A Rose by Any Other Name: LLM -Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI (2025)

Abstract: Disagreement in human labeling is ubiquitous, and can be captured in human judgment distributions (HJDs). Recent research has shown that explanations provide valuable information for understanding human label variation (HLV) and large language models (LLMs) can approximate HJD from a few human-provided label-explanation pairs. However, collecting explanations for every label is still time-consuming. This paper examines whether LLMs can be used to replace humans in generating explanations for approximating HJD. Specifically, we use LLMs as annotators to generate model explanations for a few given human labels. We test ways to obtain and combine these label-explanations with the goal to approximate human judgment distributions. We further compare the resulting human with model-generated explanations, and test automatic and human explanation selection. Our experiments show that LLM explanations are promising for NLI: to estimate HJDs, generated explanations yield comparable results to human’s when provided with human labels. Importantly, our results generalize from datasets with human explanations to i) datasets where they are not available and ii) challenging out-of-distribution test sets.

## introduction human judgment distribution (hjd, @xcite @xcite refers to the distribution of labels assigned to a specific instance by a large group of human annotators, capturing human label variation @xcite . it provides rich information related to uncertainty and plausible multi-choices that should not be discarded as noise (e.g. @xcite @xcite . for example, concerning the same premise-hypothesis pair in the natural language inference @xcite @xcite , different coders may perceive the relationship differently. recent research proposed using large language models (llms) as annotators to reduce annotation cost @xcite . some works directly solicited human judgment distribution @xcite with mixed results @xcite . in contrast, @xcite used a few labels and human-provided explanations from @xcite to help llama @xcite and mixtral @xcite to effectively approximate hjd in chaosnli, the latter crowd-sourced 100 annotations for each nli instance, establishing a relatively stable hjd @xcite . they find that the resulting llm-based model judgment distributions (mjds) closely align with hjd. while this approach avoids the need for large-scale human annotations, it still requires human-given explanations that are far more costly to obtain than annotations of nli labels alone. recent studies have found that llms can effectively provide explanations for tasks such as reasoning, sentiment analysis, and even business processes @xcite @xcite . we instead study automatic explana-tion generation for the nli task. in this paper, we investigate if llms could provide reasonable explanations for nli instance labels, and whether the generated explanations are of sufficient quality to replace costly human-provided explanations. our key idea is shown in figure 1 . specifically, we let llms generate model explanations supporting each nli label, respectively. we first examine (i) can llms approximate hjd via diverse model explanations without access to label annotations? the result is positive but does still fall short of the performance of llms with human explanations. we then consider using a few human labels to guide the selection of model explanations to help generate mjds, and ask (ii) are model explanations comparable to human's when helping llms to approximate hjd? experiments show that mjds from llms and model explanations result in comparable scores with mjds from llm and human explanations -"a rose by any other name would smell as sweet." 1 furthermore, we ask (iii) does our approach extend to nli datasets that do not have human-provided explanations? we extend the proposed method to datasets without explanations, showing our method generalizes to this more common scenario. our findings are: 2 ## generating model explanation collecting human explanations for an annotation decision is labor-intensive and missing in most natural language inference (nli) datasets. in this paper, we intend to examine whether large language models can replace humans in generating 1 a quote from romeo and juliet used to metaphorically argue the intrinsic qualities or nature of something remain the same, regardless of its name or origin. ## can model explanations help llms approximate hjd as humans do? our first research question (rq1) concerns whether llm-generated explanations can model a human judgment distribution (hjd) as effectively as the human-written explanations. previously, chen et al. (2024) introduced the task of approximating hjd from a few humanwritten labels and explanations using the llmbased model judgment distribution (mjd) estimator. we adopt their approach for our experiments and extend it from datasets that require humanwritten explanations to a broader range of datasets that include only human label annotations and benefit from llm-generated explanations ( §2). to validate the performance of modelgenerated explanations in approximating hjd, we prompt the mjd estimator on gold nli labels from the varierr nli dataset @xcite and llm-generated explanations and first compare the results with those in chen et al. (2024) when both labels and explanations are human-written. we further experiment on the overlapping subset of a more widely-used dataset without explanations, mnli @xcite to show that our methodology generalizes well to more established datasets. we present our experimental setups in §3.1, and results in §3.2. ## can human preference lead to better explanation selection? as illustrated in §2, we adopted two intuitive strategies to select model explanations: first and longest. across all experiments there is no significant difference in the results obtained from the two modes (first results in appendix). to manually assess the quality of these model explanations, we recruited a human annotator to validate 1,581 llama-generated explanations on 100 nli instances. two questions were asked for each model explanation: (1) does the model explanation faithfully describe the meanings of the premise and hypothesis (yes/no)? and (2) does the explanation bring additional relevant results are in ## related work llms to generate explanations recently llmgenerated explanations have been used in various tasks (e.g., reasoning, sentimental analysis, recommender systems, education, abusive language detection; @xcite @xcite @xcite . @xcite find that llm-generated explanations show selectivity and contain illustrative elements, but less frequently are subjective or misleading. @xcite utilize human feedback on llm-generated explanations to assist in collaborative annotation. this is even a potential future direction for us-to help build a human-llm collaboration explanation nli dataset. wiegreffe et al. (2022 llms to model label distributions despite the increasing promise of llms as annotators, many studies have attempted to use llms to approximate label distributions, with mixed success @xcite @xcite . @xcite combine human explanations and labels to enhance llms' performance to approximate hjd, but rely on datasets with human-provided explanations. in contrast, we are the first to leverage llm-generated explanations to model hjd, addressing the scarcity of explanation datasets. ## conclusion this paper demonstrates that large language models can effectively generate explanations to approximate human judgment distribution in nli. our experiments reveal that model-generated explanations, when combined with a few human labels, yield results comparable to human-provided explanations in approximating hjd. notably, our approach generalizes to explanation-free datasets and remains effective in challenging ood test sets. results indicate that llm-generated explanations can significantly reduce annotation costs, making it a scalable and efficient proxy for capturing hlv.