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:

Hence, we test if explainers rank zero-contribution tokens higher than relevant tokens.

The evidence to be evaluated is:The most common instance-wise explanatory methods are _feature-based_, i.e., they explain a prediction in terms of the input unit-features (e.g., tokens for text and super-pixels for images). Among the feature-based explainers, there are two major types of explanations: (i) feature-additive: provide signed weights for each input feature, proportional to the contributions of the features to the model's prediction (Ribeiro et al., 2016; Lundberg and Lee, 2017; Shrikumar et al., 2017; Arras et al., 2017), and (ii) feature-selective: provide a (potentially ranked) subset of features responsible for the prediction (Chen et al., 2018; Ribeiro et al., 2018; Carter et al., 2018).

In this work, we focus on verifying feature-based explainers, since they represent the majority of current works. While many explainers have been proposed, it is still an open question how to thoroughly validate their faithfulness to the target model.

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

This intermediate hard selection facilitates the existence of tokens that do not have any contribution to the final prediction.

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 \(x\).

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. From now on, we refer to non-selected tokens as irrelevant or zero-contribution interchangeably.

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

With our pruning above, we ensured that the non-selected tokens have no contribution to the prediction. However, we are yet not sure that all the non-selected tokens are relevant to the prediction. In fact, it is possible that some tokens (such as "the" or "a") are actually noise, but have been selected only to ensure that the selection is a contiguous sequence (as we mentioned, the RCNN was penalized during training for selecting disconnected tokens). Since we do not want to penalize explainers for not differentiating between noise and zero-contribution features, we further prune the dataset such that there exists at least one selected token which is, without any doubt, _clearly relevant_ for the prediction. 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 \(\mathcal{S}\mathcal{R}_{x}\).

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)

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.

For each dataset, we know that for each instance \(x\in\mathcal{D}_{a}\), the set of non-selected tokens \(\mathcal{N}_{x}\) has zero contribution to the prediction of the model. For obtaining the clearly relevant tokens, we chose a threshold of \(\tau=0.1\), since the scores are in \([0,1]\), and the ground-truth ratings correspond to \(\{0,\,0.1,\,0.2,\,\,\dots\,,\,1\}\).

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

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

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.

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.

While for LIME and SHAP we could have used the actual weights, for consistency, and since we evaluate the explainers only on their rankings, we keep the ranking-like heatmap.

Secondly, we introduced an off-the-shelf evaluation test for post-hoc explanatory methods under the feature-selection perspective.

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 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. \begin{table}
\begin{tabular}{l c c c c c c c c}  & \multicolumn{2}{c}{APPEARANCE} & \multicolumn{2}{c}{AROMA} & \multicolumn{2}{c}{PALATE} \\ \hline Model & \%\_first & \%\_mismk & \(avg\_misrk\) & \%\_first & \%\_misrk & \%\_first & \%\_mismk & \%\_mismk \\ \hline LIME & **4.24** & 24.39 & 7.02 (24.12) & 14.79 & 32.08 & 12.74 (33.54) & 2.92 & 13.93 & **3.48** (17.38) \\ SHAP & 4.74 & **16.81** & **1.16** (7.75) & **4.24** & **13.53** & **0.83** (7.10) & **2.65** & **9.20** & 9.25 (9.70) \\ L2X & 6.58 & 28.85 & 3.54 (12.66) & 12.95 & 31.61 & 4.41 (16.25) & 12.77 & 29.83 & 3.70 (13.05) \\ \end{tabular}
\end{table}
Table 1: Error rates of the explainers. The lower the values, the better the explainer. Best results in bold. In parenthesis are the standard deviations when averages are reported.

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

Input: Figure 2: Example of handshake.

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

Input:
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 evaluate the claim "Hence, we test if explainers rank zero-contribution tokens higher than relevant tokens."

Brief evaluation: The claim is well-stated and accurately reflects the evidence provided. The paper clearly demonstrates how they created a dataset with identifiable zero-contribution tokens and clearly relevant tokens, then developed metrics to test if explainers incorrectly rank zero-contribution tokens higher. The evidence shows they used multiple metrics (%_first, %_misrnk, avg_misrnk) to systematically evaluate this across three different explainers (LIME, SHAP, L2X). Table 1 provides quantitative results supporting their testing methodology.

<score>1</score>

Justification:
1. The evidence shows a rigorous methodology for identifying zero-contribution tokens (through model behavior validation) and clearly relevant tokens (through significance thresholds)
2. The metrics directly measure ranking errors between zero-contribution and relevant tokens
3. The experimental results in Table 1 demonstrate they successfully tested this claim across multiple explainers and aspects
4. Figure 6 provides concrete examples of their evaluation in practice
5. The claim makes no exaggerated statements and accurately describes what was tested


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:

Hence, we test if explainers rank zero-contribution tokens higher than relevant tokens.

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:The most common instance-wise explanatory methods are _feature-based_, i.e., they explain a prediction in terms of the input unit-features (e.g., tokens for text and super-pixels for images). Among the feature-based explainers, there are two major types of explanations: (i) feature-additive: provide signed weights for each input feature, proportional to the contributions of the features to the model's prediction (Ribeiro et al., 2016; Lundberg and Lee, 2017; Shrikumar et al., 2017; Arras et al., 2017), and (ii) feature-selective: provide a (potentially ranked) subset of features responsible for the prediction (Chen et al., 2018; Ribeiro et al., 2018; Carter et al., 2018).

In this work, we focus on verifying feature-based explainers, since they represent the majority of current works. While many explainers have been proposed, it is still an open question how to thoroughly validate their faithfulness to the target model.

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

This intermediate hard selection facilitates the existence of tokens that do not have any contribution to the final prediction.

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 \(x\).

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. From now on, we refer to non-selected tokens as irrelevant or zero-contribution interchangeably.

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

With our pruning above, we ensured that the non-selected tokens have no contribution to the prediction. However, we are yet not sure that all the non-selected tokens are relevant to the prediction. In fact, it is possible that some tokens (such as "the" or "a") are actually noise, but have been selected only to ensure that the selection is a contiguous sequence (as we mentioned, the RCNN was penalized during training for selecting disconnected tokens). Since we do not want to penalize explainers for not differentiating between noise and zero-contribution features, we further prune the dataset such that there exists at least one selected token which is, without any doubt, _clearly relevant_ for the prediction. 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 \(\mathcal{S}\mathcal{R}_{x}\).

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)

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.

For each dataset, we know that for each instance \(x\in\mathcal{D}_{a}\), the set of non-selected tokens \(\mathcal{N}_{x}\) has zero contribution to the prediction of the model. For obtaining the clearly relevant tokens, we chose a threshold of \(\tau=0.1\), since the scores are in \([0,1]\), and the ground-truth ratings correspond to \(\{0,\,0.1,\,0.2,\,\,\dots\,,\,1\}\).

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

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

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.

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.

While for LIME and SHAP we could have used the actual weights, for consistency, and since we evaluate the explainers only on their rankings, we keep the ranking-like heatmap.

Secondly, we introduced an off-the-shelf evaluation test for post-hoc explanatory methods under the feature-selection perspective.

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 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. \begin{table}
\begin{tabular}{l c c c c c c c c}  & \multicolumn{2}{c}{APPEARANCE} & \multicolumn{2}{c}{AROMA} & \multicolumn{2}{c}{PALATE} \\ \hline Model & \%\_first & \%\_mismk & \(avg\_misrk\) & \%\_first & \%\_misrk & \%\_first & \%\_mismk & \%\_mismk \\ \hline LIME & **4.24** & 24.39 & 7.02 (24.12) & 14.79 & 32.08 & 12.74 (33.54) & 2.92 & 13.93 & **3.48** (17.38) \\ SHAP & 4.74 & **16.81** & **1.16** (7.75) & **4.24** & **13.53** & **0.83** (7.10) & **2.65** & **9.20** & 9.25 (9.70) \\ L2X & 6.58 & 28.85 & 3.54 (12.66) & 12.95 & 31.61 & 4.41 (16.25) & 12.77 & 29.83 & 3.70 (13.05) \\ \end{tabular}
\end{table}
Table 1: Error rates of the explainers. The lower the values, the better the explainer. Best results in bold. In parenthesis are the standard deviations when averages are reported.

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

Input: Figure 2: Example of handshake.

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

Input:
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 a well-stated claim. The evidence demonstrates that the authors test if explainers rank zero-contribution tokens higher than relevant tokens through multiple metrics: (1) checking if the most important token is among non-selected tokens, (2) checking if any non-selected token ranks higher than clearly relevant ones, and (3) counting average number of zero-contribution tokens ranked higher than relevant ones. The results in Table 1 and examples clearly show this testing approach, with LIME ranking zero-contribution tokens like "mouthfel" and "lacing" as most important in some cases.

<score>1</score>

Justification:
1. The claim directly aligns with the verification framework and metrics presented
2. Table 1 provides quantitative evidence of this testing through multiple metrics (%_first, %_misrnk, avg_misrnk)
3. Concrete examples are shown where explainers incorrectly rank zero-contribution tokens higher than relevant ones
4. The methodology for identifying zero-contribution vs relevant tokens is clearly explained
5. The reviewer's concerns about RCNN reliability don't directly challenge this specific claim about testing the ranking behavior


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:

Hence, we test if explainers rank zero-contribution tokens higher than relevant tokens.

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:The most common instance-wise explanatory methods are _feature-based_, i.e., they explain a prediction in terms of the input unit-features (e.g., tokens for text and super-pixels for images). Among the feature-based explainers, there are two major types of explanations: (i) feature-additive: provide signed weights for each input feature, proportional to the contributions of the features to the model's prediction (Ribeiro et al., 2016; Lundberg and Lee, 2017; Shrikumar et al., 2017; Arras et al., 2017), and (ii) feature-selective: provide a (potentially ranked) subset of features responsible for the prediction (Chen et al., 2018; Ribeiro et al., 2018; Carter et al., 2018).

In this work, we focus on verifying feature-based explainers, since they represent the majority of current works. While many explainers have been proposed, it is still an open question how to thoroughly validate their faithfulness to the target model.

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

This intermediate hard selection facilitates the existence of tokens that do not have any contribution to the final prediction.

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 \(x\).

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. From now on, we refer to non-selected tokens as irrelevant or zero-contribution interchangeably.

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

With our pruning above, we ensured that the non-selected tokens have no contribution to the prediction. However, we are yet not sure that all the non-selected tokens are relevant to the prediction. In fact, it is possible that some tokens (such as "the" or "a") are actually noise, but have been selected only to ensure that the selection is a contiguous sequence (as we mentioned, the RCNN was penalized during training for selecting disconnected tokens). Since we do not want to penalize explainers for not differentiating between noise and zero-contribution features, we further prune the dataset such that there exists at least one selected token which is, without any doubt, _clearly relevant_ for the prediction. 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 \(\mathcal{S}\mathcal{R}_{x}\).

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)

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.

For each dataset, we know that for each instance \(x\in\mathcal{D}_{a}\), the set of non-selected tokens \(\mathcal{N}_{x}\) has zero contribution to the prediction of the model. For obtaining the clearly relevant tokens, we chose a threshold of \(\tau=0.1\), since the scores are in \([0,1]\), and the ground-truth ratings correspond to \(\{0,\,0.1,\,0.2,\,\,\dots\,,\,1\}\).

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

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

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.

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.

While for LIME and SHAP we could have used the actual weights, for consistency, and since we evaluate the explainers only on their rankings, we keep the ranking-like heatmap.

Secondly, we introduced an off-the-shelf evaluation test for post-hoc explanatory methods under the feature-selection perspective.

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 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. \begin{table}
\begin{tabular}{l c c c c c c c c}  & \multicolumn{2}{c}{APPEARANCE} & \multicolumn{2}{c}{AROMA} & \multicolumn{2}{c}{PALATE} \\ \hline Model & \%\_first & \%\_mismk & \(avg\_misrk\) & \%\_first & \%\_misrk & \%\_first & \%\_mismk & \%\_mismk \\ \hline LIME & **4.24** & 24.39 & 7.02 (24.12) & 14.79 & 32.08 & 12.74 (33.54) & 2.92 & 13.93 & **3.48** (17.38) \\ SHAP & 4.74 & **16.81** & **1.16** (7.75) & **4.24** & **13.53** & **0.83** (7.10) & **2.65** & **9.20** & 9.25 (9.70) \\ L2X & 6.58 & 28.85 & 3.54 (12.66) & 12.95 & 31.61 & 4.41 (16.25) & 12.77 & 29.83 & 3.70 (13.05) \\ \end{tabular}
\end{table}
Table 1: Error rates of the explainers. The lower the values, the better the explainer. Best results in bold. In parenthesis are the standard deviations when averages are reported.

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

Input: Figure 2: Example of handshake.

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

Input:
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:
Based on the evidence presented, I'll evaluate whether the claim "we test if explainers rank zero-contribution tokens higher than relevant tokens" is well-stated or overstated.

The evidence strongly supports this claim. The paper clearly:
- Defines zero-contribution tokens through RCNN selection
- Establishes metrics to measure ranking errors
- Presents empirical results showing instances where explainers rank zero-contribution tokens higher than relevant ones (Table 1)
- Provides concrete examples demonstrating this behavior (Figure 6)

The claim is precise and accurately reflects what was tested, without exaggerating the findings. The methodology and results directly align with testing this specific behavior.

<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:

Hence, we test if explainers rank zero-contribution tokens higher than relevant tokens.

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:The most common instance-wise explanatory methods are _feature-based_, i.e., they explain a prediction in terms of the input unit-features (e.g., tokens for text and super-pixels for images). Among the feature-based explainers, there are two major types of explanations: (i) feature-additive: provide signed weights for each input feature, proportional to the contributions of the features to the model's prediction (Ribeiro et al., 2016; Lundberg and Lee, 2017; Shrikumar et al., 2017; Arras et al., 2017), and (ii) feature-selective: provide a (potentially ranked) subset of features responsible for the prediction (Chen et al., 2018; Ribeiro et al., 2018; Carter et al., 2018).

In this work, we focus on verifying feature-based explainers, since they represent the majority of current works. While many explainers have been proposed, it is still an open question how to thoroughly validate their faithfulness to the target model.

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

This intermediate hard selection facilitates the existence of tokens that do not have any contribution to the final prediction.

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 \(x\).

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. From now on, we refer to non-selected tokens as irrelevant or zero-contribution interchangeably.

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

With our pruning above, we ensured that the non-selected tokens have no contribution to the prediction. However, we are yet not sure that all the non-selected tokens are relevant to the prediction. In fact, it is possible that some tokens (such as "the" or "a") are actually noise, but have been selected only to ensure that the selection is a contiguous sequence (as we mentioned, the RCNN was penalized during training for selecting disconnected tokens). Since we do not want to penalize explainers for not differentiating between noise and zero-contribution features, we further prune the dataset such that there exists at least one selected token which is, without any doubt, _clearly relevant_ for the prediction. 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 \(\mathcal{S}\mathcal{R}_{x}\).

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)

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.

For each dataset, we know that for each instance \(x\in\mathcal{D}_{a}\), the set of non-selected tokens \(\mathcal{N}_{x}\) has zero contribution to the prediction of the model. For obtaining the clearly relevant tokens, we chose a threshold of \(\tau=0.1\), since the scores are in \([0,1]\), and the ground-truth ratings correspond to \(\{0,\,0.1,\,0.2,\,\,\dots\,,\,1\}\).

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

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

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.

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.

While for LIME and SHAP we could have used the actual weights, for consistency, and since we evaluate the explainers only on their rankings, we keep the ranking-like heatmap.

Secondly, we introduced an off-the-shelf evaluation test for post-hoc explanatory methods under the feature-selection perspective.

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 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. \begin{table}
\begin{tabular}{l c c c c c c c c}  & \multicolumn{2}{c}{APPEARANCE} & \multicolumn{2}{c}{AROMA} & \multicolumn{2}{c}{PALATE} \\ \hline Model & \%\_first & \%\_mismk & \(avg\_misrk\) & \%\_first & \%\_misrk & \%\_first & \%\_mismk & \%\_mismk \\ \hline LIME & **4.24** & 24.39 & 7.02 (24.12) & 14.79 & 32.08 & 12.74 (33.54) & 2.92 & 13.93 & **3.48** (17.38) \\ SHAP & 4.74 & **16.81** & **1.16** (7.75) & **4.24** & **13.53** & **0.83** (7.10) & **2.65** & **9.20** & 9.25 (9.70) \\ L2X & 6.58 & 28.85 & 3.54 (12.66) & 12.95 & 31.61 & 4.41 (16.25) & 12.77 & 29.83 & 3.70 (13.05) \\ \end{tabular}
\end{table}
Table 1: Error rates of the explainers. The lower the values, the better the explainer. Best results in bold. In parenthesis are the standard deviations when averages are reported.

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

Input: Figure 2: Example of handshake.

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

Input:
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:
Based on my review of the evidence, the claim "Hence, we test if explainers rank zero-contribution tokens higher than relevant tokens" appears to be well-stated and accurately reflects what the authors actually tested. The evidence shows they:

1. Carefully identified tokens with zero contribution using their RCNN model
2. Identified clearly relevant tokens using a threshold approach
3. Developed specific metrics to measure if explainers incorrectly rank zero-contribution tokens higher than relevant ones
4. Tested this systematically across multiple explainers (LIME, SHAP, L2X)
5. Provided quantitative results showing instances where explainers did rank zero-contribution tokens higher

The claim directly corresponds to their methodology and findings without overstating or exaggerating the scope or implications.

<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:

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 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:
Let me 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 through:
1. Clear demonstration of a rigorous methodology to identify "zero contribution" tokens and "clearly relevant" tokens
2. Conservative thresholds and pruning to ensure high guarantees
3. Well-defined error metrics that only penalize explainers when there are clear violations (e.g., ranking zero-contribution tokens above clearly relevant ones)
4. Mathematical proofs and formal verification of the "no handshake" property to guarantee zero contribution

The claim is well-stated as it accurately reflects the evidence without exaggeration. The authors are careful to outline their guarantees and limitations.

<score>1</score>

Justification:
1. The paper demonstrates thorough verification by:
- Pruning dataset to ensure zero contribution tokens are truly irrelevant: "With our pruning above, we ensured that the non-selected tokens have no contribution to the prediction."
- Using conservative thresholds: "we prefer to be more conservative in order to ensure high guarantees on our evaluation test"
- Providing mathematical guarantees through the "no handshake" property
2. The metrics only penalize clear violations:
- Property P1: "All tokens in Nx have to be ranked lower than any token in SRx"
- Metrics measure specific failures like ranking zero-contribution tokens above clearly relevant ones
3. The claim doesn't overstate capabilities and acknowledges limitations: "Our procedure only ensures that the tokens that change the prediction by a given (high) threshold are indeed important"


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:

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: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 this claim to be moderately overstated. While the framework does provide certain guarantees through its pruning process and metrics, several key limitations emerge:

1. The review raises valid concerns about the RCNN's reliability and the high standard deviations in results (Table 1 shows very high std devs, some over 30).

2. The framework's generalizability is questionable, as noted in the review comments about potential changes in evaluations with different data sampling.

3. The word "necessary" in the claim is too strong - the paper itself acknowledges limitations like having only 1-2 clearly relevant tokens per datapoint and being "conservative" in their approach.

4. While the metrics do penalize certain types of errors, they don't comprehensively capture all possible error types.

The evidence shows the framework provides some useful guarantees but not as definitively as claimed.

<score>4</score>

Justification:
- The claim overstates by using words like "necessary" and implying comprehensive error detection
- The paper itself acknowledges being "conservative" and having limitations
- High standard deviations (shown in Table 1) suggest less reliability than claimed
- Review raises valid concerns about framework's robustness to different data sampling
- Evidence shows useful but limited guarantees, not the comprehensive evaluation implied by the claim