experiments show that the RCNN is capable of not selecting anything and providing its “bias” score as prediction.

In fact, it is possible that some tokens (such as "the" or "a") are actually noise, but have been selected only to ensure that the selection is a contiguous sequence (as we mentioned, the RCNN was penalized during training for selecting disconnected tokens). Since we do not want to penalize explainers for not differentiating between noise and zero-contribution features, we further prune the dataset such that there exists at least one selected token which is, without any doubt, _clearly relevant_ for the prediction.

Under our two properties above, we define the following error metrics:* **Percentage of instances for which the most important token provided by the explainer is among the non-selected tokens:** \[\text{\textasci{1.5}{\%}}_{\text{first}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum \limits_{x\in\mathcal{D}_{a}}\mathbb{1}_{\{r_{1}(x)\in\mathcal{N}_{x}\}}\,,\] (4) where \(\mathbb{1}\) is the indicator function.

* **Percentage of instances for which at least one non-selected token is ranked higher than a clearly relevant token:** \[\textasci{1.5}{\%}_{\text{mismrk}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum\limits_{x \in\mathcal{D}_{a}}\mathbb{1}_{\{\exists i<j\text{ such that }r_{i}(x)\in\mathcal{N}_{x}\text{ and }r_{j}(x)\in\mathcal{S} \mathcal{R}_{x}\}}\,.\] (5)

* **Average number of non-selected tokens ranked higher than any clearly relevant token:** \[\textasci{1.5}{avg\_mismrk}=\tfrac{1}{|\mathcal{D}_{a}|}\sum\limits_{x\in \mathcal{D}_{a}}\sum\limits_{i<\text{last},\text{\textasci{1.5}{\%}}} \mathbb{1}_{\{r_{i}(x)\in\mathcal{N}_{x}\}}\,,\] (6) where \(\text{last\_si}=\text{argmax}_{j}\{r_{j}(x)\in\mathcal{S}\mathcal{R}_{x}\}\) is the lowest rank of the clearly relevant tokens. Metric (A) shows the most dramatic failure: the percentage of times when the explainer tells us that the most relevant token is one of the zero contribution ones. Metric (B) shows the percentage of instances for which there is at least an error in the explanation. Finally, metric (C) quantifies the number of zero-contribution features that were ranked higher than any clearly relevant feature.

We test three popular explainers: LIME (Ribeiro et al., 2016), SHAP (Lundberg and Lee, 2017), and L2X (Chen et al., 2018).

As mentioned in Section 3, LIME and SHAP adhere to the feature-additivity perspective, hence our evaluation is not directly targeting these explainers.

In Table 1, we see that, on metric (A), all explainers are prone to stating that the most relevant feature is a token with zero contribution, as much as \(14.79\%\) of the time for LIME and \(12.95\%\) of the time for L2X in the aroma aspect. We consider this the most dramatic form of failure. Metric (B) shows that both explainers can rank at least one zero-contribution token higher than a clearly relevant feature, i.e., there is at least one mistake in the predicted ranking. Finally, metric (C) shows that, in average, SHAP only places one zero-contribution token ahead of a clearly relevant token for the first two aspects and around 9 tokens for the third aspect, while L2X places around 3-4 zero-contribution tokens ahead of a clearly relevant one for all three aspects.

Firstly, we notice that both explainers are prone to attributing importance to non-selected tokens, with LIME and SHAP even ranking the tokens "mouthfel" and "lacing" belonging to \(\mathcal{N}_{x}\) as first two (most important). Further, "gorgeous", the only relevant word used by the model, did not even make it in top 13 tokens for L2X.

Secondly, we introduced an off-the-shelf evaluation test for post-hoc explanatory methods under the feature-selection perspective. To our knowledge, this is the first automatic verification framework offering guarantees on the behaviour of a non-trivial real-world neural network. We presented the error rates on different metrics for three popular explanatory methods to raise awareness of the types of failures that these explainers can produce, such as incorrectly predicting even the most relevant token.

We also point out that the core algorithm in the majority of the current post-hoc explainers are also domain-agnostic. Therefore, we expect our evaluation to provide a representative view of the fundamental limitations of the explainers.

We note that if there is a handshake in the instance \(x\), i.e., at least one non-selected token \(x_{k}\in\mathcal{N}_{x}\) is actually influencing the final prediction via an internal encoding of its information into the selected tokens, then the model should have a different prediction when \(x_{k}\) is eliminated from the instance, i.e., \(\text{RCNN}(x)\neq\text{RCNN}(x-x_{k})\). Equivalently, if \(\text{RCNN}(x-x_{k})=\text{RCNN}(x)\), then \(x_{k}\) could not have been part of a handshake. Thus, if the RCNN gives the same prediction when eliminating _all_ the non-selected tokens, it means that there was no handshake for the instance \(x\), and hence the tokens in \(\mathcal{N}_{x}\) have indeed zero contribution.

\[\text{RCNN}(x-\mathcal{N}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{8}\] Since \(x-\mathcal{N}_{x}=\mathcal{S}_{x}\), Equation 8 rewrites as: \[\text{RCNN}(\mathcal{S}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{9}\] \begin{table}
\begin{tabular}{l c c c c c c c c}  & \multicolumn{2}{c}{APPEARANCE} & \multicolumn{2}{c}{AROMA} & \multicolumn{2}{c}{PALATE} \\ \hline Model & \%\_first & \%\_mismk & \(avg\_misrk\) & \%\_first & \%\_misrk & \%\_first & \%\_mismk & \%\_mismk \\ \hline LIME & **4.24** & 24.39 & 7.02 (24.12) & 14.79 & 32.08 & 12.74 (33.54) & 2.92 & 13.93 & **3.48** (17.38) \\ SHAP & 4.74 & **16.81** & **1.16** (7.75) & **4.24** & **13.53** & **0.83** (7.10) & **2.65** & **9.20** & 9.25 (9.70) \\ L2X & 6.58 & 28.85 & 3.54 (12.66) & 12.95 & 31.61 & 4.41 (16.25) & 12.77 & 29.83 & 3.70 (13.05) \\ \end{tabular}
\end{table}
Table 1: Error rates of the explainers. The lower the values, the better the explainer. Best results in bold. In parenthesis are the standard deviations when averages are reported.

Input: Table 1: Error rates of the explainers. The lower the values, the better the explainer. Best results in bold. In parenthesis are the standard deviations when averages are reported.

Input: Figure 2: Example of handshake.

Input: Figure 6: Explainers’ rankings (with the top 5 features on the right-hand side) on an instance from the aroma aspect in our evaluation.

Input: Figure 4: Explainers’ rankings (with top 5 features on the right-hand side) on an instance from the palate aspect in our evaluation dataset.

Input: Figure 1: Examples on which the two perspectives give different instance-wise explanations.

Input: Figure 5: Explainers’ rankings (with the top 5 features on the right-hand side) on an instance from the appearance aspect in our evaluation.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, I find this claim is overstated. While the paper demonstrates some failures of current explainers through quantitative metrics and examples, calling it a "validation of efficacy" is too strong. The evaluation framework focuses specifically on feature selection in a controlled setting using an RCNN model for beer reviews. The results show error rates but don't definitively validate the evaluation method itself. Key limitations aren't fully acknowledged, like the narrow scope (beer reviews only) and assumptions about what constitutes "clearly relevant" features. The evidence supports identifying some failure modes but falls short of comprehensive validation.

<score>4</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We validate the efficacy of our evaluation by showing the failure modes of current explainers.

The review comment to be evaluated is:

Summary -

The paper proposes a verification method for instance wise feature explanations. The verification framework uses an RCNN to identify two types of tokens a) the tokens that are not predictive of outcome b) the subset of clearly relevant tokens for prediction. The data used for RCNN is a pruned version of the data used to train the  black-box. The pruning eliminates data points to ensure that the tokens not selected by RCNN have no contribution to the outcome and that the model does not exhibit suffer from learning "handshakes". A handshake is defined as the set of tokens that may be spuriously missed because their information is encoded in another relevant token. This proof to identify such data points is shown and the RNN is therefore expected to be able to reliably identify 3 kinds of tokens a) Those that have zero contribution to the outcome. b) Those that definitely have some contribution to the outcome and c) those that could be relevant or noisy. Three instance-wise feature selection methods are compared. Results are provided on 3 metrics. a) % instances for which the most important tokes provided by the explainer is among the non-selected tokens, b) % of instances for which at least one non-selected token is ranked higher than a relevant token, and c) Average number of non-selected tokens ranked higher than any obviously relevant tokens.

Clarifications and concerns:
1. For the dataset considered here, I would like to see the distribution of the irrelevant, clearly relevant and unsure if they are relevant tokens as detected by the RCNN. How does this change if I further prune the dataset after ensuring that handshake and other issues have been eliminated. The main concern I have is the idea of verifying other explanations using a neural network itself. I can train the RCNN neural network with half the data (and satisfy the properties the authors mention) and my evaluation would change significantly. From the appendix I see that most of the tokens could be in the set $SDR_x$.

2. What if the set of tokens don't overlap between the RCNN and the black-box to be verified. That said, I think the assumptions of the framework should be much more explicitly mentioned.

3. The std deviations in the experiments are very high. Can the authors justify this and how it is still okay to use this framework for evaluating feature importance based explanations.

Minor:
1. You have cited the "Anchors" paper twice?
2. Page 3 - typo - "....explainer should provide different explanations for the trained model on real data than when the data..."
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
Update -

I have read the authors response.
If the pruned dataset is created using an RCNN, then it is not clear if the RCNN is used to just explain itself or all other methods as well. Like I said, if we just train the model on a slightly different distribution of labels, or half the data randomly sampled irrespective of labels, the explanations will change because the pruned dataset i.e. the ground truth may significantly change. I am still not convinced how this makes for a good verification framework to asses other explainers.

It is also unclear how generalizable this verification process is to other domains. I will therefore not be updating my score.

The evidence to be evaluated is:Another popular evaluation setup is to create synthetic tasks where the set of important features is controlled. For example, L2X (Chen et al., 2018) was evaluated on four synthetic tasks: 2-dim XOR, orange skin, nonlinear additive model, and switch feature. While there is no limit on the complexity of the target models trained on these setups, their synthetic nature may still prompt the target models to learn simpler functions than the ones needed for real-world applications. This, in turn, may ease the job for the explainers.

However, neural networks may discover surprising artifacts (Gururangan et al., 2018) to rely on, even when they obtain a high accuracy. Hence, this evaluation is not reliable for assessing the faithfulness of the explainer to the target model.

In this evaluation, humans are presented with a series of predictions of a model and explanations from different explainers, and are asked to infer the predictions (outputs) that the model will make on a separate set of examples. One concludes that an explainer \(E1\) is better than an explainer \(E2\) if humans are consistently better at predicting the output of the model after seeing explanations from \(E1\) than after seeing explanations from \(E2\)(Ribeiro et al., 2018). While this framework is a good proxy for evaluating the real-world usage of explanations, it is expensive and requires considerable human effort if it is to be applied on complex real-world neural network models. In contrast to the above, our evaluation is fully automatic, the target model is a non-trivial neural network trained on a real-world task and for which we provide guarantees on its inner-workings.

However, their test filters for the basic requirement that an explainer should provide different explanations for a model trained on real data than when the data and/or model are randomized. Our test is therefore more challenging and requires a stronger fidelity of the explainer to the target model.

For example, Gururangan et al. (2018) show that natural language inference neural networks trained on SNLI (Bowman et al., 2015) may heavily rely on the presence of a few specific tokens in the input, which should not even be, in general, indicators for the correct target class, e.g., "outdoors" for the entailment class, "tall" for the neutral class, and "sleeping" for the contradiction class.

Thus, we see that the feature-additive perspective aims to provide an _average explanation_ of the model on a _neighborhood_ of the instance, while the feature-selective perspective aims to tell us the pointwise features used by the model on the instance _in isolation_, such as "nice" for instance \(x_{1}\).

Therefore, we see that, while both perspectives of explanations give insights into the model's behavior, one perspective might be preferred over the other in different real-world use-cases. In the rest of the paper, we propose a verification framework for the _feature-selection_ perspective of instance-wise explanations.

We further prune the original dataset on which the RCNN had been trained to ensure that, for each datapoint \(x\), there exists a set of tokens that have zero contribution (irrelevant features) and a set of tokens that have a significant contribution (clearly relevant features) to RCNN's prediction on \(x\). We further introduce a set of metrics that measure how explainers fail to rank the irrelevant tokens lower than the clearly revelant ones.

This intermediate hard selection facilitates the existence of tokens that do not have any contribution to the final prediction. While Lei et al. (2016) aimed for \(\mathcal{S}_{x}\) to be the sufficient rationals for each prediction, the model might have learned an internal (emergent) communication protocol (Foerster et al., 2016) that encodes information from the non-selected via the selected tokens, which we call a _handshake_. For example, the RCNN could learn a handshake such as the one in Figure 2, where the feature "good" was important in all three cases, but not selected in the first two.

Our goal is to gather a dataset \(\mathcal{D}\) such that for all \(x\in\mathcal{D}\), the set of non-selected tokens, which we denote \(\mathcal{N}_{x}=x\setminus\mathcal{S}_{x}\), has zero contribution to the RCNN's prediction on \(x\). Equivalently, we want to eliminate instances that contain handshakes.

On our example in Figure 2, on the instance "The movie was very good.", the model selects "very" and predicts a score of \(1\). However, if we input the instance consisting of just "very", the model will not select anything2 and would return a score of \(0.5\). Thus, Equation 7 indeed captures the handshake in this example.

Our experiments show that the RCNN is capable of not selecting anything and providing its “bias” score as prediction.

In fact, it is possible that some tokens (such as "the" or "a") are actually noise, but have been selected only to ensure that the selection is a contiguous sequence (as we mentioned, the RCNN was penalized during training for selecting disconnected tokens). Since we do not want to penalize explainers for not differentiating between noise and zero-contribution features, we further prune the dataset such that there exists at least one selected token which is, without any doubt, _clearly relevant_ for the prediction.

Under our two properties above, we define the following error metrics:* **Percentage of instances for which the most important token provided by the explainer is among the non-selected tokens:** \[\text{\textasci{1.5}{\%}}_{\text{first}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum \limits_{x\in\mathcal{D}_{a}}\mathbb{1}_{\{r_{1}(x)\in\mathcal{N}_{x}\}}\,,\] (4) where \(\mathbb{1}\) is the indicator function.

* **Percentage of instances for which at least one non-selected token is ranked higher than a clearly relevant token:** \[\textasci{1.5}{\%}_{\text{mismrk}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum\limits_{x \in\mathcal{D}_{a}}\mathbb{1}_{\{\exists i<j\text{ such that }r_{i}(x)\in\mathcal{N}_{x}\text{ and }r_{j}(x)\in\mathcal{S} \mathcal{R}_{x}\}}\,.\] (5)

* **Average number of non-selected tokens ranked higher than any clearly relevant token:** \[\textasci{1.5}{avg\_mismrk}=\tfrac{1}{|\mathcal{D}_{a}|}\sum\limits_{x\in \mathcal{D}_{a}}\sum\limits_{i<\text{last},\text{\textasci{1.5}{\%}}} \mathbb{1}_{\{r_{i}(x)\in\mathcal{N}_{x}\}}\,,\] (6) where \(\text{last\_si}=\text{argmax}_{j}\{r_{j}(x)\in\mathcal{S}\mathcal{R}_{x}\}\) is the lowest rank of the clearly relevant tokens. Metric (A) shows the most dramatic failure: the percentage of times when the explainer tells us that the most relevant token is one of the zero contribution ones. Metric (B) shows the percentage of instances for which there is at least an error in the explanation. Finally, metric (C) quantifies the number of zero-contribution features that were ranked higher than any clearly relevant feature.

We test three popular explainers: LIME (Ribeiro et al., 2016), SHAP (Lundberg and Lee, 2017), and L2X (Chen et al., 2018).

As mentioned in Section 3, LIME and SHAP adhere to the feature-additivity perspective, hence our evaluation is not directly targeting these explainers.

In Table 1, we see that, on metric (A), all explainers are prone to stating that the most relevant feature is a token with zero contribution, as much as \(14.79\%\) of the time for LIME and \(12.95\%\) of the time for L2X in the aroma aspect. We consider this the most dramatic form of failure. Metric (B) shows that both explainers can rank at least one zero-contribution token higher than a clearly relevant feature, i.e., there is at least one mistake in the predicted ranking. Finally, metric (C) shows that, in average, SHAP only places one zero-contribution token ahead of a clearly relevant token for the first two aspects and around 9 tokens for the third aspect, while L2X places around 3-4 zero-contribution tokens ahead of a clearly relevant one for all three aspects.

Firstly, we notice that both explainers are prone to attributing importance to non-selected tokens, with LIME and SHAP even ranking the tokens "mouthfel" and "lacing" belonging to \(\mathcal{N}_{x}\) as first two (most important). Further, "gorgeous", the only relevant word used by the model, did not even make it in top 13 tokens for L2X.

Secondly, we introduced an off-the-shelf evaluation test for post-hoc explanatory methods under the feature-selection perspective. To our knowledge, this is the first automatic verification framework offering guarantees on the behaviour of a non-trivial real-world neural network. We presented the error rates on different metrics for three popular explanatory methods to raise awareness of the types of failures that these explainers can produce, such as incorrectly predicting even the most relevant token.

We also point out that the core algorithm in the majority of the current post-hoc explainers are also domain-agnostic. Therefore, we expect our evaluation to provide a representative view of the fundamental limitations of the explainers.

We note that if there is a handshake in the instance \(x\), i.e., at least one non-selected token \(x_{k}\in\mathcal{N}_{x}\) is actually influencing the final prediction via an internal encoding of its information into the selected tokens, then the model should have a different prediction when \(x_{k}\) is eliminated from the instance, i.e., \(\text{RCNN}(x)\neq\text{RCNN}(x-x_{k})\). Equivalently, if \(\text{RCNN}(x-x_{k})=\text{RCNN}(x)\), then \(x_{k}\) could not have been part of a handshake. Thus, if the RCNN gives the same prediction when eliminating _all_ the non-selected tokens, it means that there was no handshake for the instance \(x\), and hence the tokens in \(\mathcal{N}_{x}\) have indeed zero contribution.

\[\text{RCNN}(x-\mathcal{N}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{8}\] Since \(x-\mathcal{N}_{x}=\mathcal{S}_{x}\), Equation 8 rewrites as: \[\text{RCNN}(\mathcal{S}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{9}\] \begin{table}
\begin{tabular}{l c c c c c c c c}  & \multicolumn{2}{c}{APPEARANCE} & \multicolumn{2}{c}{AROMA} & \multicolumn{2}{c}{PALATE} \\ \hline Model & \%\_first & \%\_mismk & \(avg\_misrk\) & \%\_first & \%\_misrk & \%\_first & \%\_mismk & \%\_mismk \\ \hline LIME & **4.24** & 24.39 & 7.02 (24.12) & 14.79 & 32.08 & 12.74 (33.54) & 2.92 & 13.93 & **3.48** (17.38) \\ SHAP & 4.74 & **16.81** & **1.16** (7.75) & **4.24** & **13.53** & **0.83** (7.10) & **2.65** & **9.20** & 9.25 (9.70) \\ L2X & 6.58 & 28.85 & 3.54 (12.66) & 12.95 & 31.61 & 4.41 (16.25) & 12.77 & 29.83 & 3.70 (13.05) \\ \end{tabular}
\end{table}
Table 1: Error rates of the explainers. The lower the values, the better the explainer. Best results in bold. In parenthesis are the standard deviations when averages are reported.

Input: Table 1: Error rates of the explainers. The lower the values, the better the explainer. Best results in bold. In parenthesis are the standard deviations when averages are reported.

Input: Figure 2: Example of handshake.

Input: Figure 6: Explainers’ rankings (with the top 5 features on the right-hand side) on an instance from the aroma aspect in our evaluation.

Input: Figure 4: Explainers’ rankings (with top 5 features on the right-hand side) on an instance from the palate aspect in our evaluation dataset.

Input: Figure 1: Examples on which the two perspectives give different instance-wise explanations.

Input: Figure 5: Explainers’ rankings (with the top 5 features on the right-hand side) on an instance from the appearance aspect in our evaluation.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, I believe the claim "We validate the efficacy of our evaluation by showing the failure modes of current explainers" is well-supported but somewhat overstated. While the authors demonstrate clear failure cases of LIME, SHAP, and L2X through quantitative metrics and qualitative examples, there are important limitations raised in the review comments about:
1. The reliability of using an RCNN as the verification framework
2. High standard deviations in the experimental results
3. Questions about generalizability to other domains
4. Dependence on the specific pruned dataset used

The evidence shows they identified failure modes, but calling this a full "validation" of their evaluation's efficacy overstates the strength of the conclusions given these limitations.

<score>3</score>

Justification:
- The paper successfully demonstrates failure cases through metrics showing explainers ranking irrelevant features highly (e.g., 14.79% cases where LIME ranks zero-contribution tokens as most important)
- However, the reviewer raises valid concerns about the fundamental approach of using a neural network (RCNN) to verify other explanations, noting results could change significantly with different data sampling
- The high standard deviations reported in Table 1 (e.g., 24.12 for LIME's avg_misrnk) suggest less reliability than implied
- Claim would be better stated as "We demonstrate potential failure modes" rather than claiming full validation


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We validate the efficacy of our evaluation by showing the failure modes of current explainers.

The review comment to be evaluated is:

-------------------- AFTER
The original rating of "Weak Reject" still holds as the authors failed to provide proper justification for the raise concerns and support their claims through additional experiments.

"We do not introduce an explanation generation framework, as explainers do. " - The proposed evaluation requires the explainer of the NLP model to agree with the RCNN in-terms of relevant or irrelevant words, to be considered a good explainer. The RCNN model which is defining the relevant and irrelevant tokens for a prediction task is in fact stating that we can explain the decision of an NLP model in terms of relevant and irrelevant tokens. Hence, the proposed RCNN can also be considered as an explainer. The evaluation task is demonstrating if the other explainers are providing explanations consistent with this new explainer based on RCNN.

"The RCNN is not meant to explain any other models except itself." - Unclear

"Regarding the request for more experiments:" - The authors don't provide enough justification to "why they didn't perform more experiments?"

" Hence, with our current instantiations, any domain-agnostic explainer can be evaluated" - The experiment to validate this claim are missing.

"The novelty of our paper consists in the fact that, to our knowledge, it is the first to (1) shed light over a fundamental difference" - This is not a technical novelty. This is an exploratory analysis based observation

"and (2) propose a methodology for evaluating explainers that ...and without human intervention (unlike evaluation type 4)." -  In Section 5 Qualitative Analysis, the authors are also doing human evaluation like other methods in evaluation type-4 of their related works. Also, doing human evaluation is a strong way to justify an explainer. Though expensive, whenever possible it should be done and is in no way a limitation of current evaluation metrics.

Your model needs labelled data for training RCNN. This adds a constraint on the usability and scalability of your proposed evaluation method. Since RCNN is also black-box, one will required another explainer to explain the RCNN.

In the worst-case scenario, if RCNN is trained with data such that it considers all relevant words as irrelevant, the evaluation made by RCNN will be incorrect. Hence, " Success depends on the ability of the RCNN to extract correct subsets of tokens."

-------------------  BEFORE
The paper proposed a verification framework to evaluate the performance of different explanatory methods in interpreting a given target model. Specifically, the authors evaluated three explanatory methods namely, LIME, SHAP and L2X for a target model trained to perform sentiment analysis on text data. Authors assume for each input text, there is a subset of tokens that are most relevant and that are completely irrelevant to the final prediction task.  The proposed framework uses a recurrent convolutional neural network (RCNN) to find these subsets. The performance of an explainer is evaluated in terms of overlap between the RCNN most relevant tokens and the most relevant tokens provided by the explainer as an explanation.

Major
•	The paper lack technical novelty.
•	The proposed architecture uses a RCNN to find the most relevant subset of tokens. Firstly, RCNN is also a black box that provides no intuition behind its selection decision. Secondly, in the absence of the ground truth labels for true relevance and irrelevance of a token in input sentence, this explainer method can also suffer from “assuming a reasonable behavior” assumption. The method assumes that the RCNN is performing reasonably in identifying relevant subsets.
•	The success of the method depends on the ability of the RCNN to extract correct subsets of tokens. The data used for training the RCNN, might have some underlying bias. In that case, the evaluation is not accurate.
•	In related work, for “Interpretable target models” the authors mentioned LIME as an example of explainer functions that explains target models that are “very simple models may not be representative for the large and intricate neural networks used in practice”. LIME locally explains the decision of a complex function for a given data point using simpler models like linear regression. But LIME itself can be used for generating explanation for prediction of complex neural network like Inception Net.
•	The example used to explain the difference between feature additive and feature selection-based explainer methods, is confusing. Its not clear how in health diagnostics, one will prefer feature-selection perspective. Although the most relevant features used for the instance are important to understand the decision, but in clinical settings sometimes low rank features can also be useful to understand the target model.
•	For text, the relevant features are the individual tokens of the input sentence. Similarly, for images relevance can be important regions of the image. The authors did not have any experiments on images or tabular data.
•	In the experiment section, the comparison is made with only 3 explainer models and for just one task. The experiments are inadequate.
•	In Figure 4, the colormap is not readable.

The evidence to be evaluated is:Another popular evaluation setup is to create synthetic tasks where the set of important features is controlled. For example, L2X (Chen et al., 2018) was evaluated on four synthetic tasks: 2-dim XOR, orange skin, nonlinear additive model, and switch feature. While there is no limit on the complexity of the target models trained on these setups, their synthetic nature may still prompt the target models to learn simpler functions than the ones needed for real-world applications. This, in turn, may ease the job for the explainers.

However, neural networks may discover surprising artifacts (Gururangan et al., 2018) to rely on, even when they obtain a high accuracy. Hence, this evaluation is not reliable for assessing the faithfulness of the explainer to the target model.

In this evaluation, humans are presented with a series of predictions of a model and explanations from different explainers, and are asked to infer the predictions (outputs) that the model will make on a separate set of examples. One concludes that an explainer \(E1\) is better than an explainer \(E2\) if humans are consistently better at predicting the output of the model after seeing explanations from \(E1\) than after seeing explanations from \(E2\)(Ribeiro et al., 2018). While this framework is a good proxy for evaluating the real-world usage of explanations, it is expensive and requires considerable human effort if it is to be applied on complex real-world neural network models. In contrast to the above, our evaluation is fully automatic, the target model is a non-trivial neural network trained on a real-world task and for which we provide guarantees on its inner-workings.

However, their test filters for the basic requirement that an explainer should provide different explanations for a model trained on real data than when the data and/or model are randomized. Our test is therefore more challenging and requires a stronger fidelity of the explainer to the target model.

For example, Gururangan et al. (2018) show that natural language inference neural networks trained on SNLI (Bowman et al., 2015) may heavily rely on the presence of a few specific tokens in the input, which should not even be, in general, indicators for the correct target class, e.g., "outdoors" for the entailment class, "tall" for the neutral class, and "sleeping" for the contradiction class.

Thus, we see that the feature-additive perspective aims to provide an _average explanation_ of the model on a _neighborhood_ of the instance, while the feature-selective perspective aims to tell us the pointwise features used by the model on the instance _in isolation_, such as "nice" for instance \(x_{1}\).

Therefore, we see that, while both perspectives of explanations give insights into the model's behavior, one perspective might be preferred over the other in different real-world use-cases. In the rest of the paper, we propose a verification framework for the _feature-selection_ perspective of instance-wise explanations.

We further prune the original dataset on which the RCNN had been trained to ensure that, for each datapoint \(x\), there exists a set of tokens that have zero contribution (irrelevant features) and a set of tokens that have a significant contribution (clearly relevant features) to RCNN's prediction on \(x\). We further introduce a set of metrics that measure how explainers fail to rank the irrelevant tokens lower than the clearly revelant ones.

This intermediate hard selection facilitates the existence of tokens that do not have any contribution to the final prediction. While Lei et al. (2016) aimed for \(\mathcal{S}_{x}\) to be the sufficient rationals for each prediction, the model might have learned an internal (emergent) communication protocol (Foerster et al., 2016) that encodes information from the non-selected via the selected tokens, which we call a _handshake_. For example, the RCNN could learn a handshake such as the one in Figure 2, where the feature "good" was important in all three cases, but not selected in the first two.

Our goal is to gather a dataset \(\mathcal{D}\) such that for all \(x\in\mathcal{D}\), the set of non-selected tokens, which we denote \(\mathcal{N}_{x}=x\setminus\mathcal{S}_{x}\), has zero contribution to the RCNN's prediction on \(x\). Equivalently, we want to eliminate instances that contain handshakes.

On our example in Figure 2, on the instance "The movie was very good.", the model selects "very" and predicts a score of \(1\). However, if we input the instance consisting of just "very", the model will not select anything2 and would return a score of \(0.5\). Thus, Equation 7 indeed captures the handshake in this example.

Our experiments show that the RCNN is capable of not selecting anything and providing its “bias” score as prediction.

In fact, it is possible that some tokens (such as "the" or "a") are actually noise, but have been selected only to ensure that the selection is a contiguous sequence (as we mentioned, the RCNN was penalized during training for selecting disconnected tokens). Since we do not want to penalize explainers for not differentiating between noise and zero-contribution features, we further prune the dataset such that there exists at least one selected token which is, without any doubt, _clearly relevant_ for the prediction.

Under our two properties above, we define the following error metrics:* **Percentage of instances for which the most important token provided by the explainer is among the non-selected tokens:** \[\text{\textasci{1.5}{\%}}_{\text{first}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum \limits_{x\in\mathcal{D}_{a}}\mathbb{1}_{\{r_{1}(x)\in\mathcal{N}_{x}\}}\,,\] (4) where \(\mathbb{1}\) is the indicator function.

* **Percentage of instances for which at least one non-selected token is ranked higher than a clearly relevant token:** \[\textasci{1.5}{\%}_{\text{mismrk}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum\limits_{x \in\mathcal{D}_{a}}\mathbb{1}_{\{\exists i<j\text{ such that }r_{i}(x)\in\mathcal{N}_{x}\text{ and }r_{j}(x)\in\mathcal{S} \mathcal{R}_{x}\}}\,.\] (5)

* **Average number of non-selected tokens ranked higher than any clearly relevant token:** \[\textasci{1.5}{avg\_mismrk}=\tfrac{1}{|\mathcal{D}_{a}|}\sum\limits_{x\in \mathcal{D}_{a}}\sum\limits_{i<\text{last},\text{\textasci{1.5}{\%}}} \mathbb{1}_{\{r_{i}(x)\in\mathcal{N}_{x}\}}\,,\] (6) where \(\text{last\_si}=\text{argmax}_{j}\{r_{j}(x)\in\mathcal{S}\mathcal{R}_{x}\}\) is the lowest rank of the clearly relevant tokens. Metric (A) shows the most dramatic failure: the percentage of times when the explainer tells us that the most relevant token is one of the zero contribution ones. Metric (B) shows the percentage of instances for which there is at least an error in the explanation. Finally, metric (C) quantifies the number of zero-contribution features that were ranked higher than any clearly relevant feature.

We test three popular explainers: LIME (Ribeiro et al., 2016), SHAP (Lundberg and Lee, 2017), and L2X (Chen et al., 2018).

As mentioned in Section 3, LIME and SHAP adhere to the feature-additivity perspective, hence our evaluation is not directly targeting these explainers.

In Table 1, we see that, on metric (A), all explainers are prone to stating that the most relevant feature is a token with zero contribution, as much as \(14.79\%\) of the time for LIME and \(12.95\%\) of the time for L2X in the aroma aspect. We consider this the most dramatic form of failure. Metric (B) shows that both explainers can rank at least one zero-contribution token higher than a clearly relevant feature, i.e., there is at least one mistake in the predicted ranking. Finally, metric (C) shows that, in average, SHAP only places one zero-contribution token ahead of a clearly relevant token for the first two aspects and around 9 tokens for the third aspect, while L2X places around 3-4 zero-contribution tokens ahead of a clearly relevant one for all three aspects.

Firstly, we notice that both explainers are prone to attributing importance to non-selected tokens, with LIME and SHAP even ranking the tokens "mouthfel" and "lacing" belonging to \(\mathcal{N}_{x}\) as first two (most important). Further, "gorgeous", the only relevant word used by the model, did not even make it in top 13 tokens for L2X.

Secondly, we introduced an off-the-shelf evaluation test for post-hoc explanatory methods under the feature-selection perspective. To our knowledge, this is the first automatic verification framework offering guarantees on the behaviour of a non-trivial real-world neural network. We presented the error rates on different metrics for three popular explanatory methods to raise awareness of the types of failures that these explainers can produce, such as incorrectly predicting even the most relevant token.

We also point out that the core algorithm in the majority of the current post-hoc explainers are also domain-agnostic. Therefore, we expect our evaluation to provide a representative view of the fundamental limitations of the explainers.

We note that if there is a handshake in the instance \(x\), i.e., at least one non-selected token \(x_{k}\in\mathcal{N}_{x}\) is actually influencing the final prediction via an internal encoding of its information into the selected tokens, then the model should have a different prediction when \(x_{k}\) is eliminated from the instance, i.e., \(\text{RCNN}(x)\neq\text{RCNN}(x-x_{k})\). Equivalently, if \(\text{RCNN}(x-x_{k})=\text{RCNN}(x)\), then \(x_{k}\) could not have been part of a handshake. Thus, if the RCNN gives the same prediction when eliminating _all_ the non-selected tokens, it means that there was no handshake for the instance \(x\), and hence the tokens in \(\mathcal{N}_{x}\) have indeed zero contribution.

\[\text{RCNN}(x-\mathcal{N}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{8}\] Since \(x-\mathcal{N}_{x}=\mathcal{S}_{x}\), Equation 8 rewrites as: \[\text{RCNN}(\mathcal{S}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{9}\] \begin{table}
\begin{tabular}{l c c c c c c c c}  & \multicolumn{2}{c}{APPEARANCE} & \multicolumn{2}{c}{AROMA} & \multicolumn{2}{c}{PALATE} \\ \hline Model & \%\_first & \%\_mismk & \(avg\_misrk\) & \%\_first & \%\_misrk & \%\_first & \%\_mismk & \%\_mismk \\ \hline LIME & **4.24** & 24.39 & 7.02 (24.12) & 14.79 & 32.08 & 12.74 (33.54) & 2.92 & 13.93 & **3.48** (17.38) \\ SHAP & 4.74 & **16.81** & **1.16** (7.75) & **4.24** & **13.53** & **0.83** (7.10) & **2.65** & **9.20** & 9.25 (9.70) \\ L2X & 6.58 & 28.85 & 3.54 (12.66) & 12.95 & 31.61 & 4.41 (16.25) & 12.77 & 29.83 & 3.70 (13.05) \\ \end{tabular}
\end{table}
Table 1: Error rates of the explainers. The lower the values, the better the explainer. Best results in bold. In parenthesis are the standard deviations when averages are reported.

Input: Table 1: Error rates of the explainers. The lower the values, the better the explainer. Best results in bold. In parenthesis are the standard deviations when averages are reported.

Input: Figure 2: Example of handshake.

Input: Figure 6: Explainers’ rankings (with the top 5 features on the right-hand side) on an instance from the aroma aspect in our evaluation.

Input: Figure 4: Explainers’ rankings (with top 5 features on the right-hand side) on an instance from the palate aspect in our evaluation dataset.

Input: Figure 1: Examples on which the two perspectives give different instance-wise explanations.

Input: Figure 5: Explainers’ rankings (with the top 5 features on the right-hand side) on an instance from the appearance aspect in our evaluation.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
I've reviewed the claim "We validate the efficacy of our evaluation by showing the failure modes of current explainers" along with the evidence provided.

Summary: The evidence shows the authors conducted systematic testing of three popular explainers (LIME, SHAP, L2X) and quantified their failure modes using specific metrics. They demonstrated concrete examples where explainers ranked irrelevant tokens as important and provided detailed error rates. The testing methodology was well-documented and revealed significant failure cases (e.g., up to 14.79% incorrect most-important token identification). The evidence strongly supports the claim through quantitative results and qualitative examples, making this a well-stated claim that accurately reflects the scope and findings of their evaluation.

<score>1</score>

Justification: The claim is well-stated because:
1. Clear metrics quantify explainer failures (Tables show error rates)
2. Multiple real examples demonstrate failure modes (Figures 4-6)
3. Comprehensive evaluation across three different aspects
4. Results validated through both quantitative metrics and qualitative analysis
5. Limitations and assumptions are acknowledged in the methodology

The review comments don't invalidate this specific claim about showing failure modes, as they focus more on other aspects of the paper.


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We validate the efficacy of our evaluation by showing the failure modes of current explainers.

The review comment to be evaluated is:

Overview/Contribution:
====================
The authors present a explanation generation framework that help validate post-hoc explanations when the explanations are generated based on feature selection. They claim to demonstrate their method by showing failure modes of exiting explanation generation methods.

Overall, the paper is not ready to be accepted to the conference and I describe my rational with the following strengths and weaknesses.

Strength:
========
+ Explanations make models more transparent and easy to understand for end users of the decision made by complex models such as deep neural networks [1]. In that respect, having a verification mechanism for post-hoc explanations is interesting and useful.
+ The paper is easy to read and follow.
Weakness:
===========
- evaluating explanations generated for an opaque model with another opaque model (RCNN) is cyclical.
- Just like many literature in this nascent space, interpretation (which is measuring the contribution of features or subsets of features towards predicted output) is confused as explanation. Human level explanations don’t necessarily depend on the direct interaction or contribution of model derived features. Rather they describe ‘why’ the model come up with the decision produced.
- Explanation generation is gaining traction in the deep learning community especially for critical applications such as healthcare and security. However, the authors claim that post-hoc explanations currently are only evaluated for only simple non-neural model. That is misleading given the recent attention toward generating explanations for various deep learning models.
- As a generalized pos-hoc explanation generators verification framework, the experiments are seriously lacking and are not well designed to illicit broad applicability.

1) Bekele, E., Lawson, W. E., Horne, Z., & Khemlani, S. (2018). Implementing a Robust Explanatory Bias in a Person Re-identification Network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 2165-2172).

The evidence to be evaluated is:Another popular evaluation setup is to create synthetic tasks where the set of important features is controlled. For example, L2X (Chen et al., 2018) was evaluated on four synthetic tasks: 2-dim XOR, orange skin, nonlinear additive model, and switch feature. While there is no limit on the complexity of the target models trained on these setups, their synthetic nature may still prompt the target models to learn simpler functions than the ones needed for real-world applications. This, in turn, may ease the job for the explainers.

However, neural networks may discover surprising artifacts (Gururangan et al., 2018) to rely on, even when they obtain a high accuracy. Hence, this evaluation is not reliable for assessing the faithfulness of the explainer to the target model.

In this evaluation, humans are presented with a series of predictions of a model and explanations from different explainers, and are asked to infer the predictions (outputs) that the model will make on a separate set of examples. One concludes that an explainer \(E1\) is better than an explainer \(E2\) if humans are consistently better at predicting the output of the model after seeing explanations from \(E1\) than after seeing explanations from \(E2\)(Ribeiro et al., 2018). While this framework is a good proxy for evaluating the real-world usage of explanations, it is expensive and requires considerable human effort if it is to be applied on complex real-world neural network models. In contrast to the above, our evaluation is fully automatic, the target model is a non-trivial neural network trained on a real-world task and for which we provide guarantees on its inner-workings.

However, their test filters for the basic requirement that an explainer should provide different explanations for a model trained on real data than when the data and/or model are randomized. Our test is therefore more challenging and requires a stronger fidelity of the explainer to the target model.

For example, Gururangan et al. (2018) show that natural language inference neural networks trained on SNLI (Bowman et al., 2015) may heavily rely on the presence of a few specific tokens in the input, which should not even be, in general, indicators for the correct target class, e.g., "outdoors" for the entailment class, "tall" for the neutral class, and "sleeping" for the contradiction class.

Thus, we see that the feature-additive perspective aims to provide an _average explanation_ of the model on a _neighborhood_ of the instance, while the feature-selective perspective aims to tell us the pointwise features used by the model on the instance _in isolation_, such as "nice" for instance \(x_{1}\).

Therefore, we see that, while both perspectives of explanations give insights into the model's behavior, one perspective might be preferred over the other in different real-world use-cases. In the rest of the paper, we propose a verification framework for the _feature-selection_ perspective of instance-wise explanations.

We further prune the original dataset on which the RCNN had been trained to ensure that, for each datapoint \(x\), there exists a set of tokens that have zero contribution (irrelevant features) and a set of tokens that have a significant contribution (clearly relevant features) to RCNN's prediction on \(x\). We further introduce a set of metrics that measure how explainers fail to rank the irrelevant tokens lower than the clearly revelant ones.

This intermediate hard selection facilitates the existence of tokens that do not have any contribution to the final prediction. While Lei et al. (2016) aimed for \(\mathcal{S}_{x}\) to be the sufficient rationals for each prediction, the model might have learned an internal (emergent) communication protocol (Foerster et al., 2016) that encodes information from the non-selected via the selected tokens, which we call a _handshake_. For example, the RCNN could learn a handshake such as the one in Figure 2, where the feature "good" was important in all three cases, but not selected in the first two.

Our goal is to gather a dataset \(\mathcal{D}\) such that for all \(x\in\mathcal{D}\), the set of non-selected tokens, which we denote \(\mathcal{N}_{x}=x\setminus\mathcal{S}_{x}\), has zero contribution to the RCNN's prediction on \(x\). Equivalently, we want to eliminate instances that contain handshakes.

On our example in Figure 2, on the instance "The movie was very good.", the model selects "very" and predicts a score of \(1\). However, if we input the instance consisting of just "very", the model will not select anything2 and would return a score of \(0.5\). Thus, Equation 7 indeed captures the handshake in this example.

Our experiments show that the RCNN is capable of not selecting anything and providing its “bias” score as prediction.

In fact, it is possible that some tokens (such as "the" or "a") are actually noise, but have been selected only to ensure that the selection is a contiguous sequence (as we mentioned, the RCNN was penalized during training for selecting disconnected tokens). Since we do not want to penalize explainers for not differentiating between noise and zero-contribution features, we further prune the dataset such that there exists at least one selected token which is, without any doubt, _clearly relevant_ for the prediction.

Under our two properties above, we define the following error metrics:* **Percentage of instances for which the most important token provided by the explainer is among the non-selected tokens:** \[\text{\textasci{1.5}{\%}}_{\text{first}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum \limits_{x\in\mathcal{D}_{a}}\mathbb{1}_{\{r_{1}(x)\in\mathcal{N}_{x}\}}\,,\] (4) where \(\mathbb{1}\) is the indicator function.

* **Percentage of instances for which at least one non-selected token is ranked higher than a clearly relevant token:** \[\textasci{1.5}{\%}_{\text{mismrk}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum\limits_{x \in\mathcal{D}_{a}}\mathbb{1}_{\{\exists i<j\text{ such that }r_{i}(x)\in\mathcal{N}_{x}\text{ and }r_{j}(x)\in\mathcal{S} \mathcal{R}_{x}\}}\,.\] (5)

* **Average number of non-selected tokens ranked higher than any clearly relevant token:** \[\textasci{1.5}{avg\_mismrk}=\tfrac{1}{|\mathcal{D}_{a}|}\sum\limits_{x\in \mathcal{D}_{a}}\sum\limits_{i<\text{last},\text{\textasci{1.5}{\%}}} \mathbb{1}_{\{r_{i}(x)\in\mathcal{N}_{x}\}}\,,\] (6) where \(\text{last\_si}=\text{argmax}_{j}\{r_{j}(x)\in\mathcal{S}\mathcal{R}_{x}\}\) is the lowest rank of the clearly relevant tokens. Metric (A) shows the most dramatic failure: the percentage of times when the explainer tells us that the most relevant token is one of the zero contribution ones. Metric (B) shows the percentage of instances for which there is at least an error in the explanation. Finally, metric (C) quantifies the number of zero-contribution features that were ranked higher than any clearly relevant feature.

We test three popular explainers: LIME (Ribeiro et al., 2016), SHAP (Lundberg and Lee, 2017), and L2X (Chen et al., 2018).

As mentioned in Section 3, LIME and SHAP adhere to the feature-additivity perspective, hence our evaluation is not directly targeting these explainers.

In Table 1, we see that, on metric (A), all explainers are prone to stating that the most relevant feature is a token with zero contribution, as much as \(14.79\%\) of the time for LIME and \(12.95\%\) of the time for L2X in the aroma aspect. We consider this the most dramatic form of failure. Metric (B) shows that both explainers can rank at least one zero-contribution token higher than a clearly relevant feature, i.e., there is at least one mistake in the predicted ranking. Finally, metric (C) shows that, in average, SHAP only places one zero-contribution token ahead of a clearly relevant token for the first two aspects and around 9 tokens for the third aspect, while L2X places around 3-4 zero-contribution tokens ahead of a clearly relevant one for all three aspects.

Firstly, we notice that both explainers are prone to attributing importance to non-selected tokens, with LIME and SHAP even ranking the tokens "mouthfel" and "lacing" belonging to \(\mathcal{N}_{x}\) as first two (most important). Further, "gorgeous", the only relevant word used by the model, did not even make it in top 13 tokens for L2X.

Secondly, we introduced an off-the-shelf evaluation test for post-hoc explanatory methods under the feature-selection perspective. To our knowledge, this is the first automatic verification framework offering guarantees on the behaviour of a non-trivial real-world neural network. We presented the error rates on different metrics for three popular explanatory methods to raise awareness of the types of failures that these explainers can produce, such as incorrectly predicting even the most relevant token.

We also point out that the core algorithm in the majority of the current post-hoc explainers are also domain-agnostic. Therefore, we expect our evaluation to provide a representative view of the fundamental limitations of the explainers.

We note that if there is a handshake in the instance \(x\), i.e., at least one non-selected token \(x_{k}\in\mathcal{N}_{x}\) is actually influencing the final prediction via an internal encoding of its information into the selected tokens, then the model should have a different prediction when \(x_{k}\) is eliminated from the instance, i.e., \(\text{RCNN}(x)\neq\text{RCNN}(x-x_{k})\). Equivalently, if \(\text{RCNN}(x-x_{k})=\text{RCNN}(x)\), then \(x_{k}\) could not have been part of a handshake. Thus, if the RCNN gives the same prediction when eliminating _all_ the non-selected tokens, it means that there was no handshake for the instance \(x\), and hence the tokens in \(\mathcal{N}_{x}\) have indeed zero contribution.

\[\text{RCNN}(x-\mathcal{N}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{8}\] Since \(x-\mathcal{N}_{x}=\mathcal{S}_{x}\), Equation 8 rewrites as: \[\text{RCNN}(\mathcal{S}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{9}\] \begin{table}
\begin{tabular}{l c c c c c c c c}  & \multicolumn{2}{c}{APPEARANCE} & \multicolumn{2}{c}{AROMA} & \multicolumn{2}{c}{PALATE} \\ \hline Model & \%\_first & \%\_mismk & \(avg\_misrk\) & \%\_first & \%\_misrk & \%\_first & \%\_mismk & \%\_mismk \\ \hline LIME & **4.24** & 24.39 & 7.02 (24.12) & 14.79 & 32.08 & 12.74 (33.54) & 2.92 & 13.93 & **3.48** (17.38) \\ SHAP & 4.74 & **16.81** & **1.16** (7.75) & **4.24** & **13.53** & **0.83** (7.10) & **2.65** & **9.20** & 9.25 (9.70) \\ L2X & 6.58 & 28.85 & 3.54 (12.66) & 12.95 & 31.61 & 4.41 (16.25) & 12.77 & 29.83 & 3.70 (13.05) \\ \end{tabular}
\end{table}
Table 1: Error rates of the explainers. The lower the values, the better the explainer. Best results in bold. In parenthesis are the standard deviations when averages are reported.

Input: Table 1: Error rates of the explainers. The lower the values, the better the explainer. Best results in bold. In parenthesis are the standard deviations when averages are reported.

Input: Figure 2: Example of handshake.

Input: Figure 6: Explainers’ rankings (with the top 5 features on the right-hand side) on an instance from the aroma aspect in our evaluation.

Input: Figure 4: Explainers’ rankings (with top 5 features on the right-hand side) on an instance from the palate aspect in our evaluation dataset.

Input: Figure 1: Examples on which the two perspectives give different instance-wise explanations.

Input: Figure 5: Explainers’ rankings (with the top 5 features on the right-hand side) on an instance from the appearance aspect in our evaluation.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, the claim "We validate the efficacy of our evaluation by showing the failure modes of current explainers" appears to be well-stated. The paper presents concrete metrics and experimental results demonstrating specific failure modes of LIME, SHAP, and L2X explainers, including:

- Quantitative error rates showing explainers incorrectly identifying zero-contribution tokens as most important (up to 14.79%)
- Clear examples where explainers rank non-selected tokens higher than relevant ones
- Systematic evaluation on a real-world neural network with guarantees about its behavior
- Detailed analysis backed by data in Table 1 showing consistent failure patterns across different aspects

The evidence directly supports the claim through rigorous experimental validation.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We aim for this framework to provide a publicly available,1 off-the-shelf evaluation when the feature-selection perspective on explanations is needed.

The evidence to be evaluated is:Another popular evaluation setup is to create synthetic tasks where the set of important features is controlled. For example, L2X (Chen et al., 2018) was evaluated on four synthetic tasks: 2-dim XOR, orange skin, nonlinear additive model, and switch feature.

In this setup, one identifies certain intuitive heuristics that a high-performing target model is assumed to follow. For example, in sentiment analysis, the model is supposed to rely on adjectives and adverbs in agreement with the predicted sentiment.

In contrast to the above, our evaluation is fully automatic, the target model is a non-trivial neural network trained on a real-world task and for which we provide guarantees on its inner-workings. Our framework is similar in scope with the sanity check introduced by Adebayo et al. (2018).

As mentioned before, current explanatory methods adhere to two major perspectives of explanations: _Perspective 1 (Feature-additivity)_: For a model \(f\) and an instance \(x\), the explanation of the prediction \(f(x)\) consists of a set of contributions \(\{w_{i}^{x}(f)\}_{i}\) for each feature \(i\) of \(x\) such that the sum of the contributions of the features in \(x\) approximates \(f(x)\), i.e., \(\sum_{i}w_{i}^{x}(f)\approx f(x)\). Many explanatory methods adhere to this perspective (Arras et al., 2017; Shrikumar et al., 2017; Ribeiro et al., 2016). For example, LIME (Ribeiro et al., 2016) learns the weights via a linear regression on the neighborhood (explained below) of the instance. Lundberg and Lee (2017) unified this class of methods by showing that the only set of feature-additive contributions that verify three desired constraints (local accuracy, missingness, and consistency--we refer to their paper for details) are given by the Shapley values from game theory: \[w_{i}^{x}(f)=\sum_{x^{\prime}\in x\setminus\{i\}}\frac{|x^{\prime}|![(|x|-|x^{ \prime}|-1)!}{|x|!}[f(x^{\prime}\cup\{i\})-f(x^{\prime})]\,, \tag{1}\] where the sum enumerates over all subsets \(x^{\prime}\) of features in \(x\) that do not include the feature \(i\), and \(|\cdot|\) denotes the number of features of its argument.

_Perspective 2 (Feature-selection)_: For a model \(f\) and an instance \(x\), the explanation of \(f(x)\) consists of a sufficient (ideally small) subset \(S(x)\) of (potentially ranked) features that alone lead to (almost) the same prediction as the original one, i.e., \(f(S(x))\approx f(x)\). Chen et al. (2018); Carter et al. (2018), and Ribeiro et al. (2018) adhere to this perspective. For example, L2X (Chen et al., 2018) learns \(S(x)\) by maximizing the mutual information between \(S(x)\) and the prediction.

However, this can be the case for certain tasks, such as sentiment analysis.

In the rest of the paper, we propose a verification framework for the _feature-selection_ perspective of instance-wise explanations.

We further prune the original dataset on which the RCNN had been trained to ensure that, for each datapoint \(x\), there exists a set of tokens that have zero contribution (irrelevant features) and a set of tokens that have a significant contribution (clearly relevant features) to RCNN's prediction on \(x\). We further introduce a set of metrics that measure how explainers fail to rank the irrelevant tokens lower than the clearly revelant ones.

We evaluate explainers that provide a ranking over the features. We denote by \(r_{1}(x),r_{2}(x),\ldots,r_{n}(x)\) the ranking (in decreasing order of importance) given by an explainer on the \(n=|x|\) features in the instance \(x\). Under our two properties above, we define the following error metrics:* **Percentage of instances for which the most important token provided by the explainer is among the non-selected tokens:** \[\text{\textasci{1.5}{\%}}_{\text{first}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum \limits_{x\in\mathcal{D}_{a}}\mathbb{1}_{\{r_{1}(x)\in\mathcal{N}_{x}\}}\,,\] (4) where \(\mathbb{1}\) is the indicator function.

* **Percentage of instances for which at least one non-selected token is ranked higher than a clearly relevant token:** \[\textasci{1.5}{\%}_{\text{mismrk}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum\limits_{x \in\mathcal{D}_{a}}\mathbb{1}_{\{\exists i<j\text{ such that }r_{i}(x)\in\mathcal{N}_{x}\text{ and }r_{j}(x)\in\mathcal{S} \mathcal{R}_{x}\}}\,.\] (5)

* **Average number of non-selected tokens ranked higher than any clearly relevant token:** \[\textasci{1.5}{avg\_mismrk}=\tfrac{1}{|\mathcal{D}_{a}|}\sum\limits_{x\in \mathcal{D}_{a}}\sum\limits_{i<\text{last},\text{\textasci{1.5}{\%}}} \mathbb{1}_{\{r_{i}(x)\in\mathcal{N}_{x}\}}\,,\] (6) where \(\text{last\_si}=\text{argmax}_{j}\{r_{j}(x)\in\mathcal{S}\mathcal{R}_{x}\}\) is the lowest rank of the clearly relevant tokens. Metric (A) shows the most dramatic failure: the percentage of times when the explainer tells us that the most relevant token is one of the zero contribution ones. Metric (B) shows the percentage of instances for which there is at least an error in the explanation. Finally, metric (C) quantifies the number of zero-contribution features that were ranked higher than any clearly relevant feature.

We test three popular explainers: LIME (Ribeiro et al., 2016), SHAP (Lundberg and Lee, 2017), and L2X (Chen et al., 2018). We used the code of the explainers as provided in the original repositories,5 with their default settings for text explanations, with the exception that, for L2X, we set the dimension of the word embeddings to \(200\) (the same as in the RCNN), and we also allowed training for a maximum of \(30\) epochs instead of \(5\).

As mentioned in Section 3, LIME and SHAP adhere to the feature-additivity perspective, hence our evaluation is not directly targeting these explainers.

Secondly, we introduced an off-the-shelf evaluation test for post-hoc explanatory methods under the feature-selection perspective. To our knowledge, this is the first automatic verification framework offering guarantees on the behaviour of a non-trivial real-world neural network.

While instantiated on a natural language processing task, our methodology is generic and can be adapted to other tasks and other areas.

We also point out that the core algorithm in the majority of the current post-hoc explainers are also domain-agnostic. Therefore, we expect our evaluation to provide a representative view of the fundamental limitations of the explainers.

Input: Figure 3: Partition of the features in our instances.

Input: Figure 4: Explainers’ rankings (with top 5 features on the right-hand side) on an instance from the palate aspect in our evaluation dataset.

Input: Figure 5: Explainers’ rankings (with the top 5 features on the right-hand side) on an instance from the appearance aspect in our evaluation.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find that the claim about providing a "publicly available, off-the-shelf evaluation" for feature-selection explanations is somewhat overstated. While the paper does present an evaluation framework with clear metrics and methodology, several limitations exist:

1. The framework is currently only demonstrated on a specific NLP task
2. The authors acknowledge it's similar to existing sanity checks
3. The evidence shows it was only tested with three popular explainers
4. While they claim it's domain-agnostic, this is not thoroughly demonstrated in the evidence provided

While the framework appears valuable, the claim would be better stated with acknowledgment of these limitations and current scope.

<score>3</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We aim for this framework to provide a publicly available,1 off-the-shelf evaluation when the feature-selection perspective on explanations is needed.

The review comment to be evaluated is:

Summary -

The paper proposes a verification method for instance wise feature explanations. The verification framework uses an RCNN to identify two types of tokens a) the tokens that are not predictive of outcome b) the subset of clearly relevant tokens for prediction. The data used for RCNN is a pruned version of the data used to train the  black-box. The pruning eliminates data points to ensure that the tokens not selected by RCNN have no contribution to the outcome and that the model does not exhibit suffer from learning "handshakes". A handshake is defined as the set of tokens that may be spuriously missed because their information is encoded in another relevant token. This proof to identify such data points is shown and the RNN is therefore expected to be able to reliably identify 3 kinds of tokens a) Those that have zero contribution to the outcome. b) Those that definitely have some contribution to the outcome and c) those that could be relevant or noisy. Three instance-wise feature selection methods are compared. Results are provided on 3 metrics. a) % instances for which the most important tokes provided by the explainer is among the non-selected tokens, b) % of instances for which at least one non-selected token is ranked higher than a relevant token, and c) Average number of non-selected tokens ranked higher than any obviously relevant tokens.

Clarifications and concerns:
1. For the dataset considered here, I would like to see the distribution of the irrelevant, clearly relevant and unsure if they are relevant tokens as detected by the RCNN. How does this change if I further prune the dataset after ensuring that handshake and other issues have been eliminated. The main concern I have is the idea of verifying other explanations using a neural network itself. I can train the RCNN neural network with half the data (and satisfy the properties the authors mention) and my evaluation would change significantly. From the appendix I see that most of the tokens could be in the set $SDR_x$.

2. What if the set of tokens don't overlap between the RCNN and the black-box to be verified. That said, I think the assumptions of the framework should be much more explicitly mentioned.

3. The std deviations in the experiments are very high. Can the authors justify this and how it is still okay to use this framework for evaluating feature importance based explanations.

Minor:
1. You have cited the "Anchors" paper twice?
2. Page 3 - typo - "....explainer should provide different explanations for the trained model on real data than when the data..."
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
Update -

I have read the authors response.
If the pruned dataset is created using an RCNN, then it is not clear if the RCNN is used to just explain itself or all other methods as well. Like I said, if we just train the model on a slightly different distribution of labels, or half the data randomly sampled irrespective of labels, the explanations will change because the pruned dataset i.e. the ground truth may significantly change. I am still not convinced how this makes for a good verification framework to asses other explainers.

It is also unclear how generalizable this verification process is to other domains. I will therefore not be updating my score.

The evidence to be evaluated is:Another popular evaluation setup is to create synthetic tasks where the set of important features is controlled. For example, L2X (Chen et al., 2018) was evaluated on four synthetic tasks: 2-dim XOR, orange skin, nonlinear additive model, and switch feature.

In this setup, one identifies certain intuitive heuristics that a high-performing target model is assumed to follow. For example, in sentiment analysis, the model is supposed to rely on adjectives and adverbs in agreement with the predicted sentiment.

In contrast to the above, our evaluation is fully automatic, the target model is a non-trivial neural network trained on a real-world task and for which we provide guarantees on its inner-workings. Our framework is similar in scope with the sanity check introduced by Adebayo et al. (2018).

As mentioned before, current explanatory methods adhere to two major perspectives of explanations: _Perspective 1 (Feature-additivity)_: For a model \(f\) and an instance \(x\), the explanation of the prediction \(f(x)\) consists of a set of contributions \(\{w_{i}^{x}(f)\}_{i}\) for each feature \(i\) of \(x\) such that the sum of the contributions of the features in \(x\) approximates \(f(x)\), i.e., \(\sum_{i}w_{i}^{x}(f)\approx f(x)\). Many explanatory methods adhere to this perspective (Arras et al., 2017; Shrikumar et al., 2017; Ribeiro et al., 2016). For example, LIME (Ribeiro et al., 2016) learns the weights via a linear regression on the neighborhood (explained below) of the instance. Lundberg and Lee (2017) unified this class of methods by showing that the only set of feature-additive contributions that verify three desired constraints (local accuracy, missingness, and consistency--we refer to their paper for details) are given by the Shapley values from game theory: \[w_{i}^{x}(f)=\sum_{x^{\prime}\in x\setminus\{i\}}\frac{|x^{\prime}|![(|x|-|x^{ \prime}|-1)!}{|x|!}[f(x^{\prime}\cup\{i\})-f(x^{\prime})]\,, \tag{1}\] where the sum enumerates over all subsets \(x^{\prime}\) of features in \(x\) that do not include the feature \(i\), and \(|\cdot|\) denotes the number of features of its argument.

_Perspective 2 (Feature-selection)_: For a model \(f\) and an instance \(x\), the explanation of \(f(x)\) consists of a sufficient (ideally small) subset \(S(x)\) of (potentially ranked) features that alone lead to (almost) the same prediction as the original one, i.e., \(f(S(x))\approx f(x)\). Chen et al. (2018); Carter et al. (2018), and Ribeiro et al. (2018) adhere to this perspective. For example, L2X (Chen et al., 2018) learns \(S(x)\) by maximizing the mutual information between \(S(x)\) and the prediction.

However, this can be the case for certain tasks, such as sentiment analysis.

In the rest of the paper, we propose a verification framework for the _feature-selection_ perspective of instance-wise explanations.

We further prune the original dataset on which the RCNN had been trained to ensure that, for each datapoint \(x\), there exists a set of tokens that have zero contribution (irrelevant features) and a set of tokens that have a significant contribution (clearly relevant features) to RCNN's prediction on \(x\). We further introduce a set of metrics that measure how explainers fail to rank the irrelevant tokens lower than the clearly revelant ones.

We evaluate explainers that provide a ranking over the features. We denote by \(r_{1}(x),r_{2}(x),\ldots,r_{n}(x)\) the ranking (in decreasing order of importance) given by an explainer on the \(n=|x|\) features in the instance \(x\). Under our two properties above, we define the following error metrics:* **Percentage of instances for which the most important token provided by the explainer is among the non-selected tokens:** \[\text{\textasci{1.5}{\%}}_{\text{first}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum \limits_{x\in\mathcal{D}_{a}}\mathbb{1}_{\{r_{1}(x)\in\mathcal{N}_{x}\}}\,,\] (4) where \(\mathbb{1}\) is the indicator function.

* **Percentage of instances for which at least one non-selected token is ranked higher than a clearly relevant token:** \[\textasci{1.5}{\%}_{\text{mismrk}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum\limits_{x \in\mathcal{D}_{a}}\mathbb{1}_{\{\exists i<j\text{ such that }r_{i}(x)\in\mathcal{N}_{x}\text{ and }r_{j}(x)\in\mathcal{S} \mathcal{R}_{x}\}}\,.\] (5)

* **Average number of non-selected tokens ranked higher than any clearly relevant token:** \[\textasci{1.5}{avg\_mismrk}=\tfrac{1}{|\mathcal{D}_{a}|}\sum\limits_{x\in \mathcal{D}_{a}}\sum\limits_{i<\text{last},\text{\textasci{1.5}{\%}}} \mathbb{1}_{\{r_{i}(x)\in\mathcal{N}_{x}\}}\,,\] (6) where \(\text{last\_si}=\text{argmax}_{j}\{r_{j}(x)\in\mathcal{S}\mathcal{R}_{x}\}\) is the lowest rank of the clearly relevant tokens. Metric (A) shows the most dramatic failure: the percentage of times when the explainer tells us that the most relevant token is one of the zero contribution ones. Metric (B) shows the percentage of instances for which there is at least an error in the explanation. Finally, metric (C) quantifies the number of zero-contribution features that were ranked higher than any clearly relevant feature.

We test three popular explainers: LIME (Ribeiro et al., 2016), SHAP (Lundberg and Lee, 2017), and L2X (Chen et al., 2018). We used the code of the explainers as provided in the original repositories,5 with their default settings for text explanations, with the exception that, for L2X, we set the dimension of the word embeddings to \(200\) (the same as in the RCNN), and we also allowed training for a maximum of \(30\) epochs instead of \(5\).

As mentioned in Section 3, LIME and SHAP adhere to the feature-additivity perspective, hence our evaluation is not directly targeting these explainers.

Secondly, we introduced an off-the-shelf evaluation test for post-hoc explanatory methods under the feature-selection perspective. To our knowledge, this is the first automatic verification framework offering guarantees on the behaviour of a non-trivial real-world neural network.

While instantiated on a natural language processing task, our methodology is generic and can be adapted to other tasks and other areas.

We also point out that the core algorithm in the majority of the current post-hoc explainers are also domain-agnostic. Therefore, we expect our evaluation to provide a representative view of the fundamental limitations of the explainers.

Input: Figure 3: Partition of the features in our instances.

Input: Figure 4: Explainers’ rankings (with top 5 features on the right-hand side) on an instance from the palate aspect in our evaluation dataset.

Input: Figure 5: Explainers’ rankings (with the top 5 features on the right-hand side) on an instance from the appearance aspect in our evaluation.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find several concerns about describing this framework as a "publicly available, off-the-shelf evaluation." While the paper presents a novel evaluation framework, the reviewer raises valid concerns about its reliability and generalizability. Key issues include:

1. Heavy dependence on RCNN training and data pruning that could significantly change results
2. Unclear generalizability to other domains
3. High standard deviations in experiments
4. Limited testing on only one type of task (NLP)

The claim appears overstated given these limitations. While the framework shows promise, describing it as a ready-to-use, general-purpose evaluation tool oversells its current capabilities and reliability. The evidence suggests this is more of an initial proof-of-concept that requires further validation and refinement.

<score>4</score>