<ul><li>Recent studies have shown that large language models (LLMs) can assess relevance and support information retrieval (IR) tasks such as document ranking and relevance judgment generation.</li><li>In this paper, researchers investigate how different LLM modules contribute to relevance judgment through the lens of mechanistic interpretability.</li><li>They analyze the roles of various model components and identify a multi-stage, progressive process in generating relevance judgment.</li><li>The findings provide insights into the mechanisms underlying relevance assessment in LLMs, offering implications for leveraging LLMs for information retrieval tasks.</li></ul>

How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective

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