Judging the Judges: A Collection of LLM-Generated Relevance Judgements

TL;DR

This paper evaluates the use of Large Language Models for generating relevance judgments in information retrieval, benchmarking various approaches and analyzing biases and effectiveness compared to human assessments.

Contribution

It introduces the LLMJudge challenge, releasing a large dataset of 42 LLM-generated relevance labels and benchmarking different methods for automated relevance assessment.

Findings

LLMs can produce diverse relevance judgments useful for evaluation.

Systematic biases in LLM-generated labels are identified.

Ensemble models and methodological improvements enhance automated evaluation.

Abstract

Using Large Language Models (LLMs) for relevance assessments offers promising opportunities to improve Information Retrieval (IR), Natural Language Processing (NLP), and related fields. Indeed, LLMs hold the promise of allowing IR experimenters to build evaluation collections with a fraction of the manual human labor currently required. This could help with fresh topics on which there is still limited knowledge and could mitigate the challenges of evaluating ranking systems in low-resource scenarios, where it is challenging to find human annotators. Given the fast-paced recent developments in the domain, many questions concerning LLMs as assessors are yet to be answered. Among the aspects that require further investigation, we can list the impact of various components in a relevance judgment generation pipeline, such as the prompt used or the LLM chosen. This paper benchmarks and reports on the results of a large-scale automatic relevance judgment evaluation, the LLMJudge challenge at SIGIR 2024, where different relevance assessment approaches were proposed. In detail, we release and benchmark 42 LLM-generated labels of the TREC 2023 Deep Learning track relevance judgments produced by eight international teams who participated in the challenge. Given their diverse nature, these automatically generated relevance judgments can help the community not only investigate systematic biases caused by LLMs but also explore the effectiveness of ensemble models, analyze the trade-offs between different models and human assessors, and advance methodologies for improving automated evaluation techniques. The released resource is available at the following link: https://llm4eval.github.io/LLMJudge-benchmark/