@inproceedings{
tang2024selfretrieval,
title={Self-Retrieval: End-to-End Information Retrieval with One Large Language Model},
author={Qiaoyu Tang and Jiawei Chen and Zhuoqun Li and Bowen Yu and Yaojie Lu and ChengFu and Haiyang Yu and Hongyu Lin and Fei Huang and Ben He and Xianpei Han and Le Sun and Yongbin Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=H3at5y8VFW}
}
The rise of large language models (LLMs) has significantly transformed both the construction and application of information retrieval (IR) systems. 
However, current interactions between IR systems and LLMs remain limited, with LLMs merely serving as part of components within IR systems, and IR systems being constructed independently of LLMs. This separated architecture restricts knowledge sharing and deep collaboration between them.
In this paper, we introduce Self-Retrieval, a novel end-to-end LLM-driven information retrieval architecture.
Self-Retrieval unifies all essential IR functions within a single LLM, leveraging the inherent capabilities of LLMs throughout the IR process.
Specifically, Self-Retrieval internalizes the retrieval corpus through self-supervised learning,  transforms the retrieval process into sequential passage generation, and performs relevance assessment for reranking.
Experimental results demonstrate that Self-Retrieval not only outperforms existing retrieval approaches by a significant margin, but also substantially enhances the performance of LLM-driven downstream applications like retrieval-augmented generation.
Self-Retrieval: End-to-End Information Retrieval with One Large Language Model
Qiaoyu Tang Jiawei Chen Zhuoqun Li Bowen Yu Yaojie Lu ChengFu Haiyang Yu Hongyu Lin Fei Huang Ben He Xianpei Han Le Sun Yongbin Li