{"title":"不要用法学硕士来做相关性判断。","authors":"Ian Soboroff","doi":"10.54195/irrj.19625","DOIUrl":null,"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":0.0000,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11984504/pdf/","citationCount":"0","resultStr":"{\"title\":\"Don't Use LLMs to Make Relevance Judgments.\",\"authors\":\"Ian Soboroff\",\"doi\":\"10.54195/irrj.19625\",\"DOIUrl\":null,\"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\":0.0000,\"publicationDate\":\"2025-03-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11984504/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information retrieval research journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54195/irrj.19625\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information retrieval research journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54195/irrj.19625","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.