The research field of Information Retrieval (IR) has evolved significantly,
expanding beyond traditional search to meet diverse user information needs.
Recently, Large Language Models (LLMs) have demonstrated exceptional
capabilities in text understanding, generation, and knowledge inference,
opening up exciting avenues for IR research. LLMs not only facilitate
generative retrieval but also offer improved solutions for user understanding,
model evaluation, and user-system interactions. More importantly, the
synergistic relationship among IR models, LLMs, and humans forms a new
technical paradigm that is more powerful for information seeking. IR models
provide real-time and relevant information, LLMs contribute internal knowledge,
and humans play a central role of demanders and evaluators to the reliability
of information services. Nevertheless, significant challenges exist, including
computational costs, credibility concerns, domain-specific limitations, and
ethical considerations. To thoroughly discuss the transformative impact of LLMs
on IR research, the Chinese IR community conducted a strategic workshop in
April 2023, yielding valuable insights. This paper provides a summary of the
workshop's outcomes, including the rethinking of IR's core values, the mutual
enhancement of LLMs and IR, the proposal of a novel IR technical paradigm, and
open challenges.

信息检索领域，自传统搜索开始已经发展得非常成熟，并且扩展到满足用户不同的信息需求。最近，大型语言模型在文本理解、生成和知识推理方面表现出了异常的能力，为信息检索研究打开了令人兴奋的新方向。大型语言模型不仅有助于生成式检索，还为用户理解、模型评估和用户系统交互提供了改进的解决方案。最重要的是，信息检索模型、大型语言模型和人类之间的相互作用形成了一个更强大的信息搜索技术范式。信息检索模型提供实时和相关的信息，大型语言模型提供内部知识，而人类在信息服务的可靠性方面起到了需求者和评估者的核心角色。然而，仍然存在一些重大挑战，包括计算成本、可信度问题、特定领域限制和伦理考虑。为了深入讨论大型语言模型对信息检索研究的变革性影响，中国信息检索界于 2023 年 4 月举办了一次战略研讨会，得出了有价值的见解。本文总结了研讨会的成果，包括对信息检索核心价值的重新思考、大型语言模型和信息检索的相互增强、一个新的信息检索技术范式的提出以及面临的挑战。