# 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  
**arXiv:** [2403.00801](https://arxiv.org/abs/2403.00801)  
**Date:** 2024-02-23  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.00801v1/

## Key points

- Large language models (LLMs) have transformed information retrieval (IR) systems
- Self-Retrieval is a groundbreaking solution that integrates essential IR functionalities into a single LLM
- Self-Retrieval operates by internalizing the corpus within an LLM through natural language indexing
- Experimental results show that Self-Retrieval surpasses previous approaches and enhances downstream applications driven by LLMs

## Summary

, , , , 

The emergence of large language models (LLMs) has transformed the landscape of information retrieval (IR) systems, bringing about a paradigm shift in how humans access information. Traditional IR systems have struggled to adapt to this change, but a groundbreaking solution known as Self-Retrieval offers a promising solution. This end-to-end, LLM-driven architecture seamlessly integrates essential IR functionalities into a single LLM, harnessing its full potential during the retrieval process. Self-Retrieval operates by internalizing the corpus within an LLM through sophisticated natural language indexing, redefining the retrieval process as document generation and self-assessment steps that can be efficiently executed using one large language model. Experimental results have shown that Self-Retrieval surpasses previous approaches and significantly enhances downstream applications driven by LLMs. Authored by Qiaoyu Tang, Jiawei Chen, Bowen Yu, Yaojie Lu, Cheng Fu, Haiyang Yu, Hongyu Lin, Fei Huang, Ben He, Xianpei Han, Le Sun and Yongbin Li; "Self-Retrieval: Building an Information Retrieval System with One Large Language Model" sheds light on this cutting-edge approach that promises to revolutionize information retrieval processes and elevate efficiency and effectiveness of LLM-driven applications across various domains.