research
An Agent Learned When to Ask First
Counterfactual information gain guided clarification on ambiguous requests.
Summary
Counterfactual information gain guided clarification on ambiguous requests.
CIGAsk trains language models to decide when a question is needed and how to make that question informative. It combines a reward for information gained from the user's answer with an ambiguity-aware reward for asking at the right time. The authors report gains across three clarification benchmarks. The result addresses tested ambiguous-query settings, not every conversational task.
Why it matters
Counterfactual information gain guided clarification on ambiguous requests.
Limits and context
- The result addresses tested ambiguous-query settings, not every conversational task.
Key claims
Counterfactual information gain guided clarification on ambiguous requests.
Qualification: The result addresses tested ambiguous-query settings, not every conversational task.
Evidence: source-2026-09-22-017
Sources
- arXiv preprint 2609.24290arXiv · primary research
Corrections
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