# Large Language Models for Information Retrieval: A Survey

**Authors:** Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Zhicheng Dou, Ji-Rong Wen  
**arXiv:** [2308.07107](https://arxiv.org/abs/2308.07107)  
**Date:** 2023-08-14  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2308.07107v1/

## Key points

- Search engines are essential tools in information retrieval, evolving from traditional term-based methods to advanced neural models.
- Challenges such as data scarcity and interpretability persist in modern architectures.
- Large language models (LLMs) like ChatGPT and GPT-4 have revolutionized natural language processing, enhancing language understanding, generation, generalization, and reasoning abilities.
- Recent research focuses on leveraging LLMs to enhance IR systems by combining sparse retrieval methods with powerful language models.
- The confluence of LLMs and IR systems has led to advancements in query rewriting, retrieval mechanisms, reranking strategies, and reading comprehension within the field.
- A survey conducted by Yutao Zhu et al. explores the intersection between LLMs and IR systems, providing insights into query optimization and result ranking through large language models' capabilities.
- This overview highlights the transformative impact of LLMs on information retrieval processes and emphasizes the importance of integrating cutting-edge technologies with established methodologies for innovation in IR systems.

## Summary

In the realm of <b>information retrieval (IR)</b>, <b>search engines</b> have become indispensable tools for acquiring information in our daily lives. These systems have evolved from traditional term-based methods to advanced <b>neural models</b>, which excel at capturing complex contextual signals and semantic nuances. However, challenges such as data scarcity and interpretability persist in these modern architectures. The integration of large language models (<b>LLMs</b>) like ChatGPT and GPT-4 has revolutionized natural language processing by enhancing language understanding, generation, generalization, and reasoning abilities. Recent research has focused on leveraging LLMs to enhance IR systems, aiming to address the limitations of traditional methods while harnessing the power of neural architectures. This evolution necessitates a balanced approach that combines the strengths of both sparse retrieval methods and powerful language models. The confluence of LLMs and IR systems has led to advancements in query rewriting, retrieval mechanisms, reranking strategies, and reading comprehension within the field. The survey conducted by Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Zhicheng Dou, and Ji-Rong Wen delves into this intersection between LLMs and IR systems. By exploring crucial aspects such as query optimization and result ranking through the lens of large language models' capabilities, the authors provide nuanced insights into the evolving landscape of information retrieval. Additionally, they identify promising directions for future research within this rapidly expanding field. Overall,<b>this comprehensive overview highlights the transformative impact of LLMs on information retrieval processes </b>and underscores the importance of integrating cutting-edge technologies with established methodologies to drive innovation in IR systems.