用博弈论提升大模型在信息缺失时的主动提问能力。
Game of Thought: Robust Information Seeking with Large Language Models Using Game Theory
- 将信息搜索建模为零和博弈，设计对抗性评估框架。
- 在二十问游戏中显著提升最坏情况下的表现。
- 适合高风险场景下需要可靠决策的AI系统使用。
大型语言模型（LLMs）在现实应用中常因信息不足而无法完成任务，主动获取缺失信息的能力至关重要。现有方法多依赖简化假设，导致最坏情况性能下降，影响高风险场景应用。本文以二十问游戏为基准，提出对抗性信息搜索问题（SLS），将其形式化为双人零和扩展式博弈。我们提出博弈思维（GoT）框架，利用博弈论技术近似求解受限版本博弈的纳什均衡策略。实验表明，相比直接提示法与启发式搜索方法，该方法在所有测试设置下均显著提升最坏情况下的表现。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly deployed in real-world scenarios where they may lack sufficient information to complete a given task. In such settings, the ability to actively seek out missing information becomes a critical capability. Existing approaches to enhancing this ability often rely on simplifying assumptions that degrade \textit{worst-case} performance. This is an issue with serious implications in high-stakes applications. In this work, we use the game of Twenty Questions to evaluate the information-seeking ability of LLMs. We introduce and formalize its adversarial counterpart, the Strategic Language Search (SLS) problem along with its variants as a two-player zero-sum extensive form game. We propose Game of Thought (GoT), a framework that applies game-theoretic techniques to approximate a Nash equilibrium (NE) strategy for the restricted variant of the game. Empirical results demonstrate that our approach consistently improves worst-case performance compared to (1) direct prompting-based methods and (2) heuristic-guided search methods across all tested settings.
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