mlsanityData to 7 Oct 2026

Interactive Classification by Asking Informative Questions

arXiv 1911.03598 ↗First posted 9 Nov 2019cs.CL cs.HC cs.IRcited by 16

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Abstract (arXiv)

We study the potential for interaction in natural language classification. We add a limited form of interaction for intent classification, where users provide an initial query using natural language, and the system asks for additional information using binary or multi-choice questions. At each turn, our system decides between asking the most informative question or making the final classification prediction.The simplicity of the model allows for bootstrapping of the system without interaction data, instead relying on simple crowdsourcing tasks. We evaluate our approach on two domains, showing the benefit of interaction and the advantage of learning to balance between asking additional questions and making the final prediction.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Nov 2019.

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Citing papers per year: 2021: 3; 2022: 1; 2023: 3; 2024: 4; 2025: 4; 2026: 1.

16 papers.

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  1. 2023Clarify When Necessary: Resolving Ambiguity Through Interaction with LMscited by 93
  2. 2024Modeling Future Conversation Turns to Teach LLMs to Ask Clarifying Questionscited by 70
  3. 2023Active Preference Inference using Language Models and Probabilistic Reasoningcited by 29
  4. 2021Open-domain clarification question generation without question examplescited by 20
  5. 2024CLARINET: Augmenting Language Models to Ask Clarification Questions for Retrievalcited by 18
  6. 2024Contextualized Evaluations: Judging Language Model Responses to Underspecified Queriescited by 12
  7. 2025InteractComp: Evaluating Search Agents With Ambiguous Queriescited by 12
  8. 2021Database Search Results Disambiguation for Task-Oriented Dialog Systemscited by 9
  9. 2025Shoot First, Ask Questions Later? Building Rational Agents that Explore and Act Like Peoplecited by 7
  10. 2022Closed-book Question Generation via Contrastive Learningcited by 6
  11. 2023Asking More Informative Questions for Grounded Retrievalcited by 6
  12. 2024MAPLE: A Framework for Active Preference Learning Guided by Large Language Modelscited by 6
4 more
  1. 2025Sherlock Your Queries: Learning to Ask the Right Questions for Dialogue-Based Retrievalcited by 1
  2. 2021Neural Abstructions: Abstractions that Support Construction for Grounded Language Learningcited by 0
  3. 2025Which Questions Improve Learning the Most? Utility Estimation of Questions with LM-based Simulationscited by 0
  4. 2026LAVOIR: Teaching a Single-Pass Decision Encoder When and What to Ask with Amortized Value of Informationcited by 0