用大模型提升对话式搜索中的查询理解能力，让系统更懂用户意图。
Query Understanding in LLM-based Conversational Information Seeking
- 利用大模型动态解析上下文，精准捕捉用户真实需求
- 提出多轮交互下的评估指标，量化查询理解质量
- 适合研究对话系统与智能搜索的开发者和学者
对话式信息检索（CIS）中的查询理解需通过上下文感知的交互准确解读用户意图，包括消歧、查询优化和适应不断变化的信息需求。大语言模型（LLMs）通过理解复杂语言并动态调整，显著提升了实时搜索结果的相关性与准确性。本文教程探讨基于大模型的查询理解先进技术，涵盖构建稳健评估指标以衡量多轮交互中查询理解质量的方法、增强系统交互性的策略，以及主动查询管理与查询重写等应用。同时讨论了将大模型融入对话搜索系统时的关键挑战，并展望未来研究方向。目标是深化对基于大模型的对话式查询理解的理解，激发持续创新。
原文摘要 · Abstract (English)
Query understanding in Conversational Information Seeking (CIS) involves accurately interpreting user intent through context-aware interactions. This includes resolving ambiguities, refining queries, and adapting to evolving information needs. Large Language Models (LLMs) enhance this process by interpreting nuanced language and adapting dynamically, improving the relevance and precision of search results in real-time. In this tutorial, we explore advanced techniques to enhance query understanding in LLM-based CIS systems. We delve into LLM-driven methods for developing robust evaluation metrics to assess query understanding quality in multi-turn interactions, strategies for building more interactive systems, and applications like proactive query management and query reformulation. We also discuss key challenges in integrating LLMs for query understanding in conversational search systems and outline future research directions. Our goal is to deepen the audience's understanding of LLM-based conversational query understanding and inspire discussions to drive ongoing advancements in this field.
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