CA-BED: Conversation-Aware Bayesian Experimental Design

• URL: http://arxiv.org/abs/2606.01182v1
• Date: Sun, 31 May 2026 11:46:43 GMT
• Title: CA-BED: Conversation-Aware Bayesian Experimental Design
• Abstract summary: A key challenge lies in selecting questions that reduce uncertainty while incorporating responses that may be ambiguous or only partially informative.<n>We propose Conversation-Aware Bayesian Experimental Design (CA-BED), an inference-time probabilistic dialog planning framework.<n>It integrates Bayesian Experimental Design with LLM-based likelihood estimation to optimize question selection over multiple conversational turns.
• Score: 6.186706480519743
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) excel at static reasoning tasks, yet their performance often degrades in interactive scenarios where information must be actively acquired through questioning. A key challenge lies in selecting questions that reduce uncertainty while incorporating responses that may be ambiguous or only partially informative. To address this, we propose Conversation-Aware Bayesian Experimental Design (CA-BED), an inference-time probabilistic dialog planning framework that integrates Bayesian Experimental Design with LLM-based likelihood estimation to optimize question selection over multiple conversational turns. CA-BED maintains a belief distribution over hypotheses, anticipates possible answers, and propagates expected information gain through a simulated conversation tree. Across two structured entity-deduction benchmarks, CA-BED yields an average 21.8% improvement in success rates over direct prompting, with comparable gains relative to alternative information-seeking methods. It achieves these gains with an average increase of only 1.8 conversational turns compared to direct prompting.

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