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How can models learn to ask clarifying questions effectively?
A broader line of inquiry — a family of 42 specific questions the research asks around this. Follow one into its inquiring-line page, or move sideways to a related line below.
Questions in this line of inquiry 42
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- Can models learn to ask clarifying questions instead of answering prematurely?
- Can LLMs learn to ask clarifying questions instead of guessing?
- Can models learn to ask clarifying questions instead of making assumptions?
- Can models learn to stop thinking when a question lacks necessary information?
- Can models identify information gaps without just guessing or refusing to answer?
- Can proactive critical thinking alone enable models to request clarification effectively?
- Can models learn to identify what information is missing from questions?
- Do models naturally learn to ask clarifying questions without explicit supervision?
- What training approach enables models to proactively request clarification?
- Do models fail to identify what information they need without guidance?
- Should LLMs query users back when presented with under-specified scenarios?
- Can language systems learn when to ask for clarification instead of choosing one reading?
- Can models identify what information they are missing in underspecified problems?
- What structural changes enable agents to ask clarifying questions?
- Can question quality be trained separately from the decision to ask?
- Can models identify what information they are missing in underspecified tasks?
- Can proactive critical thinking train models to request clarification actively?
- How does proactive critical thinking enable models to identify missing information?
- Why do specific clarifying questions outperform generic requests for clarity?
- What makes some clarifying questions more useful than others?
- Can Q-priming further strengthen clarifying question behavior beyond social meta-learning alone?
- Why do models struggle with asking questions in multi-turn conversational reasoning tasks?
- How does ambiguity detection connect to models' ability to ask clarifying questions?
- Can dialogue systems abstain from responding when uncertainty is too high?
- How does proactive critical thinking detect when information is incomplete?
- Can question-only features replace model uncertainty checks at scale?
- Can models distinguish between ambiguous and incomplete information inputs?
- How might dual-process dialogue use information gain to trigger clarification?
- How does asymmetric information shape what to ask users first?
- Why do weaker language models fail at multi-turn strategic questioning?
- How can models select the optimal question to ask given multiple uncertainties?
- Which types of clarifying questions actually help users versus wasting their time?
- What makes a model refuse to answer without evidence present?
- What makes specific-facet questions outperform generic need-rephrasing requests?
- Which failure modes dominate when models handle underspecified requests?
- What alternatives exist when required knowledge is absent from training?
- Can clarification questions alone match the stability gains of previews plus questions?
- Why do specific clarifying questions outperform rephrased versions of user needs?
- What training signals would teach models when not to reason?
- Why do missing survey responses need careful handling in risk assessment?
- Why does document perplexity stay low while question-answering accuracy drops?
- Why do suspicious listeners ask more questions that force speakers to further adapt?