{"title":"Don't Use LLMs to Make Relevance Judgments.","authors":"Ian Soboroff","doi":"10.54195/irrj.19625","DOIUrl":"10.54195/irrj.19625","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":520515,"journal":{"name":"Information retrieval research journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11984504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144050814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}