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Should models ask for clarification instead of guessing?
A broader line of inquiry — a family of 29 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 29
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 making assumptions?
- Should LLMs query users back when presented with under-specified scenarios?
- Can LLMs learn to ask clarifying questions instead of guessing?
- Can language models ask clarifying questions when sentences are ambiguous?
- Do models naturally learn to ask clarifying questions without explicit supervision?
- Can language systems learn when to ask for clarification instead of choosing one reading?
- What makes a clarifying question aligned with user interests versus structurally sound?
- What makes some clarifying questions more useful than others?
- Why do specific clarifying questions outperform generic requests for clarity?
- What training approach enables models to proactively request clarification?
- Can models learn to identify what information is missing from questions?
- What structural changes enable agents to ask clarifying questions?
- When should agents use clarification commands instead of assuming intent?
- Why might expressed satisfaction with explanations diverge from actual cognitive clarity?
- Why do pretrained retrievers struggle with ambiguous or implicit queries?
- Can language models understand the implicit emotional intent behind questions?
- Why do models struggle with asking questions in multi-turn conversational reasoning tasks?
- Which types of clarifying questions actually help users versus wasting their time?
- How does ambiguity detection connect to models' ability to ask clarifying questions?
- How do humans decide which level of clarification to request?
- Why do weaker language models fail at multi-turn strategic questioning?
- Why do language models prefer certain response styles regardless of what the prompt asks?
- Why do published prose training data omit solicitation as a discourse property?
- Can testing prior knowledge and checking understanding improve explanation outcomes?
- Why do specific clarifying questions outperform rephrased versions of user needs?
- What makes a first answer so often the best answer a model produces?
- Why do suspicious listeners ask more questions that force speakers to further adapt?
- Why does document perplexity stay low while question-answering accuracy drops?
- What percentage of natural language relies on plausible deniability through ambiguous phrasing?