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:

While both shed light on the overall behavior of a model, we show that, when it comes to explaining the prediction on a single input in isolation, i.e., _instance-wise explanations_, the two perspectives lead to fundamentally different 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:The most common instance-wise explanatory methods are _feature-based_, i.e., they explain a prediction in terms of the input unit-features (e.g., tokens for text and super-pixels for images). Among the feature-based explainers, there are two major types of explanations: (i) feature-additive: provide signed weights for each input feature, proportional to the contributions of the features to the model's prediction (Ribeiro et al., 2016; Lundberg and Lee, 2017; Shrikumar et al., 2017; Arras et al., 2017), and (ii) feature-selective: provide a (potentially ranked) subset of features responsible for the prediction (Chen et al., 2018; Ribeiro et al., 2018; Carter et al., 2018).

Other types of explanations are (iii) example-based (Koh and Liang, 2017): identify the most relevant instances in the training set that influenced the model's prediction on the current input, and (iv) human-level explanations (Camburu et al., 2018; Park et al., 2018; Kim et al., 2018; Bekele et al., 2018): explanations that are similar to what humans provide in real-world, both in terms of arguments (human-biases) and form (full-sentence natural language). In this work, we focus on verifying feature-based explainers, since they represent the majority of current works.

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.

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.

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.

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). The generator is a bidirectional network that takes as input a piece of text \(x\) and, for each of its tokens, outputs the parameter of a Bernoulli distribution. 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 \(\mathcal{S}_{x}\).

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

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 \(0.5\).

There might be tokens (e.g., _the_ or \(a\) at the ends of the selection sequence(s)) that might have been selected in the original instance \(x\) and that become non-selected in the instance formed by \(\mathcal{S}_{x}\) without significantly changing the actual 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 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.

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.

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

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.

In this work, we first shed light on an important distinction between two widely used perspectives of explanations.

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.

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

Input: Figure 2: Example of handshake.

Input: 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 appears to be well-stated based on several key points:

1. The paper clearly demonstrates through examples (Figure 1) how feature-additive and feature-selective explanations lead to different interpretations for the same input.

2. The evidence shows concrete mathematical formulations and examples illustrating how the two perspectives fundamentally differ in their approach - feature-additive aims for neighborhood-based average explanations while feature-selective focuses on pointwise important features.

3. The review comments, while critical of other aspects, do not dispute this particular claim about the fundamental differences between the two perspectives.

4. The examples provided in Figures 1-6 consistently show how different explainers following these perspectives generate distinct explanations for the same inputs.

The claim accurately reflects what is demonstrated in the evidence without exaggeration.

<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:

While both shed light on the overall behavior of a model, we show that, when it comes to explaining the prediction on a single input in isolation, i.e., _instance-wise explanations_, the two perspectives lead to fundamentally different 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:The most common instance-wise explanatory methods are _feature-based_, i.e., they explain a prediction in terms of the input unit-features (e.g., tokens for text and super-pixels for images). Among the feature-based explainers, there are two major types of explanations: (i) feature-additive: provide signed weights for each input feature, proportional to the contributions of the features to the model's prediction (Ribeiro et al., 2016; Lundberg and Lee, 2017; Shrikumar et al., 2017; Arras et al., 2017), and (ii) feature-selective: provide a (potentially ranked) subset of features responsible for the prediction (Chen et al., 2018; Ribeiro et al., 2018; Carter et al., 2018).

Other types of explanations are (iii) example-based (Koh and Liang, 2017): identify the most relevant instances in the training set that influenced the model's prediction on the current input, and (iv) human-level explanations (Camburu et al., 2018; Park et al., 2018; Kim et al., 2018; Bekele et al., 2018): explanations that are similar to what humans provide in real-world, both in terms of arguments (human-biases) and form (full-sentence natural language). In this work, we focus on verifying feature-based explainers, since they represent the majority of current works.

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.

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.

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.

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). The generator is a bidirectional network that takes as input a piece of text \(x\) and, for each of its tokens, outputs the parameter of a Bernoulli distribution. 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 \(\mathcal{S}_{x}\).

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

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 \(0.5\).

There might be tokens (e.g., _the_ or \(a\) at the ends of the selection sequence(s)) that might have been selected in the original instance \(x\) and that become non-selected in the instance formed by \(\mathcal{S}_{x}\) without significantly changing the actual 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 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.

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.

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

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.

In this work, we first shed light on an important distinction between two widely used perspectives of explanations.

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.

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

Input: Figure 2: Example of handshake.

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

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

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

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

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

Output:
After reviewing the evidence, I find that the claim about the fundamental differences between feature-additive and feature-selective explanations is well-supported. The evidence provides clear examples (particularly in Figure 1) demonstrating how these two perspectives can lead to different explanations for the same instance. The paper shows concrete cases where feature-additive methods provide averaged contributions across neighborhoods while feature-selective methods focus on specific features used for individual predictions. The distinction is illustrated through both theoretical frameworks and practical examples, without overstating the differences.

<score>1</score>

Justification:
- The claim is well-calibrated and accurately reflects the evidence without exaggeration
- The evidence demonstrates the differences through:
1. Mathematical formalization of both perspectives
2. Clear examples showing divergent explanations (Figure 1 with "nice" vs "good" rankings)
3. Supporting experimental results showing how the two approaches can give different feature importance rankings
4. Acknowledgment of both perspectives' validity while highlighting their distinct purposes

The reviewer's comments don't challenge this specific claim about the fundamental differences between the two perspectives.


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:

First, we show that two prevalent perspectives on explanations--_feature-additivity_ and _feature-selection_--lead to fundamentally different instance-wise 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).

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.

To better understand the differences between the two perspectives, in Figure 1, we provide the instance-wise explanations that each perspective aims to provide for a hypothetical sentiment analysis regression model, where \(0\) is the most negative and \(1\) the most positive score.

In our examples in Figure 1, we clearly see the differences between the two perspectives. For the instance \(x_{1}\), the feature-additive explanation tells us that "nice" was the most relevant feature, with a weight of \(0.4\), but also that "good" had a significant contribution of \(0.3\). While for this instance alone, our model relied only on "nice" to provide a positive score of \(0.7\), it is also true that, if "nice" was not present, the model would have relied on "good" to provide a score of \(0.6\). 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.

The generator is a bidirectional network that takes as input a piece of text \(x\) and, for each of its tokens, outputs the parameter of a Bernoulli distribution. 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 \(\mathcal{S}_{x}\).

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

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

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

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.

In this work, we first shed light on an important distinction between two widely used perspectives of explanations.

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

Input: Figure 2: Example of handshake.

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

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

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

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

Output:
I'll evaluate the claim based on the provided evidence.

Summary: The evidence strongly supports the claim through clear theoretical explanations and concrete examples. The paper demonstrates fundamental differences between feature-additivity and feature-selection through:
1. Mathematical formulations showing distinct approaches
2. Illustrative examples in Figure 1 that clearly show differing explanations for the same instances
3. Real-world examples (Figures 4-6) highlighting how these perspectives lead to different interpretations
4. Discussion of trade-offs and use cases for each perspective

The claim is well-stated and accurately reflects what the evidence demonstrates without exaggeration. The authors provide comprehensive theoretical and empirical support for the distinction between these two perspectives.

<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:

First, we show that two prevalent perspectives on explanations--_feature-additivity_ and _feature-selection_--lead to fundamentally different instance-wise 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).

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.

To better understand the differences between the two perspectives, in Figure 1, we provide the instance-wise explanations that each perspective aims to provide for a hypothetical sentiment analysis regression model, where \(0\) is the most negative and \(1\) the most positive score.

In our examples in Figure 1, we clearly see the differences between the two perspectives. For the instance \(x_{1}\), the feature-additive explanation tells us that "nice" was the most relevant feature, with a weight of \(0.4\), but also that "good" had a significant contribution of \(0.3\). While for this instance alone, our model relied only on "nice" to provide a positive score of \(0.7\), it is also true that, if "nice" was not present, the model would have relied on "good" to provide a score of \(0.6\). 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.

The generator is a bidirectional network that takes as input a piece of text \(x\) and, for each of its tokens, outputs the parameter of a Bernoulli distribution. 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 \(\mathcal{S}_{x}\).

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

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

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

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.

In this work, we first shed light on an important distinction between two widely used perspectives of explanations.

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

Input: Figure 2: Example of handshake.

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

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

Input: Figure 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 that feature-additivity and feature-selection lead to fundamentally different instance-wise explanations is well-supported by the evidence provided. The paper clearly demonstrates these differences through:

1. Concrete examples in Figure 1 showing how the two approaches produce different explanations for the same instances
2. Detailed mathematical formulations explaining the distinct objectives of each perspective
3. Multiple examples showing how feature-additivity provides average explanations over neighborhoods while feature-selection focuses on pointwise features
4. Real-world examples (Figures 4-6) illustrating the practical differences in explanations

The claim is measured and accurately reflects what the evidence demonstrates, without overstating the findings.

<score>1</score>

Justification:
- The claim uses appropriate language ("fundamentally different") that is backed by clear theoretical and empirical evidence
- The examples effectively show distinct rankings and selected features between the two approaches
- The paper acknowledges the merits of both perspectives rather than claiming one is superior
- The review comments don't challenge this specific claim about the differences between the two approaches
- The evidence provides multiple forms of support: theoretical framework, toy examples, and real-world applications


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:

First, we show that two prevalent perspectives on explanations--_feature-additivity_ and _feature-selection_--lead to fundamentally different instance-wise 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).

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.

To better understand the differences between the two perspectives, in Figure 1, we provide the instance-wise explanations that each perspective aims to provide for a hypothetical sentiment analysis regression model, where \(0\) is the most negative and \(1\) the most positive score.

In our examples in Figure 1, we clearly see the differences between the two perspectives. For the instance \(x_{1}\), the feature-additive explanation tells us that "nice" was the most relevant feature, with a weight of \(0.4\), but also that "good" had a significant contribution of \(0.3\). While for this instance alone, our model relied only on "nice" to provide a positive score of \(0.7\), it is also true that, if "nice" was not present, the model would have relied on "good" to provide a score of \(0.6\). 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.

The generator is a bidirectional network that takes as input a piece of text \(x\) and, for each of its tokens, outputs the parameter of a Bernoulli distribution. 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 \(\mathcal{S}_{x}\).

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

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

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

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.

In this work, we first shed light on an important distinction between two widely used perspectives of explanations.

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

Input: Figure 2: Example of handshake.

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

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

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

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

Output:
I'll evaluate the claim that "two prevalent perspectives on explanations--feature-additivity and feature-selection--lead to fundamentally different instance-wise explanations."

This claim is well-supported by both theoretical explanations and concrete examples. The evidence shows clear mathematical and conceptual differences between the two approaches, with feature-additivity providing weighted contributions that sum to approximate the prediction, while feature-selection identifies sufficient subsets of features. Figure 1 demonstrates these differences through practical examples where the two perspectives provide distinctly different explanations for the same instances. The review comments, while critical of other aspects, don't dispute this fundamental distinction.

<score>1</score>

The claim is well-stated (1) because:
1. The evidence thoroughly demonstrates the mathematical differences between the approaches (equations and formal definitions provided)
2. Clear examples in Figure 1 show concrete cases where the perspectives give different explanations
3. The paper explicitly shows how feature-additivity provides average explanations over neighborhoods while feature-selection focuses on instance-specific features
4. The distinction is fundamental, not superficial, as shown by cases where they produce different feature rankings
5. The claim doesn't overstate the findings but accurately reflects the demonstrated differences


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:

First, we show that two prevalent perspectives on explanations--_feature-additivity_ and _feature-selection_--lead to fundamentally different instance-wise 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).

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.

To better understand the differences between the two perspectives, in Figure 1, we provide the instance-wise explanations that each perspective aims to provide for a hypothetical sentiment analysis regression model, where \(0\) is the most negative and \(1\) the most positive score.

In our examples in Figure 1, we clearly see the differences between the two perspectives. For the instance \(x_{1}\), the feature-additive explanation tells us that "nice" was the most relevant feature, with a weight of \(0.4\), but also that "good" had a significant contribution of \(0.3\). While for this instance alone, our model relied only on "nice" to provide a positive score of \(0.7\), it is also true that, if "nice" was not present, the model would have relied on "good" to provide a score of \(0.6\). 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.

The generator is a bidirectional network that takes as input a piece of text \(x\) and, for each of its tokens, outputs the parameter of a Bernoulli distribution. 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 \(\mathcal{S}_{x}\).

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

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

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

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.

In this work, we first shed light on an important distinction between two widely used perspectives of explanations.

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

Input: Figure 2: Example of handshake.

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

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

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

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

Output:
After reviewing the evidence, I find that the claim "two prevalent perspectives on explanations--feature-additivity and feature-selection--lead to fundamentally different instance-wise explanations" is well-stated and supported by the evidence. The paper clearly demonstrates these differences through:
1. Theoretical explanations of both perspectives
2. Concrete examples in Figure 1 showing how the two approaches give different explanations for the same instances
3. Mathematical formulations distinguishing the approaches
4. Visual examples showing how feature-additive methods (LIME/SHAP) differ from feature-selection approaches (L2X)

The differences are substantiated with clear evidence and not overstated. The limitations and tradeoffs of each approach are also acknowledged.

<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 address the above issue by proposing a framework capable of generating evaluation tests for the explanatory methods under the feature-selection perspective.

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

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

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.

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

_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.

In the rest of the paper, we propose a verification framework for the _feature-selection_ perspective of instance-wise explanations. Our proposed verification framework leverages the architecture of the RCNN model introduced by Lei et al. (2016). We further prune the original dataset on which the RCNN had been trained to ensure that, for each datapoint \(x\), there exists a set of tokens that have zero contribution (irrelevant features) and a set of tokens that have a significant contribution (clearly relevant features) to RCNN's prediction on \(x\). We further introduce a set of metrics that measure how explainers fail to rank the irrelevant tokens lower than the clearly revelant ones.

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

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

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 \(\tau\).

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.

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: 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 the evidence provided, I have carefully evaluated the claim "We address the above issue by proposing a framework capable of generating evaluation tests for the explanatory methods under the feature-selection perspective."

The evidence strongly supports this claim. The authors present a detailed framework that:
1. Focuses specifically on feature-based explainers from the feature-selection perspective
2. Provides clear evaluation metrics (%_first, %_mismrk, avg_mismrk)
3. Demonstrates concrete examples with visualizations (Figures 4-6)
4. Offers guarantees about the behavior of the framework with non-trivial neural networks
5. Shows implementation on real-world NLP tasks

The claim is well-stated and accurately reflects what the framework delivers without exaggeration.

<score>1</score>