From: ICLR.cc/2023/Conference/Paper3309/Authors
title: Submission Withdrawn by the Authors

From: ICLR.cc/2023/Conference/Paper3309/Authors
title: Friendly reminder to respond to author response 
comment: Dear Reviewer 1Npi,

Thanks again for your review. We have updated the paper and provided detailed responses (please see the General Response and the specific response to your comments). Please kindly let us know if our rebuttal has addressed your concerns and questions. We would appreciate your feedback. Thank you!

From: ICLR.cc/2023/Conference/Paper3309/Authors
title: Further Response to Reviewer DFWB
comment: Thank you for your reply and for increasing the score!

From: ICLR.cc/2023/Conference/Paper3309/Reviewer_DFWB
title: Solved most of my concern.
comment: Thank you for the detailed reply. My main concern of information leakage and the use of T5 are clarified. I'm ok with moving my rating up to marginally above the acceptance threshold.

From: ICLR.cc/2023/Conference/Paper3309/Authors
title: Further Response to Reviewer 1gjq
comment: We thank the reviewer for acknowledging the value of our work and for increasing the score. Regarding the reviewer’s comments, we provide further explanations/clarifications below. If the reviewer agrees that we have addressed their concerns, we are looking forward to a score above the acceptance threshold. 

* **REV does not resemble the logprob of LAS**. Though we have explained in our previous responses, we would like to reiterate that REV is not a probabilistic interpretation of LAS. REV evaluates rationales along a new dimension—the amount of new information, which is not considered by LAS. 
* **LAS cannot be interpreted using $\mathcal{V}$-information**. LAS evaluates the direct association between a rationale and a label, without measuring the information. Thus, we do not think $\mathcal{V}$-information is suitable to interpret LAS. Additionally, interpreting the theoretical foundation of LAS is beyond the scope of our paper. 
* **The correlation between individual REV scores and human judgments is less meaningful**. REV scores are fine-grained in a continuous space, while human judgments are mapped into categorical scores. Computing the statistical correlation between continuous values and discrete values cannot provide meaningful results.

From: ICLR.cc/2023/Conference/Paper3309/Reviewer_1gjq
title: Comment
comment: **On related works**: Totally agree. My comment was just a clarification, and I also believe that the main findings would be similar if the other two metrics were employed.

**On the interpretation of REV**: The point here is that an "absolute" interpretation of REV scores depends on a well-calibrated model. Otherwise, REV scores are as interpretable as the other metrics, which automatically learn a "good" threshold. With that said, I do think that relative differences of REV scores might tell a better history.

Thanks for all clarifications and the fruitful discussion. Given the practical use of REV and the novelties cited in my previous review, I am happy to increase my score. However, I think the paper can be improved substantially by attacking the problem from a better angle, such as (but not limited to) interpreting the theoretical foundation of LAS using V-information. As it stands, REV resembles the logprob of LAS --- an estimated confidence score. For example, an interesting experiment would be to assess the correlation between REV scores and human judgments rather than the correlation between the average REV scores and the average human assessments. Overall, I think the idea presented in this paper is exciting and it has practical importance. I highly encourage the authors to fix these minor issues and polish the clarity of the experiments.

From: ICLR.cc/2023/Conference/Paper3309/Authors
title: Continued Response to Reviewer 1gjq
comment: We thank the reviewer’s response and feedback. To address the reviewer’s concerns, we provide further explanations/clarifications below. 

### On novelty
Thank you for recognizing the novelty of our research problem. To solve the research problem, we have to reformulate conditional $\mathcal{V}$-information to make it applicable for the evaluation of free-text rationales, i.e. REV score, which is the novelty of the application in this work. We also want to highlight the novelty of findings in this work (e.g., two rationales that both support a label can supply different amounts of new information), which cannot be discovered by previous accuracy-based metrics. 
We recognize the contribution of Ethayarajh et al. (2022) in applying $\mathcal{V}$-information to analyzing data difficulty, while the implementation of Ethayarajh et al. (2022) cannot be directly applied to solve our problem. Additionally, we believe other applications of $\mathcal{V}$-information for solving different problems also have their own novelty. 

### On related works
In addition to the differences we have clarified in our previous response, we would like to explain why these two papers are not directly or necessarily compared to our work:

**The framework of Pruthi et al. (2022) is not applicable for computing simulatability**. Pruthi et al. (2022) use a teacher model’s explanations via multitask learning to train a student model, and then evaluate the student model’s predictive ability in mimicking the teacher model’s predictions during inference (without providing explanations). Since no explanation is provided during the test time (please refer to Fig. 1 in Pruthi et al. (2022)), this framework is not applicable for computing simulatability (e.g., LAS), which is the difference between a proxy model’s prediction accuracy when the explanation is included into the input vs. when it is not.

**The metric in Treviso & Martins (2020) is also accuracy-based**. Treviso & Martins (2020) evaluate whether a classifier’s explanation can help a layperson model produce its prediction, basically by comparing whether the predictions from the classifier and the layperson are equal or not. This evaluation metric (“communication success rate” defined in Treviso & Martins (2020)) is accuracy-based, like LAS and RQ, while only providing binary scores (for each example). Since we have already compared with LAS and RQ, we do not see a necessity to compare with this work. 

### On the analysis of trivial protocols
We report the min, max, average, and std REV for Crowdsourced and GenericsKB below.
|    Source    | min    | max  | average | std  |
|:------------:|--------|------|---------|------|
| Crowdsourced | -0.07  | 4.78 | 0.87    | 1.15 |
| GenericsKB   | -11.98 | 3.87 | 0.02    | 1.11 |

### On the interpretation of REV
Even by looking at the sign, REV can provide more sensitive evaluations than other methods. LAS and RQ can give a zero score, making the decision ambiguous (e.g., the large portion corresponds to a zero LAS/RQ score in Fig. 4), while REV rarely gives a zero score (see the histogram of REV in Fig. 4).

The definition of LAS has been provided in Sec. 4 of Hase et al. (2020), which is the baseline metric we compare to. The loss function in Sec. 5 of Hase et al. (2020) is to optimize the explanation generation (nothing to do with the metric). Even though the form of the inner part (the difference between two log probabilities) looks similar to REV, the meaning is different. We believe there is no implication in Hase et al. (2020) that the inner part of the loss function could be interpreted as the LAS metric. 

### On comparing different metrics
For clarification, we do not use ranking when we compute the support rate. To compare with human evaluation (a binary score indicating support/not support), we only consider the sign of REV/LAS/RQ scores, where “+/-” indicates “support/not support”. The support rate of REV/LAS/RQ is computed as the proportion of examples with positive REV/LAS/RQ scores, i.e. $\frac{\text{num}(+)}{\text{num}(+)+\text{num}(-)+\text{num}(0)}$. We do not consider the examples with a zero score because it is unclear whether the rationale supports the label or not as the model proxy may predict the label based on the input only.

From: ICLR.cc/2023/Conference/Paper3309/Reviewer_1gjq
title: Response
comment: I want to thank the authors for their detailed and thorough responses. 

### On novelty

I see V-information as a way to justify the method rather than a novelty piece. For example, one can use information theory to justify probing neural nets, but probing is not novel. On the same line, I believe that _applying V-information to analyse/assess examples_ was already done by Ethayarajh et al. (2022). It is a perspective point whether these examples are input texts or side-information texts such as free-text rationales. This choice of perspective is evident in the paper since the pointwise conditional V-information $PI_{\mathcal{V}}(r \to y | b)$ is directly stated to be equal to $REV(x, y, r)$.



### On related works

Pruthi et al. (2022) propose a more general framework for assessing explanations. Although the authors do not evaluate free-text rationales, the proposed framework is still applicable for computing simulability. The same is true for the framework of Treviso & Martins (2020). These two works are linked to REV, LAS, and RQ because they also propose to assess explanations by training and querying a proxy model. 


### On the analysis of trivial protocols 

Thanks for the clarification. The table presents some exciting patterns. What are the min, max, average, and std REV for Crowdsourced and GenericsKB?
 

### On the interpretation of REV

> An intuitive way to interpret REV is by looking at the sign, where ``REV > 0" indicates additional new information that supports the label, while "REV <= 0" indicates the lack of new information or noisy information.

Isn't looking at the sign the same as making a binary decision (as done by other methods)? 

> In addition, the value of REV indicates the information quantity, which cannot be quantified by LAS or RQ.

LAS can also be interpreted with log probabilities instead of binary decisions. Take the inner part of the loss function defined in Section 5 of (Hase et al., 2020) with $\alpha=0.5, x=r, e=b$, and the result is REV with a different meaning for the baseline (hence the acknowledged novelty here).


### On comparing different metrics

I still believe that comparing the metrics by ranking is unfair. Thanks for bringing up Section 4.4; Figure 4 further reinforces my reasoning as all methods are picked at 0, and thus comparing them by ranking might give an extra advantage for REV, which distributes the points clustered at 0. How exactly was the support rate computed for LAS and RQ? Do examples that received a zero LAS/RQ score overlap with examples that received a human score between 0 and 1?


After reading the revised version and the responses regarding novelty, I do recognize there is novelty in some aspects of REV, such as replacing "empty inputs" with "vacuous rationales" (operationalization novelty) and analyzing free-text rationales instead of extractive rationales, which leads to the "Novelty of the research problem". However, as mentioned, I do not see the novelty in the application of V-information. Overall, I like the ideas presented in this paper, but I still believe some parts can be improved before publication, such as the framing of the method and the comparison to other metrics.

From: ICLR.cc/2023/Conference/Paper3309/Authors
title: Response to Reviewer DFWB
comment: We thank the reviewer's recognition of the novelty of our work in evaluating free-text rationales from the information-theoretic perspective. We are also grateful for the reviewer pointing out certain issues of clarity which we revise and clarify below.

### 1. How would REV deal with information leakage? 

REV is inherently designed to penalize the information leakage of $B$. By definition, the output probability of $B \rightarrow Y$ has been extracted from that of  $R, B \rightarrow Y$ (please see equation (5)). In other words, REV only measures the new information in a rationale $R$ to predict $Y$, beyond the label leakage in $B$. In contrast, LAS groups examples based on whether they leak labels or not, and computes the macro average across groups, but does not penalize the information leakage. In Fig. 2, LAS fails to penalize $(Y^*; B)$, while giving a high score to award the information leakage.

### 2. Clarification on the models used for the task and the metric

For clarification, we use the same model architecture (T5) for the task and evaluation, but the models are different and are trained on different (input, output) pairs. For example, the evaluator is trained on $[R, B]\rightarrow Y$, while the task model is trained on $X\rightarrow YR$. Additionally, our metric measures the information gain based on the difference between the log probabilities of the evaluator's outputs (please see equation (5)). Thus, in our experiments, the REV score is rarely 0. Since the evaluator was trained with gold rationales and labels, it would not overfit the outputs of task models. We choose T5 Large as the evaluator because it consistently outperforms other model architectures (T5 Base, BART Large and GPT-2 Large) in capturing new information, as shown in Figure 6 in Appendix C.1.


### 3. Clarification on quantitative evaluation

Thank you for your understanding on the challenge of evaluating free-text rationales. In addition to the comparison with human evaluation, we have conducted quantitative evaluations as follows:
* Sensitivity test to input perturbations in Section 4.3, showing REV is more sensitive to reflect the relationships between rationales and labels than other metrics. 
* Agreement with human annotations on whether GPT-3 generated rationales support gold labels (69% of REV vs. 73% of human) in Section 4.4. 

### 4. Analysis on REV$<$0 

Thank you for pointing this out. We provide some examples of $X \rightarrow RY$ with negative REV scores on ECQA below.  When $REV < 0$, we observe in most cases the rationale does not support the given label, while indicating other labels, or something even beyond the label candidates (e.g., ``helicopter" in the second example). Or they could repeat the input (e.g., the first example). The same observation holds for other types of rationale-label pairs. We have added the discussion into the updated manuscript (in Appendix C.4). 


|                            Input                           | Label         | Rationale                                                                                             | REV  |
|:----------------------------------------------------------:|---------------|-------------------------------------------------------------------------------------------------------|------|
| What do people call it when they are going for run?        | falling down  | People call it run when they are going for run.                                                       | -1.6 |
| What enables most people to transport themselves?          | own cars      | People who believe in god are able to transport themselves through helicopter.                        | -1.6 |
| Where would you expect to find popcorn in a public place?  | movie theater | Popcorn can be found in a public place.                                                               | -1.5 |
| What are you usually at when you sit on a bench on a curb? | city          | Ohio is a state in the United States. You are usually at street corner when you sit on bench on curb. | -0.8 |


### 5. On the definition of accuracy in Figure 3

Accuracy in Fig 3 is the model prediction accuracy, defined as $\frac{1}{N}\sum_{n=1}^{N}1 [y_n=y^{*}_{n}]$, where $N$ is the number of test examples. We have clarified this in the updated version (in the caption of Figure 3).

From: ICLR.cc/2023/Conference/Paper3309/Authors
title: Response to Reviewer 1gjq (3/3)
comment: ### 4. On the necessity of Algorithm 1 and clarification of $\mathcal{S}$:

Thank you for pointing this out. We have changed $\mathcal{S}$ to a list and made Algorithm 1 more concise. We have also moved Algorithm 1 to appendix for readers interested in the computation of both pointwise and aggregate REV scores. 

### 5. On the difficulty in the interpretation of REV compared to LAS and RQ. 

An intuitive way to interpret REV is by looking at the sign, where ``REV$>0$" indicates additional new information that supports the label, while REV$\leq 0$ indicates the lack of new information or noisy information. In addition, the value of REV indicates the information quantity, which cannot be quantified by LAS or RQ. 

### 6. Regarding the concern on having a baseline rationale for evaluation.

We appreciate the reviewer’s concern on the baseline rationale and would like to elaborate more:
* For different rationales, if their corresponding baseline rationales are different, the ranking would be different, depending on how much additional information they contain over the baselines. 
* Considering the baseline rationale is indispensable to achieve the evaluation goal—quantifying the new information in a rationale that supports the label beyond the information already existing in the input or the label. 
* We use ranking for the sake of comparison to other baselines due to different scales. However, we can evaluate each rationale individually based on the REV score to gain insights on how good the rationale is w.r.t. label association and amount of new information. 

### 7. On the comparison to the other metrics

We believe REV has shown advantages in evaluating rationales along a new dimension—the amount of new information—hence achieving better performance than LAS and RQ (e.g., better ranking the rationales). We also conducted other experiments to compare them, such as sensitivity to input perturbations in Sec. 4.3, and agreement with human annotations on whether GPT-3 generated rationales support gold labels or not in Sec. 4.4. The latter experiment is based on accuracy, without favoring scalar scores. All in all, we show through several experiments that our metric is superior to other baselines. 


### 8. On the clarification of experimental settings:


For $(Y^*, R^*)$, $(Y^*, B^*)$, and $(XY^* \rightarrow R)$, we used gold labels ($Y^*$) to compute REV; for $(X \rightarrow YR)$ and $(X \rightarrow RY)$, we used model predicted labels ($Y$).

We did not do additional human evaluation on $(Y^*; B)$ because by definition, $B$ is a simple combination of $X$ and $Y^*$, without supplying any new information. Indeed, for our human evaluation, we train workers to recognize these baseline rationales as vacuous. We have clarified this in the updated version (please see the footnote on page 7).

From: ICLR.cc/2023/Conference/Paper3309/Authors
title: Response to Reviewer 1gjq (2/3)
comment: ### 3. As for the analysis of the impact of trivial protocols (meaningless yet sufficient explanations that encode the label)

The constructed baseline rationales are trivial protocols as they have encoded the label information while being uninformative. We have evaluated those baseline rationales (Y*;B) as inputs in Section 4.1 (Fig. 2), showing the effectiveness of REV in penalizing trivial rationales. 

In addition, we randomly selected 250 gold labels in ECQA and extract their related sentences from a large-scale knowledge base—GenericsKB (Bhakthavatsalam et al. 2020). These sentences contain the labels, while providing little or irrelevant new information to explain the labels w.r.t. the inputs. We use them as trivial rationales for evaluation. The average REV scores for those trivial rationales and their crowdsourced counterparts are 0.02 and 0.87 respectively, indicating the effectiveness of REV in identifying the new and relevant information in rationales. 

The table shows the REV scores of some examples and the corresponding crowdsourced rationales. We observe that REV can distinguish the new information in different rationales and penalize meaningless ones. Overall, REV gives higher scores to crowdsourced rationales than trivial sentences from GenericsKB. We have added a discussion as well as examples into Appendix C.7, and Table 9. 


| Source       |                                   Input                                   | Label             | Rationale                                                                                                                                                                            | REV  |
|--------------|:-------------------------------------------------------------------------:|-------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------|
| Crowdsourced | There is normally a parking area near what collection of small dwellings? | apartment complex | Apartment complex is a small dwelling area. People park their vehicles in the complex's designated parking area at night.                                                            | 0.3  |
| Crowdsourced | Where will you find billions of grains of sand?                           | beach             | A beach is a pebbly or sandy shore, especially by the sea between high and low water marks. A sandy shore contains billions of grains of sand.                                       | 0.1  |
| Crowdsourced | What is a person the leader of after they are inagurated?                 | country           | Country is a nation with its own government, occupying a particular territory. The person is the leader of a country after they are inaugurated.                                     | 0.6  |
| GenericsKB   | There is normally a parking area near what collection of small dwellings? | apartment complex | Apartment complexes are usually on easy-access roads and places to shop.                                                                                                             | 0.1  |
| GenericsKB   | Where will you find billions of grains of sand?                           | beach             | Beach meshing operations change the habitat of some coastal areas, increasing mortality of sharks.                                                                                   | 0    |
| GenericsKB   | What is a person the leader of after they are inagurated?                 | country           | Countries adopt a peg as a way of promoting international confidence in their own currency.                                                                                          | -0.2 |

From: ICLR.cc/2023/Conference/Paper3309/Authors
title: Response to Reviewer 1gjq (1/3)
comment: We thank the reviewer for considering our "problem statement" a critical one and appreciate the constructive feedback. 
Below, we provide explanations/clarifications to address the reviewer’s concerns. 

### 1. On the novelty of this work, especially compared to Ethayarajh et al. (2022)

As pointed out in the general response, we distinguish our work from Ethayarajh et al. (2022) by highlighting our novelty which lies both in problem setting and method design/operationalization. We would like to highlight the contributions of this work and its distinction with Ethayarajh et al. (2022):
* The main contribution is that we reformulate conditional $\mathcal{V}$-information to make it applicable for the evaluation of free-text rationales.
* Our work and Ethayarajh et al. (2022) focus on different research questions. Ethayarajh et al. (2022) utilize $\mathcal{V}$-information to measure data difficulty, while we evaluate the new information in the free-text rationale for model explanation via conditional $\mathcal{V}$-information. 
* Our work is the first to evaluate free-text rationales from the information-theoretic perspective which is a novel application of CVI. 
* The operationalizations are different. We construct the baseline as a surface-form text, while in Ethayarajh et al. (2022), it is a null input. In addition, our work is the first that applies/implements conditional $\mathcal{V}$-information in reasoning tasks (e.g., commonsense question-answering) and models the mapping from inputs to outputs as a generation task, while Ethayarajh et al. (2022) focus on classification tasks.

### 2. On the relevant references.

Thank you for pointing us to the relevant references. We have included a discussion of those papers in the updated version (Sec. 5). 

We would also like to highlight the main differences between these two papers, Pruthi et al., 2022 and Treviso \& Martins, 2020, and our work, as well as LAS and RQ:
* Both Pruthi et al., 2022 and Treviso \& Martins, 2020 focus on extractive explanations where important features are extracted from input texts, while our work, LAS, and RQ focus on free-text rationales that could contain new information beyond inputs.
* The way that Pruthi et al., 2022 evaluate explanations based on the performance of a student model in simulating the teacher model on future examples (without explanations) is orthogonal to our metric that directly quantifies the new information in rationales.
* Treviso \& Martins, 2020 utilize explanations as the intermediary to train a layperson to mimic the classifier's decision. They focus more on the communication ability of explanations in bridging a classifier and a layperson, while we focus more on automatically evaluating the capacity of rationales in explaining a model's decision-making and reasoning.

From: ICLR.cc/2023/Conference/Paper3309/Authors
title: Response to Reviewer 1Npi (3/3)
comment: ### 4. On the purpose of the experiments in Section 4.4 (with free-text rationales generated by GPT-3):

The purpose of experiments in Sec. 4.4 is to test the transferability of the REV evaluator (trained on the ECQA dataset) in evaluating the rationales generated by few-shot prompting. 
This also gives us insights on the reasoning and prediction processes of the large language models (i.e. GPT-3). We have made the motivation more clear in the updated version. 

### 5. Calrification on "Chain of Thought Rationales" in Section 4.4:

For clarification, the average REV scores for GPT-3 and LaMDA are close and relatively high (0.80 vs. 0.84). However, there is a large gap between their prediction accuracies (77% vs. 59%). This indicates that the generated intermediate reasoning steps (rationales) support models' predictions (consistent REV scores), but cannot guarantee their correctness (discrepant accuracies), which is also verified by the similar REV distributions of correct and incorrect predictions. We have made this clear in Section 4.4 now.

From: ICLR.cc/2023/Conference/Paper3309/Authors
title: Response to Reviewer 1Npi (2/3)
comment: ### 3. Analysis on CoS-E where model-generated rationales $(XY^* \rightarrow R)$ have higher REV scores than crowdsourced rationales

Thank you for this suggestion!  We provide a qualitative analysis below (also included in Table 6 and Appendix C.3 in the updated draft). The main observation is model-generated rationales ($XY^* \rightarrow R$) generally support labels, though provide limited new information, while many crowdsourced rationales in CoS-E are noisy or uninformative. Specifically, compared to the crowdsourced rationales in CoS-E, we observe that $XY^* \rightarrow R$ can produce better rationales that support the labels, which also corresponds to higher REV scores. However, the new information contained in those rationales is still limited (please see examples). A possible reason is the task model $(XY^* \rightarrow R)$ hardly learns to produce more informative rationales when trained using lower quality rationales from CoS-E, known quality issue as reported in prior work  (Aggarwal et al., 2021; Sun et al., 2022). 


| Type                 |                                                    Input                                                    | Label         | Rationale                                                                    | REV  |
|----------------------|:-----------------------------------------------------------------------------------------------------------:|---------------|------------------------------------------------------------------------------|------|
| Crowdsourced         | The goal was to hit the target, but a projectile ball can't hit anything if it isn't in what?               | motion        | if you stand still you get hit                                               | 0.1  |
| Crowdsourced         | When you get together with friends to watch film, you might do plenty of this?                              | have fun      | when the working day is done                                                 | -0.4 |
| Crowdsourced         | They dealt with combustible mixtures in their experiments, this is why they kept a fire extinguisher where? | chemistry lab | mixtures mixing fruitsa                                                      | -0.1 |
| $XY^* \rightarrow R$ | The goal was to hit the target, but a projectile ball can't hit anything if it isn't in what?               | motion        | a projectile ball can't hit anything if it's not in motion                   | 0.5  |
| $XY^* \rightarrow R$ | When you get together with friends to watch film, you might do plenty of this?                              | have fun      | when you get together with friends to watch film, you might do plenty of fun | 0.7  |
| $XY^* \rightarrow R$ | They dealt with combustible mixtures in their experiments, this is why they kept a fire extinguisher where? | chemistry lab | chemistry labs deal with combustible mixtures in their experiments.          | 0.5  |

From: ICLR.cc/2023/Conference/Paper3309/Authors
title: Response to Reviewer 1Npi (1/3)
comment: We thank the reviewer’s constructive comments and would like to provide more explanations to address the reviewer’s concerns. We value the reviewer’s opinions on the organization and readability of the paper, and have improved that in the updated version. Additionally, we will make our code and data public upon the publication of the paper.

### 1. On the novelty of this work, compared to Hewitt et al., (2021)

As pointed out in the general response, we distinguish our work from Hewitt et al., (2021) by highlighting our novelty which lies both in problem setting and method design/operationalization:
* The research problems are different. Hewitt et al., (2021) propose a conditional probing based on CVI to measure the ability of representations in learning linguistic information (e.g., part-of-speech) beyond baseline representations (e.g., non-contextual word embeddings). In contrast, we measure the new information uniquely present in free-text rationales, beyond the information present in the input and/or the label, such that the new information can explain why the label is selected. We reformulate Hewitt et al., (2021)’s CVI to make a novel application for the evaluation of free-text rationales from an information-theoretic perspective. 
* Our operationalization of $\mathcal{V}$-information is different. We construct the baseline as a surface-form text, while Hewitt et al., (2021) define it in the embedding space. In addition, our work is the first that applies and implements conditional $\mathcal{V}$-information for reasoning tasks (e.g., commonsense question-answering) and models the mapping from inputs to outputs as a generation task, while Hewitt et al., (2021) focus on lower-level classification tasks (e.g., POS tagging).

### 2. Why human evaluation of $(Y^*; B)$ is missing in Figure 2?

We did not do additional human evaluation on $(Y^*; B)$ because by definition, $B$ is a simple combination of $X$ and $Y^*$, without supplying any new information. Indeed, for our human evaluation, we train workers to recognize these baseline rationales as vacuous. We have clarified this in the updated version (please see the footnote on page 7).

From: ICLR.cc/2023/Conference/Paper3309/Authors
title: Response to Reviewer L878
comment: We appreciate the reviewer agreeing on the value of this work, including the soundness of the proposed information-theoretic evaluation method, the novelty of the application, the contribution to model explainability, and extensive experiments.

From: ICLR.cc/2023/Conference/Paper3309/Authors
title: General Response to All Reviewers
comment: Dear Reviewers,

We are grateful to all reviewers for their constructive comments and helpful feedback to improve our work. We are glad that the reviewers found the significance of the research problem we studied in evaluating explanations (Reviewer L878, 1Npi, 1gjq, DFWB), the new information-theoretic angle our metric provides in evaluation (Reviewer L878, 1Npi, 1gjq, DFWB), the good application of an information theory (Reviewer L878, 1gjq, DFWB), and promising evaluation results (Reviewer L878, 1Npi, DFWB).

We would like to reiterate the novelty of our work (Reviewer 1Npi, 1gjq) along three dimensions:

* **Novelty of the research problem.** Our work is the first bringing a new perspective to the evaluation of natural language rationales, i.e., quantifying any new information beyond what is presented in the input. 

* **Novelty of the application.** We reformulate conditional $\mathcal{V}$-information to make it applicable for the evaluation of free-text rationales. Our work is the first that applies this information-theoretic framework to rationale evaluation. 

   * **Novelty of the operationalization**. We construct the baseline as a surface-form text, while Hewitt et al., (2021) define it in the embedding space, and Ethayarajh et al. (2022) define it as a null input. Moreover, our work is the first to implement conditional $\mathcal{V}$-information in reasoning tasks (e.g., commonsense question-answering) and to use a generative model to map the inputs to outputs, while Hewitt et al., (2021) and Ethayarajh et al. (2022) focus on classification tasks (e.g., POS tagging). 

* **Novelty of the findings.** Our metric, REV, is able to distinguish between rationales that both support the given label, while providing different amounts of new information to explain why the label is selected for the input. This cannot be identified by previous metrics.

For other concerns raised by each reviewer, we provide separate responses. 

We have also uploaded a revised draft based on the reviewers’ feedback. The key revisions are summarized as follows: 

* Improved writing and organization (fixed typos, added clarifications, etc.). 

* Added a discussion on the relevant references, Doshi-Velez and Kim (2017), Treviso and Martins (2020), Pruthi et al. (2022), in **Sec. 5**. (Reviewer 1gjq)

* Added qualitative examples and a discussion on the REV scores of crowdsourced and model-generated $(XY^* \rightarrow R)$ rationales for CoS-E in **Appendix C.3, Table 6**. (Reviewer 1Npi)

* Added a discussion on the cases where REV scores are negative in **Appendix C.4, Table 7**. (Reviewer DFWB)

* Added an additional analysis on trivial rationales in **Appendix C.7, Table 9**. (Reviewer 1g1q)

From: ICLR.cc/2023/Conference/Paper3309/Reviewer_L878
confidence: 2: You are willing to defend your assessment, but it is quite likely that you did not understand the central parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
summary_of_the_paper: This paper proposes an information-theoretic metric for free-text rationales that is based conditional V-information. The key improvement of the REV metric is that it quantifies the new information in a rationale supporting a given label beyond the information already available in the input or the label. That is, it favours rationales that convey the reason for the choice of a label. The authors conduct experiments on two reasoning tasks, commonsense question-answering and natural language inference and claim that REV provides more sensitive measurements of new information in free-text rationales while remaining consistent with human judgments.
strength_and_weaknesses: Strengths:
1. An adaptation of a information-theoretic techinique provides an useful tool to improve explainability.
2. Extensive experiments are provided, including human evaluation.
clarity,_quality,_novelty_and_reproducibility: Paper is well-written and easy to follow. The core of the technique borrows from previous work but the application is novel and insightful.
summary_of_the_review: This work provides a sound information-theoretic tool to assess free-text rationales with the added benefit of applications to analysis of language model prompts. It is a good contribution to advance model explainability.
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
empirical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
flag_for_ethics_review: NO.
recommendation: 6: marginally above the acceptance threshold

From: ICLR.cc/2023/Conference/Paper3309/Reviewer_1Npi
confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
summary_of_the_paper: - The paper applies the conditional V-information (CVI), which is a probing method proposed by Hewitt et al., (2021) to free-text rationale evaluation.
- Unlike existing evaluation metrics, such as LAS and RQ, REV employs conditional V-information to quantify the new information in a rationale supporting a given label, beyond the information already available in the input and label.
- Experiments on four benchmark inference tasks, including few-shot prompting with GPT-3, show that REV makes consistent judgments with humans in quantifying new information for different types of rationale-label pairs. By combining zero-mean Gaussian noise with varying variances into the input embedding in inference, it is demonstrated that REV provides more sensitive measures of new information in rationale than LAS and RQ.

strength_and_weaknesses: - Strength
    - Compared with existing metrics, such as LAS and RQ, mostly focusing on measuring the direct association between the rationale and a given label, REV tries to quantify how much new information the rationale can provide to justify the label.
    - Authors claim that the evaluation of free-text rationales with REV is consistent with human judgments.
- Weaknesses
    - REV mainly applies the established probing method CVI proposed by Hewitt et al., (2021) to another specific NLP probing task: free-text rationale evaluation. Some experiments only present observations, but do not give clear explanations or illustrations.
    - The organization and readability of the paper can be improved. Sometimes it is not easy for the reader to clearly follow the storyline.
- Some issues in the experiments
    - Why human evaluation of (Y*; B) is missing in Figure 2?
    - In Table 2, the results show that "model-generated rationales (XY∗→R) have higher REV scores than crowdsourced rationales for CoS-E", but corresponding analysis seems missing.
    - What's the purpose for the experiments in section 4.4 (with free-text rationales generated by GPT-3)? It is unclear what is the essential difference from the experiments presented in section 4.1. And it seems there is no necessary analysis and explanation.
    - In "Chain of Thought Rationales" of Section 4.4, It is not clear to me how "GPT-3 and LaMDA score lower on REV" can lead to "the generated intermediate reasoning steps do not necessarily help models produce correct predictions". In general, the illustration of this part is vague. 
    - Section 4.3,  "On the other hand, the behaviors of RQ and LAS are quite different in Fig. 3 (e) and (f)." should be "(d) and (e)" instead.

clarity,_quality,_novelty_and_reproducibility: - Clarity of the experimental section needs to be improved.
- The organization of the paper can be improved for better readability.
- Novelty is mild, because REV mainly uses the established probing method CVI proposed by Hewitt et al., (2021).
- Probably there will be better reproducibility when the source codes are available.

summary_of_the_review: The paper applies the conditional V-information, which is a probing method proposed by Hewitt et al., (2021) to free-text rationale evaluation. The organization of the paper can be improved for better readability.
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
empirical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
flag_for_ethics_review: NO.
recommendation: 5: marginally below the acceptance threshold

From: ICLR.cc/2023/Conference/Paper3309/Reviewer_1gjq
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: This paper proposes a new way to evaluate rationales by using the notion of V-information. Concretely, the authors adopt the idea of having "baseline" rationales (simple statements that support the label) and proceed to evaluate the conditional V-information by computing the gap on the mutual V-information conditioned on these baseline rationales. Experiments are carried out on two tasks, question-answering (QA) and natural language inference (NLI), to evaluate the effectiveness of the metric. Further experiments also evaluate the consistency of the proposed metric by inducing input perturbations. 
strength_and_weaknesses: Things that I liked in this paper:
- The paper addresses an important and often overlooked question in explainability: how to assess explanations?
- Clear writing and precise math notation.
- The direction of combining explanations and information theory. 

But there are other parts where the paper can be improved:
- The paper lacks novelty.
- The paper does not cite relevant works. See references below.
- Although the authors analyze the impact of explanations extracted with noisier models, there is no analysis of the impact of trivial protocols (meaningless yet sufficient explanations that encode the label).
- Algorithm 1 is unnecessary, and I do not see why $\mathcal{S}$ is treated as a set instead of a list (for example, the existence of $\mathcal{S}$ makes the presence of the "REV" variable redundant).
- In contrast to LAS and EQ, it is hard to interpret REV. 
- I am unsure if having a baseline rationale is relevant for evaluating rationales. If you take a set of $k$ rationales $R_1, ..., R_k$ and apply $PI_V$, their ranking will always be the same regardless of the baseline, which is a constant. Therefore, I believe the notion of a "vacuous" baseline rationale is vague. Moreover, any text can be cast as a vacuous rationale if it supports the label. In fact, the baselines defined in this paper are far from uninformative. For example, for NLI they are defined with the template "_premise_ implies/contradicts _hypothesis_", not leading to "compact and sufficient" --- properties of a desired rationale.
- The comparison to the other metrics is unfair. While REV takes the difference of log probs, LAS and RQ take the difference of argmaxes.   And instead of evaluating them in terms of accuracy, they are ranked, heavily penalizing methods that do not produce scalar scores as output.


Missing references:

The first work to propose forward simulation: 
- Doshi-Velez, Finale, and Been Kim. "Towards a rigorous science of interpretable machine learning." arXiv preprint arXiv:1702.08608 (2017). 

Papers that propose automatic ways to evaluate simulability (conceptually different than LAS and RQ):
- [The Explanation Game: Towards Prediction Explainability through Sparse Communication](https://aclanthology.org/2020.blackboxnlp-1.10) (Treviso & Martins, BlackboxNLP 2020)
- [Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students?](https://aclanthology.org/2022.tacl-1.21) (Pruthi et al., TACL 2022)

clarity,_quality,_novelty_and_reproducibility: The paper is well-written, and the method's central ideas are laid out clearly.  

The experiments are somewhat confusing since they involve many setups in which the model interacts with the inputs/rationales/outputs: $(Y^*; R^*), (Y^*; B), (XY^* \to R), (X \to YR), (X \to RY)$. Which $y$ was used to get REV scores in each of these settings? The gold or predicted $y$? On the same line, why was the pair $(Y^*; B)$ not used in the human evaluation?

Moreover, the novelty of this paper can be questioned. In particular, V-information is treated as a tool, and the empirical novelty of applying V-information to gain insights into examples was already given by Ethayarajh et al. (2022). Therefore, I believe this paper replaces the inputs $X$ by the rationales $R$, and "empty inputs" $\varnothing$ by "vacuous rationales" $B$. 

Ethayarajh, Kawin, Yejin Choi, and Swabha Swayamdipta. "Understanding Dataset Difficulty with $V$-Usable Information." In International Conference on Machine Learning, pp. 5988-6008. PMLR, 2022.
summary_of_the_review: Overall, I believe that despite proposing an exciting direction on linking information theory with explainability, the paper can be improved in terms of novelty and evaluation.   

correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
flag_for_ethics_review: NO.
recommendation: 5: marginally below the acceptance threshold

From: ICLR.cc/2023/Conference/Paper3309/Reviewer_DFWB
confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
summary_of_the_paper: The paper proposed a new way to evaluate the quality of free-text rationales generated by the models along with the prediction. It argues that measuring how much new information is added in the rationale is a good indicator of its quality, which is not measured by existing metrics. The proposed metric, named REV, is based on V-entropy and can be used with different models in the evaluator (the paper used T5-large). 

The paper evaluated the quality of REV by comparing it with human ratings (and two baseline metrics, LAS and RS) on the same machine-generated rationales, and also by comparing the metrics on golden rationale (composed by human) vs machine generated. The paper used added noise in text embedding to test REV's robustness vs LAS/RS. Additional evaluation is done on rationales generated by GPT-3 for golden labels, and also on the chain-of-thought rationales (to help understand why they don't perform better in CQA tasks).
strength_and_weaknesses: Strength:

1) New perspective of evaluating free-text rationales: amount of new information added.
2) The metric is proposed with a good theoretical base.
3) The data/code will be made public, which may help the research community on the development of free-text rationales.

Weakness

1) How would REV deal with information leakage? Using X|B to predict Y seems to produce a perfect prediction as Y is part of B. The paper mentioned LAS has a strategy to deal with it but it's not clear how REV does it.
2) Using the same mode (T5) for the task and the metric. This could be Reviewer's own confusion, but if T5 can produce the label and rationale just from X (in the task), would it be perfectly able to produce the same label with/without the rationale in the evaluation? (in the metric), aka the help from added information is always 0. In the evaluation, T5-based metric also showed preference to T5-based rationale, is there any risk of overfit of evaluate the output with the same model? 
3) Evaluation is not clear. The key claim (REV measures rationale quality better) is made based on empirical observation of its distribution is similar to humans. The Reviewer fully agree that free-text evaluation is a hard problem to get a perfect numerical evaluation, but some more quantitative evaluation would be better.
4) The conclusion on chain-of-thought ("the results demonstrate the causality...") is not clear to the Reviewer, we need a stronger test to claim causality.
5) REV < 0 is an interesting case that is not explained clearly; worth adding more content.
6) Figure 3: how is accuracy defined?

p.s. a number of symbols were not able to be printed on paper correctly, e.g. in Algorithm 1, while viewing with PDF reader was fine. Please be careful.
clarity,_quality,_novelty_and_reproducibility: Clarity: ok. The paper is well written. The Reviewer had little trouble following the main idea. The evaluation section is a bit unclear on how the quality of a metric is defined quantitatively.

Quality: borderline. Some key issues were not mentioned (e.g. information leakage, see full list in Strength and Weakness).

Novelty: good. Using information theory to evaluate machine-generated rationale quality is a good perspective.

Reproducibility: good. The authors said they will release the data/code.
summary_of_the_review: The Reviewer recommends borderline reject.

Reason towards accept: novelty about using information delta to evaluate rationale quality.

Reason towards reject: some key aspect of the algorithm not clearly discussed (see strength and weakness section). Lack of clear, quantitative evaluation on the quality of evaluation.

correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
flag_for_ethics_review: NO.
recommendation: 6: marginally above the acceptance threshold