Benchmarking LLM-based Relevance Judgment Methods

Large Language Models (LLMs) are increasingly deployed in both academic and industry settings to automate the evaluation of information seeking systems, particularly by generating graded relevance judgments. Several studies report Kendall τ correlations exceeding 0.85 when comparing system rankings derived from human versus LLM-generated relevance labels. Previous work on LLM-based relevance assessment has primarily focused on replicating graded human relevance judgments through various prompting strategies. However, there has been limited exploration of alternative assessment methods or comprehensive comparative studies. In this paper, we systematically compare multiple LLM-based relevance assessment methods, including binary relevance judgments, graded relevance assessments, pairwise preference-based methods, and two nugget-based evaluation methods~-~document-agnostic and document-dependent. Wherever possible, we employ state-of-the-art tools and optimized prompts tailored for these methods. In addition to a traditional comparison based on system rankings using Kendall correlations, we also examine how well LLM judgments align with human preferences, as inferred from relevance grades. We conduct extensive experiments on datasets from three TREC Deep Learning tracks 2019, 2020 and 2021 as well as the ANTIQUE dataset, which focuses on non-factoid open-domain question answering. Beyond dataset-specific results, our work offers a practical methodology for evaluating diverse LLM-based relevance assessment methods. As part of our data release, we include relevance judgments generated by both an open-source (Llama3.2b) and a commercial (gpt-4o) model. Our goal is to reproduce various LLM-based relevance judgment methods to provide a comprehensive comparison. We release all the relevance judgments as a resource that establishes a baseline for future work, ensuring a level playing field for evaluation of LLM-based relevance judgments. All code, data, and resources are publicly available in our GitHub Repository at https://github.com/Narabzad/llm-relevance-judgement-comparison