CLARITI uses Shapley-based empirical rewards in RL to ask fewer but more effective clarifying questions in SE tasks, matching GPT-5 performance with 41% fewer questions.
Distributional Data Analysis Findings Here we present the complete results from our D5 analysis comparing answerable versus non-answerable clarification questions
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Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks
CLARITI uses Shapley-based empirical rewards in RL to ask fewer but more effective clarifying questions in SE tasks, matching GPT-5 performance with 41% fewer questions.