让大模型像侦探一样主动提问，高效获取用户信息
BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design
- 用贝叶斯实验设计优化提问策略，每步选最有价值的问题
- 在20个问题游戏中显著优于传统提示法，信息获取效率提升明显
- 适合需要精准交互的场景，如个性化推荐、智能客服
我们提出一种通用方法——BED-LLM（基于大语言模型的贝叶斯实验设计），利用序列贝叶斯实验设计框架，使大语言模型能够智能、自适应地从用户或其他外部源收集信息。该方法通过迭代选择能最大化对感兴趣变量的期望信息增益（EIG）的问题，实现高效交互。我们基于大模型的预测分布构建概率模型，以合理形式化并估计EIG，深入分析其构建与更新过程中的关键决策。实验表明，在20个问题游戏和用户偏好主动推断任务中，相较于纯提示生成法及其他自适应策略，BED-LLM在多种测试中均取得显著性能提升。
原文摘要 · Abstract (English)
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.
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