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.

翻译：信息检索（IR）研究领域已显著演进，从传统搜索扩展到满足多样化的用户信息需求。近年来，大型语言模型（LLMs）在文本理解、生成和知识推理方面展现出卓越能力，为IR研究开辟了令人振奋的新途径。LLMs不仅促进了生成式检索，还为用户理解、模型评估和用户系统交互提供了改进方案。更重要的是，IR模型、LLMs与人类之间的协同关系形成了一种更强大的信息获取技术新范式：IR模型提供实时相关信息，LLMs贡献内部知识，而人类作为信息服务的需求者和评估者发挥着核心作用。然而，仍存在显著挑战，包括计算成本、可信度问题、领域特定限制以及伦理考量。为深入探讨LLMs对IR研究的变革性影响，中国IR社区于2023年4月举办了战略研讨会，获得了宝贵见解。本文总结了研讨会的成果，包括对IR核心价值的重新思考、LLMs与IR的相互促进、新型IR技术范式的提出以及开放挑战。