CA-BED: Conversation-Aware Bayesian Experimental Design
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, anticipa...
| Authors | Daniel Arnould, Rashad Aziz, Zixuan Kang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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