Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2025

Abstract

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 multiturn 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.

Keywords

Query understanding, Large language models, Conversational search

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

WWW '25: The ACM Web Conference 2025, Sydney, Australia, April 28 - May 2

First Page

73

Last Page

76

Identifier

10.1145/3701716.3715869

Publisher

ACM

City or Country

New York

Additional URL

https://dl.acm.org/doi/10.1145/3701716.3715869

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