BED-LLM: intelligent information gathering with LLMs and Bayesian experimental design

Conference item : Poster

We propose a general-purpose approach for improving the ability of large language models (LLMs) to intelligently and adaptively gather information from a user or other external source using the framework of sequential Bayesian experimental design (BED). This enables LLMs to act as effective multi-turn conversational agents and interactively interface with external environments. Our approach, which we call BED-LLM (Bayesian experimental design with large language models), is based on iteratively choosing questions or queries that maximize the expected information gain (EIG) with respect to a variable of interest given the responses gathered previously. We show how this EIG can be formulated (and then estimated) in a principled way using a probabilistic model derived from the LLM's predictive distributions and provide detailed insights into key decisions in its construction and updating procedure. We find that BED-LLM achieves substantial gains in performance across a wide range of tests based on the 20 Questions game and using the LLM to actively infer user preferences, compared to purely prompting-based design generation and other adaptive design strategies.

Authors: Choudhury, D; Williamson, S; Golinski, A; Miao, N; Bickford Smith, F

• Publication status: Accepted
• Peer review status: Peer reviewed
• Publication date: 2026-04-01
• Acceptance date: 2026-01-26
• Event title: Fourteenth International Conference on Learning Representations
• Event location: Rio de Janeiro, Brazil
• Event website: https://iclr.cc/Conferences/2026
• Event start date: 2026-04-23
• Event end date: 2026-04-27
• Language: English
• Keywords: question asking; Bayesian experimental design; experimental design; BED; expected information gain; EIG; information gain; Bayesian; uncertainty; LLM; conversational agent; clarification
• Subtype: Poster