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 introduce a verification framework for explanatory methods under the feature-selection perspective.

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.

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

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

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.

Thus, we see that the feature-additive perspective aims to provide an _average explanation_ of the model on a _neighborhood_ of the instance, while the feature-selective perspective aims to tell us the pointwise features used by the model on the instance _in isolation_, such as "nice" for instance \(x_{1}\). An even more pronounced difference between the two perspectives is visible on instance \(x_{2}\), where the ranking of features is now different. The feature-selective explanation ranks "good" and "nice" as the two most important features, while on the instance \(x_{2}\) in isolation, the model relied on the tokens "very" and "good", that the feature-selection perspective would aim to provide. Therefore, we see that, while both perspectives of explanations give insights into the model's behavior, one perspective might be preferred over the other in different real-world use-cases. In the rest of the paper, we propose a verification framework for the _feature-selection_ perspective of instance-wise explanations.

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. 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 \(\tau\).

Thus, we have further partitioned \(\mathcal{S}_{x}\) into \(\mathcal{S}_{x}=\mathcal{S}\mathcal{R}_{x}\cup\mathcal{S}\mathcal{D}\mathcal{ K}_{x}\), where \(\mathcal{S}\mathcal{R}_{x}\) are the clearly relevant tokens, and \(\mathcal{S}\mathcal{D}\mathcal{K}_{x}\) are the rest of the selected tokens for which we do not know if they are relevant or noise (SDK stands for "selected don't know").

First, we note that our procedure does not provide an explainer in itself, since we do not give an actual ranking, nor any contribution weights, and it is possible for some of the tokens in \(\mathcal{S}\mathcal{D}\mathcal{K}_{x}\) to be even more important than tokens in \(\mathcal{S}\mathcal{R}_{x}\). However, we guarantee the following two properties: **P1: ** All tokens in \(\mathcal{N}_{x}\) have to be ranked lower than any token in \(\mathcal{S}\mathcal{R}_{x}\). **P2:** The first most important token has to be in \(\mathcal{S}_{x}\). We evaluate explainers that provide a ranking over the features. We denote by \(r_{1}(x),r_{2}(x),\ldots,r_{n}(x)\) the ranking (in decreasing order of importance) given by an explainer on the \(n=|x|\) features in the instance \(x\). Under our two properties above, we define the following error metrics:* **Percentage of instances for which the most important token provided by the explainer is among the non-selected tokens:** \[\text{\textasci{1.5}{\%}}_{\text{first}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum \limits_{x\in\mathcal{D}_{a}}\mathbb{1}_{\{r_{1}(x)\in\mathcal{N}_{x}\}}\,,\] (4) where \(\mathbb{1}\) is the indicator function.

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

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

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

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

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

\[\text{RCNN}(x-\mathcal{N}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{8}\] Since \(x-\mathcal{N}_{x}=\mathcal{S}_{x}\), Equation 8 rewrites as: \[\text{RCNN}(\mathcal{S}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{9}\]

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

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

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

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

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

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

Output:
After reviewing the evidence, the claim "We introduce a verification framework for explanatory methods under the feature-selection perspective" appears to be well-stated. The paper clearly presents a structured verification framework with:
- A clear methodology using RCNN to identify relevant/irrelevant tokens
- Mathematical formalization of the framework's properties and guarantees
- Defined metrics for evaluation
- Demonstrated application on real examples
- Transparent discussion of limitations noted in reviewer comments

While the reviewer raises valid concerns about RCNN reliability and generalizability, these don't invalidate the basic claim of introducing a verification framework. The claim avoids overstating capabilities and simply declares the introduction of the framework itself.

<score>1</score>

Justification:
1. The evidence thoroughly documents the framework's methodology, metrics, and implementation
2. The claim is modest and accurately reflects what was accomplished, without making grand assertions about effectiveness
3. The reviewer's concerns relate to framework limitations rather than the veracity of the introductory claim itself
4. The paper acknowledges limitations and uncertainty, demonstrating scientific rigor
5. Direct evidence is provided through detailed equations, figures and examples demonstrating the framework in practice


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 introduce a verification framework for explanatory methods under the feature-selection perspective.

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.

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

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

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.

Thus, we see that the feature-additive perspective aims to provide an _average explanation_ of the model on a _neighborhood_ of the instance, while the feature-selective perspective aims to tell us the pointwise features used by the model on the instance _in isolation_, such as "nice" for instance \(x_{1}\). An even more pronounced difference between the two perspectives is visible on instance \(x_{2}\), where the ranking of features is now different. The feature-selective explanation ranks "good" and "nice" as the two most important features, while on the instance \(x_{2}\) in isolation, the model relied on the tokens "very" and "good", that the feature-selection perspective would aim to provide. Therefore, we see that, while both perspectives of explanations give insights into the model's behavior, one perspective might be preferred over the other in different real-world use-cases. In the rest of the paper, we propose a verification framework for the _feature-selection_ perspective of instance-wise explanations.

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. 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 \(\tau\).

Thus, we have further partitioned \(\mathcal{S}_{x}\) into \(\mathcal{S}_{x}=\mathcal{S}\mathcal{R}_{x}\cup\mathcal{S}\mathcal{D}\mathcal{ K}_{x}\), where \(\mathcal{S}\mathcal{R}_{x}\) are the clearly relevant tokens, and \(\mathcal{S}\mathcal{D}\mathcal{K}_{x}\) are the rest of the selected tokens for which we do not know if they are relevant or noise (SDK stands for "selected don't know").

First, we note that our procedure does not provide an explainer in itself, since we do not give an actual ranking, nor any contribution weights, and it is possible for some of the tokens in \(\mathcal{S}\mathcal{D}\mathcal{K}_{x}\) to be even more important than tokens in \(\mathcal{S}\mathcal{R}_{x}\). However, we guarantee the following two properties: **P1: ** All tokens in \(\mathcal{N}_{x}\) have to be ranked lower than any token in \(\mathcal{S}\mathcal{R}_{x}\). **P2:** The first most important token has to be in \(\mathcal{S}_{x}\). We evaluate explainers that provide a ranking over the features. We denote by \(r_{1}(x),r_{2}(x),\ldots,r_{n}(x)\) the ranking (in decreasing order of importance) given by an explainer on the \(n=|x|\) features in the instance \(x\). Under our two properties above, we define the following error metrics:* **Percentage of instances for which the most important token provided by the explainer is among the non-selected tokens:** \[\text{\textasci{1.5}{\%}}_{\text{first}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum \limits_{x\in\mathcal{D}_{a}}\mathbb{1}_{\{r_{1}(x)\in\mathcal{N}_{x}\}}\,,\] (4) where \(\mathbb{1}\) is the indicator function.

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

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

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

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

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

\[\text{RCNN}(x-\mathcal{N}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{8}\] Since \(x-\mathcal{N}_{x}=\mathcal{S}_{x}\), Equation 8 rewrites as: \[\text{RCNN}(\mathcal{S}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{9}\]

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

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

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

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

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

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

Output:
After reviewing the claim and evidence, I find it to be moderately overstated. While the paper does present a framework for verifying explanatory methods from a feature-selection perspective, several key limitations are evident:

1. The framework is heavily constrained by requiring labeled training data for the RCNN
2. The RCNN itself is a black box model that needs explaining
3. The review points out that the evaluation depends critically on the RCNN's ability to correctly identify relevant features
4. The experiments are limited in scope (only 3 explainer models on one task)
5. The framework still relies on human evaluation in Section 5, contradicting claims of being fully automatic

The evidence shows the authors developed an evaluation approach, but oversell its capabilities and comprehensiveness.

<score>4</score>


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

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

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

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

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

The claim to be assessed is:

We introduce a verification framework for explanatory methods under the feature-selection perspective.

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.

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

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

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.

Thus, we see that the feature-additive perspective aims to provide an _average explanation_ of the model on a _neighborhood_ of the instance, while the feature-selective perspective aims to tell us the pointwise features used by the model on the instance _in isolation_, such as "nice" for instance \(x_{1}\). An even more pronounced difference between the two perspectives is visible on instance \(x_{2}\), where the ranking of features is now different. The feature-selective explanation ranks "good" and "nice" as the two most important features, while on the instance \(x_{2}\) in isolation, the model relied on the tokens "very" and "good", that the feature-selection perspective would aim to provide. Therefore, we see that, while both perspectives of explanations give insights into the model's behavior, one perspective might be preferred over the other in different real-world use-cases. In the rest of the paper, we propose a verification framework for the _feature-selection_ perspective of instance-wise explanations.

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. 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 \(\tau\).

Thus, we have further partitioned \(\mathcal{S}_{x}\) into \(\mathcal{S}_{x}=\mathcal{S}\mathcal{R}_{x}\cup\mathcal{S}\mathcal{D}\mathcal{ K}_{x}\), where \(\mathcal{S}\mathcal{R}_{x}\) are the clearly relevant tokens, and \(\mathcal{S}\mathcal{D}\mathcal{K}_{x}\) are the rest of the selected tokens for which we do not know if they are relevant or noise (SDK stands for "selected don't know").

First, we note that our procedure does not provide an explainer in itself, since we do not give an actual ranking, nor any contribution weights, and it is possible for some of the tokens in \(\mathcal{S}\mathcal{D}\mathcal{K}_{x}\) to be even more important than tokens in \(\mathcal{S}\mathcal{R}_{x}\). However, we guarantee the following two properties: **P1: ** All tokens in \(\mathcal{N}_{x}\) have to be ranked lower than any token in \(\mathcal{S}\mathcal{R}_{x}\). **P2:** The first most important token has to be in \(\mathcal{S}_{x}\). We evaluate explainers that provide a ranking over the features. We denote by \(r_{1}(x),r_{2}(x),\ldots,r_{n}(x)\) the ranking (in decreasing order of importance) given by an explainer on the \(n=|x|\) features in the instance \(x\). Under our two properties above, we define the following error metrics:* **Percentage of instances for which the most important token provided by the explainer is among the non-selected tokens:** \[\text{\textasci{1.5}{\%}}_{\text{first}}=\tfrac{1}{|\mathcal{D}_{a}|}\sum \limits_{x\in\mathcal{D}_{a}}\mathbb{1}_{\{r_{1}(x)\in\mathcal{N}_{x}\}}\,,\] (4) where \(\mathbb{1}\) is the indicator function.

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

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

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

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

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

\[\text{RCNN}(x-\mathcal{N}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{8}\] Since \(x-\mathcal{N}_{x}=\mathcal{S}_{x}\), Equation 8 rewrites as: \[\text{RCNN}(\mathcal{S}_{x})=\text{RCNN}(x)\implies\text{no handshake in }x\,. \tag{9}\]

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

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

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

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

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

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

Output:
Based on my review, the claim "We introduce a verification framework for explanatory methods under the feature-selection perspective" is well-stated and accurately reflects the evidence provided. The paper presents:

1. Clear distinction between feature-additive and feature-selection perspectives
2. Detailed verification framework with specific metrics (Equations 4-6)
3. Concrete evaluation examples shown in figures testing different explainers (LIME, SHAP, L2X)
4. Mathematical formalization of the framework's guarantees (Equations 8-9)
5. Demonstration of the framework's ability to identify explainer failures

The evidence thoroughly supports the claim's scope and the review comment, while critical, doesn't dispute the fundamental contribution of introducing this verification framework. The claim doesn't overstate the work's significance 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 is based on a non-trivial neural network architecture trained on a real-world task, and for which we are able to provide guarantees on its inner workings.

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.

Our proposed verification framework leverages the architecture of the RCNN model introduced by Lei et al. (2016).

The RCNN (Lei et al., 2016) consists of two modules: a generator followed by an encoder, both instantiated with recurrent convolutional neural networks (Lei et al., 2015).

According to this distribution, the RCNN selects a subset of tokens from \(x\), called \(\mathcal{S}_{x}=\text{generator}(x)\), and passes it to the encoder, which makes the final prediction solely as a function of \(\mathcal{S}_{x}\).

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_.

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 \(0.5\).

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

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 \(\mathcal{S}_{x}\).

In this work, we instantiate our framework on the RCNN model trained on the BeerAdvocate corpus,3 on which the RCNN was initially evaluated (Lei et al., 2016). BeerAdvocate consists of a total of \(\approx.100\)K human-generated multi-aspect beer reviews, where the three considered aspects are appearance, aroma, and palate. The reviews are accompanied with fractional ratings originally between 0 and 5 for each aspect independently. The RCNN is a regression model with the goal to predict the rating, rescaled between 0 and 1 for simplicity. Three separate RCNNs are trained, one for each aspect independently, with the same default settings.4Footnote 3: [http://people.csail.mit.edu/taolei/beer/](http://people.csail.mit.edu/taolei/beer/)

With the above procedure, we gathered three datasets \(\mathcal{D}_{a}\), one for each aspect \(a\). 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 \(\{0,\,0.1,\,0.2,\,\,\dots\,,\,1\}\). Therefore, a change in prediction of \(0.1\) is to be considered clearly significant for this task.

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

Input: Figure 2: Example of handshake.

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

Output:
After reviewing the evidence, I assess this claim as slightly overstated. While the framework does use a non-trivial neural network (RCNN) trained on a real-world task (BeerAdvocate reviews), and provides some guarantees about token relevance (P1 and P2 properties), the scope of these guarantees is relatively limited. The guarantees mainly focus on token selection patterns and relative importance rankings, rather than comprehensive "inner workings" as broadly implied in the claim. The evidence shows they can verify specific behaviors around token selection and contribution, but this represents only a subset of the model's inner workings.

<score>3</score>

Justification:
1. The claim accurately states they use a non-trivial neural network (RCNN) on real data (BeerAdvocate corpus)
2. However, the "guarantees on inner workings" part is somewhat overstated because:
   - The guarantees are limited to specific properties around token selection (P1 and P2)
   - The paper acknowledges limitations like "our procedure does not provide an explainer in itself"
   - The verification mainly focuses on token contribution patterns rather than comprehensive model behavior
3. The evidence shows successful verification of specific behaviors, but the broad phrase "inner workings" implies more comprehensive understanding than demonstrated
4. Direct quote supporting limitation: "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"


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 is based on a non-trivial neural network architecture trained on a real-world task, and for which we are able to provide guarantees on its inner workings.

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.

Our proposed verification framework leverages the architecture of the RCNN model introduced by Lei et al. (2016).

The RCNN (Lei et al., 2016) consists of two modules: a generator followed by an encoder, both instantiated with recurrent convolutional neural networks (Lei et al., 2015).

According to this distribution, the RCNN selects a subset of tokens from \(x\), called \(\mathcal{S}_{x}=\text{generator}(x)\), and passes it to the encoder, which makes the final prediction solely as a function of \(\mathcal{S}_{x}\).

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_.

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 \(0.5\).

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

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 \(\mathcal{S}_{x}\).

In this work, we instantiate our framework on the RCNN model trained on the BeerAdvocate corpus,3 on which the RCNN was initially evaluated (Lei et al., 2016). BeerAdvocate consists of a total of \(\approx.100\)K human-generated multi-aspect beer reviews, where the three considered aspects are appearance, aroma, and palate. The reviews are accompanied with fractional ratings originally between 0 and 5 for each aspect independently. The RCNN is a regression model with the goal to predict the rating, rescaled between 0 and 1 for simplicity. Three separate RCNNs are trained, one for each aspect independently, with the same default settings.4Footnote 3: [http://people.csail.mit.edu/taolei/beer/](http://people.csail.mit.edu/taolei/beer/)

With the above procedure, we gathered three datasets \(\mathcal{D}_{a}\), one for each aspect \(a\). 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 \(\{0,\,0.1,\,0.2,\,\,\dots\,,\,1\}\). Therefore, a change in prediction of \(0.1\) is to be considered clearly significant for this task.

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

Input: Figure 2: Example of handshake.

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

Output:
Based on the evidence provided, I'll evaluate the claim "Our framework is based on a non-trivial neural network architecture trained on a real-world task, and for which we are able to provide guarantees on its inner workings."

The evidence shows that while the framework does use a non-trivial RCNN architecture trained on real beer reviews (a real-world task), the reviewer raises valid concerns about the reliability and generalizability of the guarantees. Key issues include:

1. High standard deviations in experiments
2. Dependency on RCNN training data selection
3. Limited verification scope (only tested on beer reviews)
4. Potential changes in results with different data pruning

While the framework does provide some guarantees (P1 and P2 properties), these appear more limited than the broad claim suggests. The claim somewhat overstates the robustness and generalizability of the guarantees.

<score>3</score>


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

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

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

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

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

The claim to be assessed is:

Our framework is based on a non-trivial neural network architecture trained on a real-world task, and for which we are able to provide guarantees on its inner workings.

The review comment to be evaluated is:

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

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

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

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

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

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

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

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

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

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

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

The evidence to be evaluated is:In contrast to the above, our evaluation is fully automatic, the target model is a non-trivial neural network trained on a real-world task and for which we provide guarantees on its inner-workings.

Our proposed verification framework leverages the architecture of the RCNN model introduced by Lei et al. (2016).

The RCNN (Lei et al., 2016) consists of two modules: a generator followed by an encoder, both instantiated with recurrent convolutional neural networks (Lei et al., 2015).

According to this distribution, the RCNN selects a subset of tokens from \(x\), called \(\mathcal{S}_{x}=\text{generator}(x)\), and passes it to the encoder, which makes the final prediction solely as a function of \(\mathcal{S}_{x}\).

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_.

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 \(0.5\).

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

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 \(\mathcal{S}_{x}\).

In this work, we instantiate our framework on the RCNN model trained on the BeerAdvocate corpus,3 on which the RCNN was initially evaluated (Lei et al., 2016). BeerAdvocate consists of a total of \(\approx.100\)K human-generated multi-aspect beer reviews, where the three considered aspects are appearance, aroma, and palate. The reviews are accompanied with fractional ratings originally between 0 and 5 for each aspect independently. The RCNN is a regression model with the goal to predict the rating, rescaled between 0 and 1 for simplicity. Three separate RCNNs are trained, one for each aspect independently, with the same default settings.4Footnote 3: [http://people.csail.mit.edu/taolei/beer/](http://people.csail.mit.edu/taolei/beer/)

With the above procedure, we gathered three datasets \(\mathcal{D}_{a}\), one for each aspect \(a\). 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 \(\{0,\,0.1,\,0.2,\,\,\dots\,,\,1\}\). Therefore, a change in prediction of \(0.1\) is to be considered clearly significant for this task.

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

Input: Figure 2: Example of handshake.

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

Output:
Based on my review of the claim and evidence, this appears to be a moderately overstated claim. While the framework does use an RCNN architecture trained on a real-world beer review dataset (showing it's non-trivial), several limitations exist in the guarantees and scope:

1. The guarantees are limited to specific properties about token rankings (P1 and P2)
2. The framework was only tested on one specific NLP task (beer reviews)
3. The review raises valid concerns about RCNN's black-box nature and potential biases
4. The evidence shows the framework requires labeled training data, limiting scalability
5. The term "guarantees on its inner workings" overstates what's actually proven - they mainly show properties about token selection rather than comprehensive inner workings

<score>4</score>


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

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

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

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

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

The claim to be assessed is:

Our framework is based on a non-trivial neural network architecture trained on a real-world task, and for which we are able to provide guarantees on its inner workings.

The review comment to be evaluated is:

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

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

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

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

The evidence to be evaluated is:In contrast to the above, our evaluation is fully automatic, the target model is a non-trivial neural network trained on a real-world task and for which we provide guarantees on its inner-workings.

Our proposed verification framework leverages the architecture of the RCNN model introduced by Lei et al. (2016).

The RCNN (Lei et al., 2016) consists of two modules: a generator followed by an encoder, both instantiated with recurrent convolutional neural networks (Lei et al., 2015).

According to this distribution, the RCNN selects a subset of tokens from \(x\), called \(\mathcal{S}_{x}=\text{generator}(x)\), and passes it to the encoder, which makes the final prediction solely as a function of \(\mathcal{S}_{x}\).

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_.

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 \(0.5\).

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

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 \(\mathcal{S}_{x}\).

In this work, we instantiate our framework on the RCNN model trained on the BeerAdvocate corpus,3 on which the RCNN was initially evaluated (Lei et al., 2016). BeerAdvocate consists of a total of \(\approx.100\)K human-generated multi-aspect beer reviews, where the three considered aspects are appearance, aroma, and palate. The reviews are accompanied with fractional ratings originally between 0 and 5 for each aspect independently. The RCNN is a regression model with the goal to predict the rating, rescaled between 0 and 1 for simplicity. Three separate RCNNs are trained, one for each aspect independently, with the same default settings.4Footnote 3: [http://people.csail.mit.edu/taolei/beer/](http://people.csail.mit.edu/taolei/beer/)

With the above procedure, we gathered three datasets \(\mathcal{D}_{a}\), one for each aspect \(a\). 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 \(\{0,\,0.1,\,0.2,\,\,\dots\,,\,1\}\). Therefore, a change in prediction of \(0.1\) is to be considered clearly significant for this task.

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

Input: Figure 2: Example of handshake.

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

Output:
Based on my review, this claim warrants a moderate overstatement score. While the evidence shows they did work with a non-trivial neural network (RCNN) on a real-world task (BeerAdvocate reviews), the "guarantees on inner workings" aspect is somewhat overstated. The evidence shows they primarily guarantee two specific properties (P1 and P2) about token rankings, rather than broad guarantees about the model's inner workings. The reviewer also raises valid concerns about using an opaque model (RCNN) to evaluate explanations, calling it "cyclical."

<score>3</score>

Justification:
1. Evidence supports "non-trivial neural network" and "real-world task" parts:
- RCNN architecture with generator and encoder modules
- BeerAdvocate corpus with ~100K reviews
- Three aspects (appearance, aroma, palate)

2. "Guarantees on inner workings" is overstated because:
- Guarantees are limited to specific token ranking properties (P1, P2)
- Reviewer points out cyclical nature of using opaque model for verification
- Evidence shows potential "handshake" issues where internal communication protocols may exist
- Methodology is focused on specific aspects rather than comprehensive inner workings

The claim would be better stated if it specified the limited nature of the guarantees provided rather than implying broad guarantees about the model's inner workings.