Don't Use LLMs to Make Relevance Judgments

Relevance judgments and other truth data for information retrieval (IR) evaluations are created manually. There is a strong temptation to use large language models (LLMs) as proxies for human judges. However, letting the LLM write your truth data handicaps the evaluation by setting that LLM as a ceiling on performance. There are ways to use LLMs in the relevance assessment process, but just generating relevance judgments with a prompt isn't one of them.

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Publication Details

Journal
Information Retrieval Research
Published
2025-03-25
DOI
https://doi.org/10.54195/irrj.19625
Citations
10
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
24.81
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article

Don't Use LLMs to Make Relevance Judgments

Ian Soboroff
10 citations
Information Retrieval Research
Topic Modeling
24.81
article

Don't Use LLMs to Make Relevance Judgments

Ian Soboroff
article en
10 citations

Abstract

Relevance judgments and other truth data for information retrieval (IR) evaluations are created manually. There is a strong temptation to use large language models (LLMs) as proxies for human judges. However, letting the LLM write your truth data handicaps the evaluation by setting that LLM as a ceiling on performance. There are ways to use LLMs in the relevance assessment process, but just generating relevance judgments with a prompt isn't one of them.

Information Retrieval ResearchVol. 1(1)
National Institute of Standards and Technology (US), National Institute of Standards (EG)
Quality Education
Openalex Percentile: Top 1%
Topic Modeling
24.81
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