mlsanityData to 14 Oct 2026

A Survey on Asking Clarification Questions Datasets in Conversational Systems

arXiv 2305.15933 ↗First posted 25 May 2023cs.IRcited by 28

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Abstract (arXiv)

The ability to understand a user's underlying needs is critical for conversational systems, especially with limited input from users in a conversation. Thus, in such a domain, Asking Clarification Questions (ACQs) to reveal users' true intent from their queries or utterances arise as an essential task. However, it is noticeable that a key limitation of the existing ACQs studies is their incomparability, from inconsistent use of data, distinct experimental setups and evaluation strategies. Therefore, in this paper, to assist the development of ACQs techniques, we comprehensively analyse the current ACQs research status, which offers a detailed comparison of publicly available datasets, and discusses the applied evaluation metrics, joined with benchmarks for multiple ACQs-related tasks. In particular, given a thorough analysis of the ACQs task, we discuss a number of corresponding research directions for the investigation of ACQs as well as the development of conversational systems.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 May 2023.

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Citing papers per year: 2023: 3; 2024: 8; 2025: 12; 2026: 5.

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  1. 2024Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Modelscited by 154
  2. 2025CollabLLM: From Passive Responders to Active Collaboratorscited by 124
  3. 2024CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Modelscited by 54
  4. 2023Grounding Gaps in Language Model Generationscited by 47
  5. 2024AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queriescited by 35
  6. 2025Navigating Rifts in Human-LLM Grounding: Study and Benchmarkcited by 29
  7. 2024STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agentscited by 17
  8. 2025Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models is Missingcited by 14
  9. 2025HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistantscited by 11
  10. 2024ProductAgent: Benchmarking Conversational Product Search Agent with Asking Clarification Questionscited by 10
  11. 2025InfoQuest: Evaluating Multi-Turn Dialogue Agents for Open-Ended Conversations with Hidden Contextcited by 8
  12. 2024Dynamic Contexts for Generating Suggestion Questions in RAG Based Conversational Systemscited by 7
16 more
  1. 2025Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Actioncited by 6
  2. 2024Taking Action Towards Graceful Interaction: The Effects of Performing Actions on Modelling Policies for Instruction Clarification Requestscited by 4
  3. 2025Curiosity by Design: An LLM-based Coding Assistant Asking Clarification Questionscited by 4
  4. 2026IDRBench: Benchmarking the Interactive Capabilities of Deep Research Agentscited by 4
  5. 2025Synthetic Clarification and Correction Dialogues about Data-Centric Tasks -- A Teacher-Student Approachcited by 3
  6. 2025CLEAR-KGQA: Clarification-Enhanced Ambiguity Resolution for Knowledge Graph Question Answeringcited by 3
  7. 2023Alexpaca: Learning Factual Clarification Question Generation Without Examplescited by 2
  8. 2026What Users Leave Unsaid: Under-Specified Queries Limit Vision-Language Modelscited by 2
  9. 2024Generating Clarification Questions for Disambiguating Contractscited by 1
  10. 2025Correct-Detect: Balancing Performance and Ambiguity Through the Lens of Coreference Resolution in LLMscited by 1
  11. 2025From Answers to Guidance: A Proactive Dialogue System for Legal Documentscited by 1
  12. 2026WASIL: In-the-Wild Arabic Spoken Interactions with LLMscited by 1
  13. 2023Lexical Entrainment for Conversational Systemscited by 0
  14. 2025Dagstuhl Perspectives Workshop 24352 -- Conversational Agents: A Framework for Evaluation (CAFE): Manifestocited by 0
  15. 2026Knowing When to Ask: Self-Gated Clarification for Hierarchical Language Agentscited by 0
  16. 2026Turning Intent into Specifications: A Benchmark and an Interactive User-Assistant Agentcited by 0