{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:22:43Z","timestamp":1785543763440,"version":"3.56.0"},"reference-count":317,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62402497 and 62272467"],"award-info":[{"award-number":["62402497 and 62272467"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve as components of dialogue, question-answering, and recommender systems. The trajectory of IR has evolved dynamically from its origins in term-based methods to its integration with advanced neural models. While the neural models excel at capturing complex contextual signals and semantic nuances, they still face challenges such as data scarcity, interpretability, and the generation of contextually plausible yet potentially inaccurate responses. This evolution requires a combination of traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity). Meanwhile, the emergence of large language models (LLMs) has revolutionized natural language processing due to their remarkable language understanding, generation, and reasoning abilities. Consequently, recent research has sought to leverage LLMs to improve IR systems. Given the rapid evolution of this research trajectory, it is necessary to consolidate existing methodologies and provide nuanced insights through a comprehensive overview. In this survey, we delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, readers, and search agents.<\/jats:p>","DOI":"10.1145\/3748304","type":"journal-article","created":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T15:11:17Z","timestamp":1757085077000},"page":"1-54","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":90,"title":["Large Language Models for Information Retrieval: A Survey"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9432-3251","authenticated-orcid":false,"given":"Yutao","family":"Zhu","sequence":"first","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin\u00a0University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6855-6558","authenticated-orcid":false,"given":"Huaying","family":"Yuan","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin\u00a0University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3013-4555","authenticated-orcid":false,"given":"Shuting","family":"Wang","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin\u00a0University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3946-9178","authenticated-orcid":false,"given":"Jiongnan","family":"Liu","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin\u00a0University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8874-719X","authenticated-orcid":false,"given":"Wenhan","family":"Liu","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin\u00a0University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9888-2589","authenticated-orcid":false,"given":"Chenlong","family":"Deng","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin\u00a0University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9812-0438","authenticated-orcid":false,"given":"Haonan","family":"Chen","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin\u00a0University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7765-8466","authenticated-orcid":false,"given":"Zheng","family":"Liu","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9781-948X","authenticated-orcid":false,"given":"Zhicheng","family":"Dou","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin\u00a0University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9777-9676","authenticated-orcid":false,"given":"Ji-Rong","family":"Wen","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin\u00a0University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Proceedings of the EMTCIR\/UM-CIR@SIGIR-AP (CEUR Workshop Proceedings)","author":"Abbasiantaeb Zahra","year":"2024","unstructured":"Zahra Abbasiantaeb, Chuan Meng, Leif Azzopardi, and Mohammad Aliannejadi. 2024. 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