{"ID":2877359,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.21184","arxiv_id":"2508.21184","title":"BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design","abstract":"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.","short_abstract":"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 ag...","url_abs":"https://arxiv.org/abs/2508.21184","url_pdf":"https://arxiv.org/pdf/2508.21184v3","authors":"[\"Deepro Choudhury\",\"Sinead Williamson\",\"Adam Goliński\",\"Ning Miao\",\"Freddie Bickford Smith\",\"Michael Kirchhof\",\"Yizhe Zhang\",\"Tom Rainforth\"]","published":"2025-08-28T19:51:43Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"stat.ML\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}