Paper: Measuring Information in Text Explanations (ICLR-2024)

Reviewer comment (Writing):
The writing can be improved: The goal of this paper can be more clear. I was lost in the middle of the paper and wondered (1) which parts are used for evaluation and (2) what are they truly evaluating for? I could only realize the goal after reading through the whole paper and gave it a guess.

Author response:
- **The evaluation plan and goals.** We added the experiment protocol and goals to the first paragraphs in 5.1 to 5.3. We also updated the pdf to make it more organized.

Reviewer comment (Evaluation):
This paper claims to unify the evaluation of rationale and natural language explanations. Unification would make me expect the proposed evaluation method can evaluate them in a good standing. However, the results turn out the proposed metrics do not have a consistent meaning for human interpretation and meanwhile there is no user study to validate this.

Author response:
- **Only automatic and no human evaluations?** Yes — human eval of explanation is a huge topic. Each format of explanation format (NLE or rationale), and each topic of the explanation contents can have a unique impact to the audience [1]. Also the situation of the audience matters [2]. The human evaluations in a careful enough manner would lead to the scales of experiments that significantly go beyond the scope of this paper, so we can only focus on the automatic evaluation perspective.

Reviewer comment (Evaluation):
From the results, the most consistent part is that the proposed relevance score I(X;E) is correlated with the type overlap ratio and embedding similarity. Nonetheless, since explanan often includes (similar) words in the input, the result is not giving new insights.

Author response:
- **New insights beyond the correlations to some scores?** There are at least three insights beyond correlations: (1) With ANOVA, we quantify how much each aspect (lexical-semantic, reasoning, clarity, relevance) can explain the variation in the measured information scores. (2) We show a tradeoff between relevance-informativeness phenomenon. (3) We illustrate that the openai and cohere embeddings preserve the distributions, whereas the roberta embeddings focus more on the decision boundary.

Reviewer comment (Evaluation):
Is there a reason for choosing embeddings for computing the entropy? What do the authors think about using the probability distribution to compute the entropy?

Author response:
- **Why choose embeddings to compute the entropy?** Embedding is the crucial component that allows multiple types of explanations (NLE, rationale, and potentially other types) to be comparable on the same ground. The pipelines involving conditional probabilities usually need another module that computes the one-dimensional probability value from the embeddings.

Reviewer comment (Evaluation):
Please add proper reference to the statistics in the paper:  
  * In section 4.3: Where are the statistics for the statement “On the explanations in natural language, most inter-score correlations are low, indicating that these scores measure a diverse collection of aspects.”? The authors have pointed to Appendix A.5, but it could be better to point to Figure 7. Also, I would suggest this statistics be moved to the main content.  
  * In section 5.1: How do the authors compute the statistics “The reasoning category scores can explain 16% and 21% of the variance in the estimated I(Y ; E) for OpenAI and RoBERTa on NLE (18% and 19% for rationale), and no more than 15% for any other categories.”?  
  * In section 5.2: The authors do not put any reference to statistics in this section. I guess the authors are referring to Figure 4, 5, and 6. I would also suggest Figures 5 and 6 be moved to the main content. To comprise the paper space, I would think that prompt templates and discussion about multimodal can be moved to appendix.

Author response:
******************************************************Comments about the formats:******************************************************
    - Reference for “Inter-score correlations are low”: Indeed. They are from Figure 7. We updated the references in the pdf to make this clearer.
    - References for the ANOVA results: We updated the references to the ANOVA details to make this clearer.
    - Figures 4-6, and moving some statistics to the main content. We moved around some parts. Figures 5-6 takes more space than the prompt templates (which we also consider quite important, since LLM’s behavior is heavily affected by the actual wording of the prompts) so we are keeping them at the current version.

Reviewer comment (Writing):
I would suggest changing the naming of “informativeness” in the GPTScore evaluation items. This term is the same as the proposed “informativeness score I(Y;E)”, so can cause confusion.

Author response:
- Naming confusion for “informativeness” (GPTScore vs I(Y;E)). Good catch. We changed to informational support (`info_support` in the tables to save space) to avoid confusion.

Reviewer comment (Presentation):
Why is there no “informativeness” from Table 1 in Figure 3’s x-axis?

Author response:
- Table 1 in Figure 3 x-axis: The “informativeness” (now info_support) is added now. The past version didn’t contain this due to a confusion with I(Y;E).

Reviewer comment (Presentation):
I would suggest changing the order of x-axis in Figure 3 to match the order of them in Table 1.

Author response:
- Changing the order of x-axis in Figure 3 to match the previous table: Done.

Reviewer comment (Experiments):
The structure of how experiments and results are reported can be improved. In particular, it would be helpful if the authors list the experiments performed, listing rationale behind the experiment, the procedure, aims, metrics, and other aspects of relevance.

Author response:
**Improve the structure of experiments and results.** Thanks — we updated the pdf. The current version lists the sections in a more organized way.

Reviewer comment (Presentation):
We encourage the authors to restructure Section 4 and Section 5 to describe better (a) the original data they have and (b) the experimental design (rationale behind the experiment, procedure, aims, metrics, etc.). In the current manuscript, it seems most of the experimental design is described in the "Data and Materials" section, while the "Experiments" section resembles more to "Results and Evaluation".

Author response:
**Improve the structure of experiments and results.** Thanks — we updated the pdf. The current version lists the sections in a more organized way.

Reviewer comment (Presentation):
Figure 4: the authors provide two plots with identical descriptions, but from the caption, they seem to refer to different concepts (one should reflect informativeness, while the second reflects rationale).

Author response:
**Figure 4 name confusion**: Yes this confusion is avoided in this version. We changed the “informativeness” in the silver labels into “informational support”.

Reviewer comment (Novelty):
The approach has promise as it plows new ground in an area where there is limited success today.

Author response:
Thank you for commending on the contribution! We believe that model evaluation can benefit from information-theoretic approaches. While the area of information estimator is not as popular as e.g., prompting LLMs, or LLM agents, there are still some meaningful progress in the past a few years, and we still believe there will be improvement potential.

Reviewer comment (Evaluation):
The experiments section does not describe the process, specifically how evaluation is applied.

Author response:
To your question about the experiment procedures: we updated the pdf. Each subsection there now starts with a paragraph describing the experiment setup. The procedure is now clearer and reflects how each experiment is set up.