A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI
Authors: Beiduo Chen, Siyao Peng, Anna Korhonen, Barbara Plank
Accepted at: acl25


Reviewer paper summaries: The paper explores the potential of LLMs to generate explanations that approximate human judgment distributions (HJD) in Natural Language Inference (NLI) tasks. The authors test whether LLM-generated explanations can serve as viable substitutes for costly human-written explanations, finding that when combined with a few human labels, model explanations yield comparable results to human explanations. Their approach generalizes to datasets lacking human-provided explanations and improves performance on OOD test sets. Experiments reveal that variability in explanations plays a crucial role in modeling human label variation (HLV), and LLMs can effectively approximate HJD even without extensive human annotation.

This paper explores whether explanations generated by LLMs can be used for human judgement distributions. The experiments with different LLMs on different settings and datasets proved that explanations generated by LLMs when combined with human labels yield comparable results to human-provided explanations in human judgement distributions.

The paper explores the use of LLMs to generate explanations for natural language inference tasks, aiming to approximate human judgment distributions (HJD). The core idea is to see if LLM-generated explanations can reliably substitute human-provided explanations, which are often costly and time-consuming to acquire. The study investigates different methods for obtaining and combining these LLM-generated explanations, comparing them with human explanations, and also tests the approach on datasets where human explanations are not available.
Summary of strengths: 1. This paper presents an innovative use of LLMs for generating explanations in NLI tasks. By using LLM-generated explanations as proxies for HJD, the study explores a cost-effective and scalable alternative to human annotation.
2. The experimental design is robust and well-structured, incorporating multiple datasets to ensure a comprehensive evaluation of LLM-generated explanations. The study carefully considers OOD scenarios, demonstrating the generalizability of its approach beyond the training data. Additionally, the use of diverse and well-chosen evaluation metrics, including distributional similarity measures and fine-tuning performance, strengthens the validity of the findings and provides a nuanced understanding of how model-generated explanations compare to human annotations.

* This paper studies an interesting and important task of human judgement distributions. I really loved the idea of evaluating LLMs' explanations by study whether they can be used for HJD.
* Comprehensive experiments proved that explanations generated by LLMs when combined with human labels yield comparable results to human-provided explanations in human judgement distributions. 
* The author further constructed OOD experiments to prove that the LLMs' ability in explanation generation isn't from learning directly from the training data.

* The problem of scaling up annotation tasks using LLMs is well-motivated.

* The authors posed three research questions in the context of LLM-as-annotator and conducted experiments to provide answer/explanation and insights.

* The proposed approach demonstrates promising outcomes in terms of generalizability.
Summary of weaknesses: 1. The primary concern I have with this paper is its generalizability beyond NLI. While NLI is indeed one of the most well-structured tasks for explanation generation and benefits from extensive datasets, the conclusions drawn here may still feel too domain-specific. It remains unclear how well the proposed approach would transfer to other NLP tasks that involve different reasoning patterns or annotation challenges. Additionally, I wonder whether the authors have considered potential data contamination issues in the ANLI dataset, as model exposure to similar training data could impact the validity of the OOD evaluation.
2. This leads to my second question: what is the main motivation or practical application of this work if it does not generalize beyond NLI? While I appreciate the exploratory nature of the study, it would be helpful to clarify how these findings translate into real-world applications. If LLM-generated explanations are primarily effective within NLI, their impact might be limited unless the approach can be adapted for other NLP tasks. A clearer discussion of its broader applicability would strengthen the paper’s contributions.

* In section 2, why generate many explanations then query and select some of them? I'm curious about the performance when you only ask the model to generate one explanations.
* Narrative and writing could be improved. The current structure makes it hard for me to read and comprehend. Here are some example:
  * In section 2, there are 4 paragraphs in parallel, but their relationship isn't really in parallel. I would put Model Explanation Generation as section 2.1, and put Label-Free / Label-Guided in section 2.2.1 and 2.2.2 respectively, and finally make "Selecting First vs longest explanations" as a paragraph in section 2.2.2
  * I would suggest write all RQs in the introduction, and explain "sub-rqs" like the current paragraph title in section 3.2 in the beginning of section 3.

* The paper is missing discussion relevant related work. Wang et al. [1] explores the impact of LLM-generated explanations for annotation tasks. Wiegreffe et al. [2] explore free text explanations for NLI tasks along coarse- and fine-grained dimensions and Mishra et al. [3] highlight the implication of faithfully explaining model predictions with guardrails. The work needs better contextualization with existing work and insights.

* While the paper shows that LLM explanations can approximate HJD, it does not address how these explanations would be used in practice or how effectively they help real users understand and resolve disagreements.

* It’s unclear why the authors opted to select the longest explanations. The selection criteria is not well-justified.

[1] Wang X, Kim H, Rahman S, Mitra K, Miao Z. Human-LLM collaborative annotation through effective verification of LLM labels. InProceedings of the CHI Conference on Human Factors in Computing Systems 2024 May 11 (pp. 1-21).
[2] Wiegreffe S, Hessel J, Swayamdipta S, Riedl M, Choi Y. Reframing human-AI collaboration for generating free-text explanations. arXiv preprint arXiv:2112.08674. 2021 Dec 16.
[3 Aditi Mishra, Sajjadur Rahman, Kushan Mitra, Hannah Kim, and Estevam Hruschka. 2024. Characterizing Large Language Models as Rationalizers of Knowledge-intensive Tasks. In Findings of the Association for Computational Linguistics: ACL 2024, pages 8117–8139, Bangkok, Thailand. Association for Computational Linguistics.
Comments, suggestions and typos: I find this study to be well-designed and methodologically strong, with a rigorous experimental setup and thorough evaluation. However, I still have concerns regarding the identified weaknesses, particularly in terms of generalizability beyond NLI and potential data contamination in ANLI. If the authors provide a satisfactory response addressing these concerns, I would be inclined to raise my score, as the overall contributions of the paper are significant.

see summary of weakness.
Also, it is unclear that whether the author is going to release their constructed OOD ANLI dataset.

see strengths and weaknesses.
Overall assessment scores: 3.5

3

3.5