https://arxiv.org/abs/2403.00801 Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2403.00801 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Information Retrieval arXiv:2403.00801 (cs) [Submitted on 23 Feb 2024] Title:Self-Retrieval: Building an Information Retrieval System with One Large Language Model Authors:Qiaoyu Tang, Jiawei Chen, Bowen Yu, Yaojie Lu, Cheng Fu, Haiyang Yu, Hongyu Lin, Fei Huang, Ben He, Xianpei Han, Le Sun, Yongbin Li Download a PDF of the paper titled Self-Retrieval: Building an Information Retrieval System with One Large Language Model, by Qiaoyu Tang and 11 other authors Download PDF HTML (experimental) Abstract:The rise of large language models (LLMs) has transformed the role of information retrieval (IR) systems in the way to humans accessing information. Due to the isolated architecture and the limited interaction, existing IR systems are unable to fully accommodate the shift from directly providing information to humans to indirectly serving large language models. In this paper, we propose Self-Retrieval, an end-to-end, LLM-driven information retrieval architecture that can fully internalize the required abilities of IR systems into a single LLM and deeply leverage the capabilities of LLMs during IR process. Specifically, Self-retrieval internalizes the corpus to retrieve into a LLM via a natural language indexing architecture. Then the entire retrieval process is redefined as a procedure of document generation and self-assessment, which can be end-to-end executed using a single large language model. Experimental results demonstrate that Self-Retrieval not only significantly outperforms previous retrieval approaches by a large margin, but also can significantly boost the performance of LLM-driven downstream applications like retrieval augumented generation. Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2403.00801 [cs.IR] (or arXiv:2403.00801v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2403.00801 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Qiaoyu Tang [view email] [v1] Fri, 23 Feb 2024 18:45:35 UTC (516 KB) Full-text links: Access Paper: Download a PDF of the paper titled Self-Retrieval: Building an Information Retrieval System with One Large Language Model, by Qiaoyu Tang and 11 other authors * Download PDF * HTML (experimental) * TeX Source * Other Formats view license Current browse context: cs.IR < prev | next > new | recent | 2403 Change to browse by: cs cs.AI cs.CL References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... BibTeX formatted citation x [loading... ] Data provided by: Bookmark BibSonomy logo Reddit logo (*) Bibliographic Tools Bibliographic and Citation Tools [ ] Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) [ ] Litmaps Toggle Litmaps (What is Litmaps?) [ ] scite.ai Toggle scite Smart Citations (What are Smart Citations?) ( ) Code, Data, Media Code, Data and Media Associated with this Article [ ] Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) [ ] DagsHub Toggle DagsHub (What is DagsHub?) [ ] GotitPub Toggle Gotit.pub (What is GotitPub?) [ ] Links to Code Toggle Papers with Code (What is Papers with Code?) [ ] ScienceCast Toggle ScienceCast (What is ScienceCast?) ( ) Demos Demos [ ] Replicate Toggle Replicate (What is Replicate?) [ ] Spaces Toggle Hugging Face Spaces (What is Spaces?) [ ] Spaces Toggle TXYZ.AI (What is TXYZ.AI?) ( ) Related Papers Recommenders and Search Tools [ ] Link to Influence Flower Influence Flower (What are Influence Flowers?) [ ] Connected Papers Toggle Connected Papers (What is Connected Papers?) [ ] Core recommender toggle CORE Recommender (What is CORE?) * Author * Venue * Institution * Topic ( ) About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) * About * Help * Click here to contact arXiv Contact * Click here to subscribe Subscribe * Copyright * Privacy Policy * Web Accessibility Assistance * arXiv Operational Status Get status notifications via email or slack