One More Turn, Less Regret: A Regret-Based Multi-Turn Benchmark for LLMs' Clarification Policies
Ambiguous user requests make clarification a sequential decision problem for conversational LLM assistants: they must decide whether to ask, what to ask, when to stop, and when to answer. We introduce RegretBench, a multi-turn benchmark that evaluates clarification as policy behavior rather than isolated question quality. RegretBench provides a hidden-intent formulation of ambiguity, supports free-form interaction grounded in semantic-state tracking, and introduces a regret-based objective that measures how much value a model loses relative to a reference clarification policy. Experiments on o
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- PossiblePossibly related (embedding) · 47%Evaluating long-term memory limits in stateless LLM chatbots — feedback needed [D] →
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%open-webui/open-webui →
“Shared author/contributor keys: nguyen”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
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- FuzzySimilar title/name (fuzzy) · 59%jeinlee1991/chinese-llm-benchmark →
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- LinkedLinked via arxiv author · 85%Minh Ngoc Ta →
“One More Turn, Less Regret: A Regret-Based Multi-Turn Benchmark for LLMs' Clarification Policies”
- LinkedLinked via arxiv author · 85%My Anh Tran Nguyen →
“One More Turn, Less Regret: A Regret-Based Multi-Turn Benchmark for LLMs' Clarification Policies”
- LinkedLinked via arxiv author · 85%Duong D. Nguyen →
“One More Turn, Less Regret: A Regret-Based Multi-Turn Benchmark for LLMs' Clarification Policies”