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:

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

The evidence from Table 1 shows that SHAP generally outperforms L2X across all metrics and aspects, with LIME showing mixed performance. Looking at the metrics:
- %_first: LIME and SHAP perform better than L2X in 2 out of 3 aspects
- %_misrnk: SHAP consistently outperforms L2X, while LIME varies
- avg_misrnk: SHAP clearly outperforms L2X, while LIME underperforms in some cases

While the claim about SHAP is well-supported, the inclusion of LIME in the statement makes it slightly overstated since LIME's performance is more variable and sometimes worse than L2X. The claim would be more accurate if it specifically highlighted SHAP's superior performance while noting LIME's mixed results.

<score>2</score>

The score of 2 reflects that while the claim has some merit based on the evidence, particularly for SHAP, the blanket statement about both LIME and SHAP performing better "most of the time" is a slight overstatement given LIME's inconsistent performance.


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:

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: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:
I'll evaluate the claim "Interestingly, we find that, most of the time, LIME and SHAP perform better than L2X."

Analyzing the evidence from Table 1 and the text:
- SHAP consistently outperforms L2X across all metrics and aspects
- LIME outperforms L2X in most metrics but not all
- The evidence shows specific measurements comparing error rates where SHAP and LIME generally have lower error rates than L2X
- The limitation of L2X requiring known K features is explained, providing reasoning for the performance difference

The claim is well-stated (score 1) because:
1. The phrase "most of the time" accurately reflects that while SHAP/LIME generally perform better, it's not universal
2. The comparative performance is directly supported by quantitative evidence
3. The claim avoids absolute statements and maintains appropriate uncertainty
4. The evidence provides clear metrics showing LIME/SHAP's superior performance in majority of cases

<score>1</score>


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

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

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

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

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

The claim to be assessed is:

We provide the error rates of these explanatory methods to raise awareness of their possible modes of failure under the feature-selection perspective of explanations.

The evidence to be evaluated is: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.

Typically, explainers are tested on linear regression and decision trees (e.g., LIME (Ribeiro et al., 2016)) or support vector representations (e.g., MAPLE (Plumb et al., 2018)). While this evaluation accurately assesses the faithfulness of the explainer to the target model, these very simple models may not be representative for the large and intricate neural networks used in practice.

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

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

_Perspective 2 (Feature-selection)_: For a model \(f\) and an instance \(x\), the explanation of \(f(x)\) consists of a sufficient (ideally small) subset \(S(x)\) of (potentially ranked) features that alone lead to (almost) the same prediction as the original one, i.e., \(f(S(x))\approx f(x)\). Chen et al. (2018); Carter et al. (2018), and Ribeiro et al. (2018) adhere to this perspective. For example, L2X (Chen et al., 2018) learns \(S(x)\) by maximizing the mutual information between \(S(x)\) and the prediction. However, it assumes that the number of important features per instance, i.e., \(|S(x)|\), is known, which is usually not the case in practice. A downside of this perspective is that it may not always be true that the model relied only on a (small) subset of features, as opposed to using all the features. However, this can be the case for certain tasks, such as sentiment analysis.

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

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

This intermediate hard selection facilitates the existence of tokens that do not have any contribution to the final prediction. While Lei et al. (2016) aimed for \(\mathcal{S}_{x}\) to be the sufficient rationals for each prediction, the model might have learned an internal (emergent) communication protocol (Foerster et al., 2016) that encodes information from the non-selected via the selected tokens, which we call a _handshake_.

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.

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.

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 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.

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

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

Output:
After reviewing the claim and evidence, I find this to be a well-stated claim. The paper thoroughly demonstrates awareness of failure modes through:
1. Comprehensive error metrics (% first, % mismark, avg_misrank)
2. Detailed quantitative results showing specific error rates (e.g., LIME's 14.79% error rate for most important token in aroma aspect)
3. Clear examples of ranking failures through visualizations
4. Balanced discussion of limitations across different explainers (LIME, SHAP, L2X)

The claim modestly states they "provide error rates to raise awareness" rather than making broader proclamations, accurately reflecting their methodical analysis and findings.

<score>1</score>


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

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

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

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

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

The claim to be assessed is:

We provide the error rates of these explanatory methods to raise awareness of their possible modes of failure under the feature-selection perspective of explanations.

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: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.

Typically, explainers are tested on linear regression and decision trees (e.g., LIME (Ribeiro et al., 2016)) or support vector representations (e.g., MAPLE (Plumb et al., 2018)). While this evaluation accurately assesses the faithfulness of the explainer to the target model, these very simple models may not be representative for the large and intricate neural networks used in practice.

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

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

_Perspective 2 (Feature-selection)_: For a model \(f\) and an instance \(x\), the explanation of \(f(x)\) consists of a sufficient (ideally small) subset \(S(x)\) of (potentially ranked) features that alone lead to (almost) the same prediction as the original one, i.e., \(f(S(x))\approx f(x)\). Chen et al. (2018); Carter et al. (2018), and Ribeiro et al. (2018) adhere to this perspective. For example, L2X (Chen et al., 2018) learns \(S(x)\) by maximizing the mutual information between \(S(x)\) and the prediction. However, it assumes that the number of important features per instance, i.e., \(|S(x)|\), is known, which is usually not the case in practice. A downside of this perspective is that it may not always be true that the model relied only on a (small) subset of features, as opposed to using all the features. However, this can be the case for certain tasks, such as sentiment analysis.

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

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

This intermediate hard selection facilitates the existence of tokens that do not have any contribution to the final prediction. While Lei et al. (2016) aimed for \(\mathcal{S}_{x}\) to be the sufficient rationals for each prediction, the model might have learned an internal (emergent) communication protocol (Foerster et al., 2016) that encodes information from the non-selected via the selected tokens, which we call a _handshake_.

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.

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.

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 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.

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

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

Output:
After reviewing the evidence, the claim "We provide the error rates of these explanatory methods to raise awareness of their possible modes of failure under the feature-selection perspective of explanations" appears well-stated (score of 1). The paper thoroughly demonstrates this by:

1. Providing comprehensive error metrics with detailed results in Table 1
2. Showing concrete examples of failures through visualizations (Figure 5)
3. Including statistical evidence with standard deviations
4. Carefully documenting the limitations and failure modes of different explainers

The claim is modest and accurately reflects what the paper delivers - a systematic evaluation of error rates to highlight potential failures, without overstating the implications or making sweeping generalizations.

<score>1</score>


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

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

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

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

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

The claim to be assessed is:

We provide the error rates of these explanatory methods to raise awareness of their possible modes of failure under the feature-selection perspective of explanations.

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: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.

Typically, explainers are tested on linear regression and decision trees (e.g., LIME (Ribeiro et al., 2016)) or support vector representations (e.g., MAPLE (Plumb et al., 2018)). While this evaluation accurately assesses the faithfulness of the explainer to the target model, these very simple models may not be representative for the large and intricate neural networks used in practice.

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

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

_Perspective 2 (Feature-selection)_: For a model \(f\) and an instance \(x\), the explanation of \(f(x)\) consists of a sufficient (ideally small) subset \(S(x)\) of (potentially ranked) features that alone lead to (almost) the same prediction as the original one, i.e., \(f(S(x))\approx f(x)\). Chen et al. (2018); Carter et al. (2018), and Ribeiro et al. (2018) adhere to this perspective. For example, L2X (Chen et al., 2018) learns \(S(x)\) by maximizing the mutual information between \(S(x)\) and the prediction. However, it assumes that the number of important features per instance, i.e., \(|S(x)|\), is known, which is usually not the case in practice. A downside of this perspective is that it may not always be true that the model relied only on a (small) subset of features, as opposed to using all the features. However, this can be the case for certain tasks, such as sentiment analysis.

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

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

This intermediate hard selection facilitates the existence of tokens that do not have any contribution to the final prediction. While Lei et al. (2016) aimed for \(\mathcal{S}_{x}\) to be the sufficient rationals for each prediction, the model might have learned an internal (emergent) communication protocol (Foerster et al., 2016) that encodes information from the non-selected via the selected tokens, which we call a _handshake_.

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.

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.

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 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.

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

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

Output:
Based on my review of the evidence, this claim regarding providing error rates to raise awareness of failure modes appears well-stated and supported by the evidence. The paper presents detailed error metrics and results in Table 1 showing specific failure rates across different explainers (LIME, SHAP, L2X) and aspects (Appearance, Aroma, Palate). The evidence demonstrates concrete examples of failures, such as explainers incorrectly ranking non-selected tokens as most important up to 14.79% of the time. The claims are measured and backed by quantitative data without overstating the implications.

<score>1</score>

Justification:
1. The claim is precise and directly supported by Table 1's error metrics
2. Results show specific failure rates without exaggeration (e.g. "as much as 14.79% of the time for LIME")
3. Evaluation includes multiple metrics (%_first, %_mismrk, avg_misrk) providing comprehensive error analysis
4. Figure 5 provides concrete examples of ranking failures
5. The claim focuses on "raising awareness" rather than making broader unsupported generalizations

The reviewer comments, while critical of other aspects of the paper, do not specifically challenge the validity of the error rate findings or their presentation.


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

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

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

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

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

The claim to be assessed is:

We provide the error rates of these explanatory methods to raise awareness of their possible modes of failure under the feature-selection perspective of explanations.

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: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.

Typically, explainers are tested on linear regression and decision trees (e.g., LIME (Ribeiro et al., 2016)) or support vector representations (e.g., MAPLE (Plumb et al., 2018)). While this evaluation accurately assesses the faithfulness of the explainer to the target model, these very simple models may not be representative for the large and intricate neural networks used in practice.

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

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

_Perspective 2 (Feature-selection)_: For a model \(f\) and an instance \(x\), the explanation of \(f(x)\) consists of a sufficient (ideally small) subset \(S(x)\) of (potentially ranked) features that alone lead to (almost) the same prediction as the original one, i.e., \(f(S(x))\approx f(x)\). Chen et al. (2018); Carter et al. (2018), and Ribeiro et al. (2018) adhere to this perspective. For example, L2X (Chen et al., 2018) learns \(S(x)\) by maximizing the mutual information between \(S(x)\) and the prediction. However, it assumes that the number of important features per instance, i.e., \(|S(x)|\), is known, which is usually not the case in practice. A downside of this perspective is that it may not always be true that the model relied only on a (small) subset of features, as opposed to using all the features. However, this can be the case for certain tasks, such as sentiment analysis.

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

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

This intermediate hard selection facilitates the existence of tokens that do not have any contribution to the final prediction. While Lei et al. (2016) aimed for \(\mathcal{S}_{x}\) to be the sufficient rationals for each prediction, the model might have learned an internal (emergent) communication protocol (Foerster et al., 2016) that encodes information from the non-selected via the selected tokens, which we call a _handshake_.

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.

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.

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 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.

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

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

Output:
Based on the evidence provided, the claim that "We provide the error rates of these explanatory methods to raise awareness of their possible modes of failure under the feature-selection perspective of explanations" appears well-stated. The paper presents clear metrics and empirical results demonstrating specific failure modes through:

1. Detailed error metrics with concrete percentages (Table 1)
2. Examples showing incorrect feature rankings (Figure 5)
3. Specific failure cases discussed (e.g., LIME/SHAP incorrectly ranking non-selected tokens as most important)
4. Quantitative analysis across multiple aspects (appearance, aroma, palate)

The claim accurately reflects what was demonstrated without overstating the findings. The reviewer comments don't directly challenge this specific claim about showing error rates.

<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:

For example, our findings show that, in certain cases, the explainers predict the most relevant token to be among the tokens with zero contribution.

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

This intermediate hard selection facilitates the existence of tokens that do not have any contribution to the final prediction. While Lei et al. (2016) aimed for \(\mathcal{S}_{x}\) to be the sufficient rationals for each prediction, the model might have learned an internal (emergent) communication protocol (Foerster et al., 2016) that encodes information from the non-selected via the selected tokens, which we call a _handshake_.

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 \(0.5\).

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

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.

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.

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:

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.

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.

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.

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.

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.

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

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

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

Output:
After reviewing the claim and evidence, the evidence strongly supports this claim through concrete data. Table 1 shows that explainers like LIME and L2X predict zero-contribution tokens as most relevant up to 14.79% and 12.95% of the time respectively for the aroma aspect. The paper clearly demonstrates this through multiple metrics and examples, particularly in Figure 5 where tokens like "laces" and "mouthfel" (which have zero contribution) are ranked highly by explainers. The claim accurately reflects these findings without exaggeration, making it well-stated.

<score>1</score>