  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.
