Line of inquiry
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How can models identify insufficient information and respond appropriately without guessing?
A broader line of inquiry — a family of 39 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 39
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- 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 LLMs learn to ask clarifying questions instead of guessing?
- How do reasoning improvements suppress a model's ability to abstain?
- Can models learn to ask clarifying questions instead of making assumptions?
- Can models learn to identify what information is missing from questions?
- Do reasoning models overthink ill-posed questions instead of recognizing incompleteness?
- Can reasoning models reject ill-posed questions or do they overthink?
- Can proactive critical thinking alone enable models to request clarification effectively?
- Why do reasoning models confidently generate wrong answers instead of abstaining?
- Do models trained for reasoning lose their ability to decline questions?
- Does training for better reasoning reduce an AI system's ability to abstain?
- Do models naturally learn to ask clarifying questions without explicit supervision?
- What training approach enables models to proactively request clarification?
- Can models identify what information they are missing in underspecified problems?
- Can models identify what information they are missing in underspecified tasks?
- How does proactive critical thinking enable models to identify missing information?
- Why do models detect false assumptions but still fail to correct them appropriately?
- Can models distinguish between activated knowledge and genuine reasoning?
- Does reasoning training actively undermine the abstention capacity safety training created?
- Can proactive critical thinking train models to request clarification actively?
- How should safety training and reasoning training balance abstention differently?
- How does proactive critical thinking detect when information is incomplete?
- Can AI systems identify important unanswered questions that emerge during reasoning?
- What structural changes enable agents to ask clarifying questions?
- What makes a model refuse to answer without evidence present?
- Why do models confirm seeing hints but rarely mention them unprompted?
- How can agents detect missing information before attempting to solve problems?
- Can a model predict the right action but execute the wrong one?
- Why do models struggle with asking questions in multi-turn conversational reasoning tasks?
- How do models signal knowledge gaps through token probability?
- Can models distinguish between ambiguous and incomplete information inputs?
- What happens when reasoning fine-tuning eliminates model refusal mechanisms entirely?
- What training signals would teach models when not to reason?
- What makes abstention a learnable behavior instead of a default penalty?
- How does ambiguity detection connect to models' ability to ask clarifying questions?
- Can question-only features replace model uncertainty checks at scale?
- What alternatives exist when required knowledge is absent from training?
- Can abstention behavior transfer from small models to frontier models?