Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2025

Abstract

Query understanding in CIS involves accurately interpreting user intent through context-aware interactions. This includes resolving ambiguities, refining queries, and adapting to evolving information needs. LLM 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 LLM 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 information seeking system

Discipline

Artificial Intelligence and Robotics | Programming Languages and Compilers

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

SIGIR '25: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, Padua Italy, July 13 - 18

First Page

4098

Last Page

4101

Identifier

10.1145/3726302.3731687

Publisher

ACM

City or Country

New York

Additional URL

https://dl.acm.org/doi/10.1145/3726302.3731687

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