# Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community

**Authors:** Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, Shen Gao, Jiafeng Guo, Xiangnan He, Yanyan Lan, Chenliang Li, Yiqun Liu, Ziyu Lyu, Weizhi Ma, Jun Ma, Zhaochun Ren  
**arXiv:** [2307.09751](https://arxiv.org/abs/2307.09751)  
**Date:** 2023-07-19  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.09751v2/

## Key points

- Information Retrieval (IR) has evolved significantly, expanding beyond traditional search methods to cater to diverse user information needs.
- Large Language Models (LLMs) have played a crucial role in this transformation, showcasing remarkable capabilities in text comprehension, generation, and knowledge inference.
- LLMs have made a substantial impact on generative retrieval, enhancing user understanding, model evaluation, and interactions between users and systems.
- The integration of IR models, LLMs, and human users has created a new technical paradigm that enhances the information seeking process.
- Challenges related to computational costs, credibility of generated content, domain-specific limitations, and ethical data usage need to be addressed in the integration of LLMs into IR research.
- A strategic workshop organized by the Chinese IR community highlighted the mutual enhancement potential between LLMs and traditional IR techniques.
- Researchers need to collaborate to address challenges while leveraging LLM strengths for advancing information retrieval capabilities further.

## Summary

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The field of Information Retrieval (IR) has undergone significant evolution in recent years, expanding beyond traditional search methods to cater to a wide range of user information needs. This transformation has been largely driven by the emergence and advancement of Large Language Models (LLMs), which have showcased remarkable capabilities in text comprehension, generation, and knowledge inference. The integration of LLMs into IR research has opened up exciting possibilities and avenues for exploration. One key aspect where LLMs have made a substantial impact is in generative retrieval, offering enhanced solutions for user understanding, model evaluation, and interactions between users and systems. The collaborative relationship between IR models, LLMs, and human users has given rise to a new technical paradigm that significantly enhances the process of information seeking. While IR models deliver real-time and relevant information, LLMs contribute internal knowledge that enriches the retrieval process. Human users play a pivotal role as both demanders of information and evaluators of the reliability of information services. Despite the numerous benefits brought about by the integration of LLMs into IR research, there are notable challenges that need to be addressed. These include concerns related to computational costs, credibility issues surrounding generated content, limitations in domain-specific applications, and ethical considerations regarding data usage. To delve deeper into the transformative impact of LLMs on IR research, the Chinese IR community organized a strategic workshop in April 2023. The insights gathered from this workshop have led to a reevaluation of core values within the field of IR, highlighting the mutual enhancement potential between LLMs and traditional IR techniques. A novel technical paradigm for IR has been proposed based on this synergy between different components. Moving forward, it is essential for researchers in the field to address these challenges collaboratively while leveraging the strengths offered by LLMs to advance information retrieval capabilities further. By embracing this new technical paradigm and working towards overcoming existing obstacles through collective efforts, the future holds promising opportunities for enhancing information retrieval processes and services.