mlsanityData to 14 Oct 2026

Query Understanding in LLM-based Conversational Information Seeking

arXiv 2504.06356 ↗First posted 8 Apr 2025cs.CLcited by 5

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 40 of its 61 references that are in the map. First posted on arXiv 8 Apr 2025.

Builds on

40 of 61 references.

References · 40

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2018QuAC : Question Answering in Contextcited by 381
  3. 2023Query2doc: Query Expansion with Large Language Modelscited by 149
  4. 2020Open-Domain Question Answering Goes Conversational via Question Rewritingcited by 148
  5. 2023User Behavior Simulation with Large Language Model based Agentscited by 140
  6. 2019Asking Clarifying Questions in Open-Domain Information-Seeking Conversationscited by 139
  7. 2023Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaborationcited by 116
  8. 2019Deeper Text Understanding for IR with Contextual Neural Language Modelingcited by 113
  9. 2024STaR-GATE: Teaching Language Models to Ask Clarifying Questionscited by 104
  10. 2020Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systemscited by 98
  11. 2021TopiOCQA: Open-domain Conversational Question Answering with Topic Switchingcited by 96
  12. 2020Question Rewriting for Conversational Question Answeringcited by 93
28 more
  1. 2020Few-Shot Generative Conversational Query Rewritingcited by 85
  2. 2023Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Searchcited by 75
  3. 2023Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactionscited by 70
  4. 2023Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewritingcited by 69
  5. 2020TREC CAsT 2019: The Conversational Assistance Track Overviewcited by 64
  6. 2021Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questionscited by 63
  7. 2023ConvGQR: Generative Query Reformulation for Conversational Searchcited by 59
  8. 2024Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Trainingcited by 57
  9. 2023Grounding Gaps in Language Model Generationscited by 47
  10. 2023Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestioncited by 43
  11. 2023User Simulation for Evaluating Information Access Systemscited by 31
  12. 2021Conversational Fashion Image Retrieval via Multiturn Natural Language Feedbackcited by 29
  13. 2022INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactionscited by 27
  14. 2022Evaluating Mixed-initiative Conversational Search Systems via User Simulationcited by 25
  15. 2024BASES: Large-scale Web Search User Simulation with Large Language Model based Agentscited by 25
  16. 2023Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyondcited by 24
  17. 2023An In-depth Investigation of User Response Simulation for Conversational Searchcited by 20
  18. 2024Leveraging LLMs for Unsupervised Dense Retriever Rankingcited by 19
  19. 2024TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistantscited by 17
  20. 2024Can We Use Large Language Models to Fill Relevance Judgment Holes?cited by 17
  21. 2024STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agentscited by 17
  22. 2020Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Searchcited by 13
  23. 2021Analysing Mixed Initiatives and Search Strategies during Conversational Searchcited by 13
  24. 2023Experience and Evidence are the eyes of an excellent summarizer! Towards Knowledge Infused Multi-modal Clinical Conversation Summarizationcited by 8
  25. 2024Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Searchcited by 8
  26. 2024Dynamic Contexts for Generating Suggestion Questions in RAG Based Conversational Systemscited by 7
  27. 2022McQueen: a Benchmark for Multimodal Conversational Query Rewritecited by 6
  28. 2023An Interactive Query Generation Assistant using LLM-based Prompt Modification and User Feedbackcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025In-Context Examples Matter: Improving Emotion Recognition in Conversation with Instruction Tuningcited by 1
  2. 2025Sherlock Your Queries: Learning to Ask the Right Questions for Dialogue-Based Retrievalcited by 1
  3. 2026Enhancing Conversational Agents via Task-Oriented Adversarial Memory Adaptationcited by 1
  4. 2026When to Ask a Question: Understanding Communication Strategies in Generative AI Toolscited by 0
  5. 2026Training User Simulators with Latent Intrinsic Feedbackcited by 0