“Seeing the Big through the Small”: Can LLM s Approximate Human Judgment Distributions on NLI from a Few Explanations? (2024)

Abstract: Human label variation (HLV) is a valuable source of information that arises when multiple human annotators provide different labels for valid reasons. In Natural Language Inference (NLI) earlier approaches to capturing HLV involve either collecting annotations from many crowd workers to represent human judgment distribution (HJD) or use expert linguists to provide detailed explanations for their chosen labels. While the former method provides denser HJD information, obtaining it is resource-intensive. In contrast, the latter offers richer textual information but it is challenging to scale up to many human judges. Besides, large language models (LLMs) are increasingly used as evaluators (“LLM judges”) but with mixed results, and few works aim to study HJDs. This study proposes to exploit LLMs to approximate HJDs using a small number of expert labels and explanations. Our experiments show that a few explanations significantly improve LLMs’ ability to approximate HJDs with and without explicit labels, thereby providing a solution to scale up annotations for HJD. However, fine-tuning smaller soft-label aware models with the LLM-generated model judgment distributions (MJDs) presents partially inconsistent results: while similar in distance, their resulting fine-tuned models and visualized distributions differ substantially. We show the importance of complementing instance-level distance measures with a global-level shape metric and visualization to more effectively evaluate MJDs against human judgment distributions.

## introduction in natural language processing (nlp), we are faced with many situations in which more than one plausible label (or reading) exists, a phenomenon referred to as human label variation @xcite . hlv could be caused by inherent disagreement @xcite , subjectivity @xcite , or cases where multi-premise: marriage is an important institution. hypothesis: marriage is crucial to society. based on the context, we only know the attributes of marriage as an important institution, not whether it is important to society. that something is an important institution can be interpreted as being important for society. ## related work human label variation human label variation represents a phenomenon in which inherent disagreement exists in annotation due to genuine disagreement, subjectivity or simply because two (or more) views are plausible @xcite . @xcite propose that disagreement is not noise but signal, which is giving us information, as human labels are bound to be scarce yet at the same time critical as they provide human interpretations and values. with substantial datasets, providing many judgments by high-quality coders for each item, training directly with soft labels achieved better results than training from aggregated or even gold labels @xcite . for example, @xcite find that the soft-label prediction auxiliary task reduces the penalty for errors on ambiguous entities and thereby mitigates overfitting. thus, @xcite argue for a refined evaluation objective that requires models to explicitly capture the full distribution of plausible human judgments. human label variation in nli for nli-the task to determine whether a given premise entails, contradicts, or is neutral towards a target hypothesis-there exist several datasets addressing human label variation (hlv). on the one hand, variation nli @xcite and chaos nli @xcite collect annotations from 50 and 100 crowd workers for human judgment distributions (hjds). on the other, live nli @xcite and varierr nli @xcite are annotated by only 5 or 4 linguists, but add textual explanations to their nli labels. while these works have used nli with explanations, their goals differ: either to unravel more on reasons for disagreement from the annotators @xcite or to use explanations to facilitate separation of plausible variation from annotation errors @xcite . however, little research has been done on estimating hjds from a few labels and explanations. ## llms to estimate hjds as llm outputs are typically in text form and fluctuate based on inputs and parameters, obtaining model judgment distributions (mjds) directly from the outputs to approximate human judgment distributions (hjds) is challenging. we propose llm prompts with multiple-choice questions ( §3.1), foot_0 to illustrate how we estimate mjd using first-token probability ( §3.2) and reduce biases via permutations of choice options and explanations ( §3.3). ## analysis and discussion we observe from §5 that even though llama3 and mixtral are equally similar to chaos nli hjds in distribution comparisons (see ## conclusion this paper analyzes to what extent llms can approximate human judgment distributions from a few explanations. our results show that a few explanations improve llm's ability to approximate hjds. however, measuring the distance of the resulting mjd is insufficient: while similar in distance, their resulting fine-tuned models and visualized distributions differ substantially. we adopt an error visualization tool and a global-level metric, aligning our distribution and fine-tuning results. our method can also be extended to other tasks beyond nli, and we encourage an uptake of explanation-informed datasets.