思维博弈：利用博弈论的大型语言模型鲁棒信息检索
Game of Thought: Robust Information Seeking with Large Language Models Using Game Theory
AI总结：
本文提出Game of Thought框架，通过博弈论技术提升大型语言模型在信息检索中的鲁棒性，实验证明其在最坏情况下的性能优于传统方法。
摘要：
大型语言模型（LLMs）越来越多地应用于现实世界场景，其中它们可能缺乏完成给定任务所需的足够信息。在这样的设置中，主动寻找缺失信息的能力成为一项关键能力。现有的增强这种能力的方法往往依赖于简化假设，这些假设会降低最坏情况下的性能。这在高风险应用中有着严重的影响。在本工作中，我们利用“二十问”游戏来评估LLMs的信息检索能力。我们引入并形式化其对抗性 counterpart，即战略语言搜索（SLS）问题及其变体作为双人零和扩展形式博弈。我们提出了思维博弈（GoT）框架，该框架应用博弈论技术来近似该游戏中受限变体的纳什均衡（NE）策略。实验证果表明，我们的方法在所有测试设置中都比（1）直接提示方法和（2）启发式引导搜索方法在最坏情况下表现更优。
英文摘要：
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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