大模型让对话式搜索更智能，支持多轮理解与灵活交互。
Conversational Search: From Fundamentals to Frontiers in the LLM Era
- 利用大模型理解上下文中的用户意图，实现多轮对话搜索。
- 结合指令跟随与生成能力，提升搜索结果的准确性与自然性。
- 适合对智能搜索、对话系统感兴趣的研究者与开发者。
对话式搜索通过多轮交互满足用户的复杂信息需求。在交互过程中，系统需理解用户在对话上下文中的搜索意图，并通过灵活的对话界面返回相关结果。近年来，具备指令遵循、内容生成和推理能力的大语言模型（LLMs）受到广泛关注，为构建智能对话式搜索系统带来了新机遇与挑战。本教程旨在介绍对话式搜索中基础原理与由大模型推动的前沿进展之间的联系，面向来自学术界与产业界的师生及从业者。参与者将全面了解对话式搜索的核心原则与由大模型驱动的最新发展，获得助力下一代对话式搜索系统建设的知识基础。
原文摘要 · Abstract (English)
Conversational search enables multi-turn interactions between users and systems to fulfill users' complex information needs. During this interaction, the system should understand the users' search intent within the conversational context and then return the relevant information through a flexible, dialogue-based interface. The recent powerful large language models (LLMs) with capacities of instruction following, content generation, and reasoning, attract significant attention and advancements, providing new opportunities and challenges for building up intelligent conversational search systems. This tutorial aims to introduce the connection between fundamentals and the emerging topics revolutionized by LLMs in the context of conversational search. It is designed for students, researchers, and practitioners from both academia and industry. Participants will gain a comprehensive understanding of both the core principles and cutting-edge developments driven by LLMs in conversational search, equipping them with the knowledge needed to contribute to the development of next-generation conversational search systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。