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:

Our framework therefore generates necessary evaluation tests, and our metrics penalize explainers only when we are able to guarantee that they produced an error.

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: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.

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.

There is no direct supervision on the subset selection, and the generator and encoder were trained jointly, with supervision only on the final prediction.

At training time, to circumvent the non-differentiability introduced by the intermediate sampling, the gradients for the generator were estimated using a REINFORCE-style procedure (Williams, 1992).

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_.

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.

However, since it would be difficult to differentiate between such a case and an actual handshake, we simply prune the dataset by retaining only the instances for which \(\mathcal{S}_{\mathcal{S}_{x}}=\mathcal{S}_{x}\).

With our pruning above, we ensured that the non-selected tokens have no contribution to the prediction.

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. To ensure that a given selected token \(s\) is clearly relevant, we check that, when removing the token \(s\), the absolute change in prediction with respect to the original prediction is higher than a significant threshold \(\tau\).

Our procedure only ensures that the tokens that change the prediction by a given (high) threshold are indeed important and should therefore be ranked higher than any of the non-selected tokens, which have zero contribution.

First, we note that our procedure does not provide an explainer in itself, since we do not give an actual ranking, nor any contribution weights, and it is possible for some of the tokens in \(\mathcal{S}\mathcal{D}\mathcal{K}_{x}\) to be even more important than tokens in \(\mathcal{S}\mathcal{R}_{x}\). However, we guarantee the following two properties: **P1: ** All tokens in \(\mathcal{N}_{x}\) have to be ranked lower than any token in \(\mathcal{S}\mathcal{R}_{x}\). **P2:** The first most important token has to be in \(\mathcal{S}_{x}\).

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 note that we usually obtained \(1\) or \(2\) clearly relevant tokens per datapoints, showing that our threshold of \(0.1\) is likely very strict. However, we prefer to be more conservative in order to ensure high guarantees on our evaluation test.

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.

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:
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'll assess the claim: "Our framework therefore generates necessary evaluation tests, and our metrics penalize explainers only when we are able to guarantee that they produced an error."

Summary:
The evidence strongly supports this claim. The framework includes careful pruning steps to ensure:
1. Non-selected tokens have zero contribution (by eliminating handshakes)
2. Clear identification of relevant tokens (using significance threshold τ)
3. Well-defined error metrics that only penalize explainers when they violate guaranteed properties:
   - Ranking non-selected (zero-contribution) tokens above clearly relevant ones
   - Identifying zero-contribution tokens as most important

The methodology is conservative and only makes claims when there are strong guarantees. The empirical results in Table 1 demonstrate these guaranteed error cases occurring across different explainers.

<score>1</score>

The claim is well-stated as it accurately reflects what the framework delivers without exaggeration. The evidence provides detailed mathematical and empirical support for how the metrics only penalize explainers in cases where errors can be conclusively demonstrated.

The review comments, while raising valid concerns about other aspects of the paper, do not directly challenge the specific claim about the framework generating necessary tests and only penalizing guaranteed errors.


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:

Our framework therefore generates necessary evaluation tests, and our metrics penalize explainers only when we are able to guarantee that they produced an error.

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: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.

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.

There is no direct supervision on the subset selection, and the generator and encoder were trained jointly, with supervision only on the final prediction.

At training time, to circumvent the non-differentiability introduced by the intermediate sampling, the gradients for the generator were estimated using a REINFORCE-style procedure (Williams, 1992).

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_.

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.

However, since it would be difficult to differentiate between such a case and an actual handshake, we simply prune the dataset by retaining only the instances for which \(\mathcal{S}_{\mathcal{S}_{x}}=\mathcal{S}_{x}\).

With our pruning above, we ensured that the non-selected tokens have no contribution to the prediction.

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. To ensure that a given selected token \(s\) is clearly relevant, we check that, when removing the token \(s\), the absolute change in prediction with respect to the original prediction is higher than a significant threshold \(\tau\).

Our procedure only ensures that the tokens that change the prediction by a given (high) threshold are indeed important and should therefore be ranked higher than any of the non-selected tokens, which have zero contribution.

First, we note that our procedure does not provide an explainer in itself, since we do not give an actual ranking, nor any contribution weights, and it is possible for some of the tokens in \(\mathcal{S}\mathcal{D}\mathcal{K}_{x}\) to be even more important than tokens in \(\mathcal{S}\mathcal{R}_{x}\). However, we guarantee the following two properties: **P1: ** All tokens in \(\mathcal{N}_{x}\) have to be ranked lower than any token in \(\mathcal{S}\mathcal{R}_{x}\). **P2:** The first most important token has to be in \(\mathcal{S}_{x}\).

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 note that we usually obtained \(1\) or \(2\) clearly relevant tokens per datapoints, showing that our threshold of \(0.1\) is likely very strict. However, we prefer to be more conservative in order to ensure high guarantees on our evaluation test.

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.

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:
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 it to be moderately overstated. While the framework does provide some guarantees through careful dataset pruning and specific metrics, there are important limitations:

1. The framework only works for feature-selection explanations, not all types of explanations
2. The guarantees are limited to specific conditions (zero contribution tokens and clearly relevant tokens)
3. The method requires significant dataset pruning to establish these guarantees
4. The results in Table 1 show non-trivial error rates across all explainers tested

While the framework does generate evaluation tests and metrics that identify certain types of errors with guarantees, the claim's broad wording "necessary evaluation tests" and implying guarantees for all errors overstates what the evidence demonstrates. A more qualified statement acknowledging these limitations would be more accurate.

<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:

To our knowledge, we are the first to introduce an automatic and non-trivial evaluation test that does not rely on speculations on the behavior of the target model.

The evidence to be evaluated is: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.

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.

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.

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. To ensure that a given selected token \(s\) is clearly relevant, we check that, when removing the token \(s\), the absolute change in prediction with respect to the original prediction is higher than a significant threshold \(\tau\).

We highlight that simply because a selected token alone did not make a change in prediction higher than a threshold does not mean that this token is not relevant, as it may be essential in combination with other tokens. Our procedure only ensures that the tokens that change the prediction by a given (high) threshold are indeed important and should therefore be ranked higher than any of the non-selected tokens, which have zero contribution. We thus further prune the dataset to retain only the datapoints \(x\) for which \(|\mathcal{S}\mathcal{R}_{x}|\geq 1\), i.e., there is at least one clearly relevant token per instance.

First, we note that our procedure does not provide an explainer in itself, since we do not give an actual ranking, nor any contribution weights, and it is possible for some of the tokens in \(\mathcal{S}\mathcal{D}\mathcal{K}_{x}\) to be even more important than tokens in \(\mathcal{S}\mathcal{R}_{x}\). However, we guarantee the following two properties: **P1: ** All tokens in \(\mathcal{N}_{x}\) have to be ranked lower than any token in \(\mathcal{S}\mathcal{R}_{x}\). **P2:** The first most important token has to be in \(\mathcal{S}_{x}\). 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.

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: \[\text{RCNN}(\mathcal{S}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{9}\]

Input: Figure 2: Example of handshake.

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 partially overstated. While the authors do present a novel automatic evaluation framework with certain guarantees about the target model's behavior, there are a few key considerations:

1. The claim of being "first" is very strong and difficult to definitively prove
2. The evidence shows they specifically focus on the "feature-selection perspective" of instance-wise explanations, which is more narrow than the general claim suggests
3. Their framework does rely on some assumptions and specific conditions (like pruning the dataset to ensure zero contribution tokens and clearly relevant features exist)
4. The test provides guarantees under specific circumstances rather than completely avoiding "speculations"

While their approach is innovative and provides concrete metrics, the claim would be better stated with more qualification about its specific scope and limitations.

<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:

To our knowledge, we are the first to introduce an automatic and non-trivial evaluation test that does not rely on speculations on the behavior of the target model.

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: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.

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.

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.

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. To ensure that a given selected token \(s\) is clearly relevant, we check that, when removing the token \(s\), the absolute change in prediction with respect to the original prediction is higher than a significant threshold \(\tau\).

We highlight that simply because a selected token alone did not make a change in prediction higher than a threshold does not mean that this token is not relevant, as it may be essential in combination with other tokens. Our procedure only ensures that the tokens that change the prediction by a given (high) threshold are indeed important and should therefore be ranked higher than any of the non-selected tokens, which have zero contribution. We thus further prune the dataset to retain only the datapoints \(x\) for which \(|\mathcal{S}\mathcal{R}_{x}|\geq 1\), i.e., there is at least one clearly relevant token per instance.

First, we note that our procedure does not provide an explainer in itself, since we do not give an actual ranking, nor any contribution weights, and it is possible for some of the tokens in \(\mathcal{S}\mathcal{D}\mathcal{K}_{x}\) to be even more important than tokens in \(\mathcal{S}\mathcal{R}_{x}\). However, we guarantee the following two properties: **P1: ** All tokens in \(\mathcal{N}_{x}\) have to be ranked lower than any token in \(\mathcal{S}\mathcal{R}_{x}\). **P2:** The first most important token has to be in \(\mathcal{S}_{x}\). 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.

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: \[\text{RCNN}(\mathcal{S}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{9}\]

Input: Figure 2: Example of handshake.

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, this appears to be somewhat overstated. While the authors present a novel automatic evaluation framework with certain guarantees for feature-selection explanations, several limitations exist:

1. The reviewer raises valid concerns about the framework's dependency on an RCNN and how changes in training data could significantly impact results.
2. The high standard deviations in experiments suggest some instability.
3. The generalizability to other domains is questioned.
4. The framework only works for feature-selection perspective and requires specific conditions (pruned dataset, existence of clearly relevant tokens).

While the method is innovative and automatic, claiming to be "the first" without acknowledging these limitations or potential related work is an overstatement.

<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:

To our knowledge, we are the first to introduce an automatic and non-trivial evaluation test that does not rely on speculations on the behavior of the target model.

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: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.

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.

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.

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. To ensure that a given selected token \(s\) is clearly relevant, we check that, when removing the token \(s\), the absolute change in prediction with respect to the original prediction is higher than a significant threshold \(\tau\).

We highlight that simply because a selected token alone did not make a change in prediction higher than a threshold does not mean that this token is not relevant, as it may be essential in combination with other tokens. Our procedure only ensures that the tokens that change the prediction by a given (high) threshold are indeed important and should therefore be ranked higher than any of the non-selected tokens, which have zero contribution. We thus further prune the dataset to retain only the datapoints \(x\) for which \(|\mathcal{S}\mathcal{R}_{x}|\geq 1\), i.e., there is at least one clearly relevant token per instance.

First, we note that our procedure does not provide an explainer in itself, since we do not give an actual ranking, nor any contribution weights, and it is possible for some of the tokens in \(\mathcal{S}\mathcal{D}\mathcal{K}_{x}\) to be even more important than tokens in \(\mathcal{S}\mathcal{R}_{x}\). However, we guarantee the following two properties: **P1: ** All tokens in \(\mathcal{N}_{x}\) have to be ranked lower than any token in \(\mathcal{S}\mathcal{R}_{x}\). **P2:** The first most important token has to be in \(\mathcal{S}_{x}\). 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.

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: \[\text{RCNN}(\mathcal{S}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{9}\]

Input: Figure 2: Example of handshake.

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 and review comments, I'll evaluate the claim "To our knowledge, we are the first to introduce an automatic and non-trivial evaluation test that does not rely on speculations on the behavior of the target model."

Summary: This claim appears overstated for several reasons:

1. The reviewer points out that the RCNN itself could be considered an explainer, undermining the claim of not relying on speculations about model behavior
2. The method still requires labeled training data and relies on RCNN's ability to correctly identify relevant tokens
3. The evidence shows the framework has limitations and assumptions about the RCNN's behavior
4. The review notes that human evaluation is still used in Section 5, contradicting the fully "automatic" nature claimed
5. The claim of being "first" is particularly strong given the reviewer's points about technical novelty

<score>4</score>

The claim would be better stated by acknowledging these limitations and positioning it as an additional approach rather than claiming to be the first automatic, non-speculative evaluation method.


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:

To our knowledge, we are the first to introduce an automatic and non-trivial evaluation test that does not rely on speculations on the behavior of the target model.

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: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.

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.

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.

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. To ensure that a given selected token \(s\) is clearly relevant, we check that, when removing the token \(s\), the absolute change in prediction with respect to the original prediction is higher than a significant threshold \(\tau\).

We highlight that simply because a selected token alone did not make a change in prediction higher than a threshold does not mean that this token is not relevant, as it may be essential in combination with other tokens. Our procedure only ensures that the tokens that change the prediction by a given (high) threshold are indeed important and should therefore be ranked higher than any of the non-selected tokens, which have zero contribution. We thus further prune the dataset to retain only the datapoints \(x\) for which \(|\mathcal{S}\mathcal{R}_{x}|\geq 1\), i.e., there is at least one clearly relevant token per instance.

First, we note that our procedure does not provide an explainer in itself, since we do not give an actual ranking, nor any contribution weights, and it is possible for some of the tokens in \(\mathcal{S}\mathcal{D}\mathcal{K}_{x}\) to be even more important than tokens in \(\mathcal{S}\mathcal{R}_{x}\). However, we guarantee the following two properties: **P1: ** All tokens in \(\mathcal{N}_{x}\) have to be ranked lower than any token in \(\mathcal{S}\mathcal{R}_{x}\). **P2:** The first most important token has to be in \(\mathcal{S}_{x}\). 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.

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: \[\text{RCNN}(\mathcal{S}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{9}\]

Input: Figure 2: Example of handshake.

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 this claim to be somewhat overstated. While the authors do present a novel automatic evaluation framework with certain guarantees for feature-selection based explanations, the reviewer correctly points out that post-hoc explanations have been previously evaluated for deep learning models. The claim of being "first" is too strong, though their specific approach of using guaranteed zero-contribution features appears novel. Their framework is more specific (focused on feature-selection) rather than being completely general as implied.

The key limitations are:
1. Scope is limited to feature-selection explanations only
2. Uses another neural network (RCNN) for verification
3. Prior work exists on evaluating explanations for deep learning models
4. Framework makes specific assumptions about feature contributions

<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:

Interestingly, we find that, most of the time, LIME and SHAP perform better than L2X.

The evidence to be evaluated is:However, we see in Table 1 that, in practice, LIME and SHAP outperformed L2X on the majority of the metrics, even though L2X is a feature-selection explainer. We hypothesize that a major limitation of L2X is the requirement to know the number of important features per instance. Indeed, L2X learns a distribution over the set of features by maximizing the mutual information between subsets of \(K\) features and the response variable, where \(K\) is assumed to be known. In practice, one usually does not know how many features per instance a model relied on.

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. \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:
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:
Let me assess the claim based on the provided evidence:

The claim that "LIME and SHAP perform better than L2X most of the time" appears to be well-stated based on the evidence presented in Table 1. Looking at the metrics across all three aspects (APPEARANCE, AROMA, PALATE):

1. SHAP consistently shows the best performance (bold numbers) across most metrics
2. LIME performs better than L2X in most cases, though not as consistently as SHAP
3. L2X generally shows higher error rates across metrics compared to both LIME and SHAP
4. The evidence explicitly states that "LIME and SHAP outperformed L2X on the majority of the metrics"

The claim is modest in using "most of the time" rather than making an absolute statement, acknowledging that the performance difference isn't universal. The evidence directly supports this measured claim.

<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:

Interestingly, we find that, most of the time, LIME and SHAP perform better than L2X.

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:However, we see in Table 1 that, in practice, LIME and SHAP outperformed L2X on the majority of the metrics, even though L2X is a feature-selection explainer. We hypothesize that a major limitation of L2X is the requirement to know the number of important features per instance. Indeed, L2X learns a distribution over the set of features by maximizing the mutual information between subsets of \(K\) features and the response variable, where \(K\) is assumed to be known. In practice, one usually does not know how many features per instance a model relied on.

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. \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:
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:
Let me evaluate the claim "Interestingly, we find that, most of the time, LIME and SHAP perform better than L2X."

Looking at Table 1, LIME and SHAP indeed show better performance than L2X across most metrics:
- For APPEARANCE: LIME/SHAP have lower %_first (4.24%/4.74% vs 6.58%)
- For AROMA: SHAP has significantly better performance across all metrics
- For PALATE: LIME/SHAP have better performance (2.92%/2.65% vs 12.77% in %_first)

However, the high standard deviations (mentioned in both evidence and review comments) suggest considerable variability in performance. The review also raises valid concerns about the verification framework's reliability and generalizability.

The claim is mostly supported by the evidence but could be more precisely stated to acknowledge the high variability and framework limitations.

<score>2</score>

Justification:
1. Evidence supports LIME/SHAP generally outperforming L2X
2. "Most of the time" is somewhat imprecise but reasonably reflects the tabulated results
3. The claim would be better with acknowledgment of:
   - High standard deviations
   - Verification framework limitations
   - Specific metrics where performance differs
4. While slightly imprecise, the claim isn't substantially overstated given the evidence