Game of Thought: Robust Information Seeking with Large Language Models Using Game Theory

• URL: http://arxiv.org/abs/2602.01708v1
• Date: Mon, 02 Feb 2026 06:33:18 GMT
• Title: Game of Thought: Robust Information Seeking with Large Language Models Using Game Theory
• Authors: Langyuan Cui, Chun Kai Ling, Hwee Tou Ng,
• Abstract summary: We use the game of Twenty Questions to evaluate the information-seeking ability of Large Language Models (LLMs)<n>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.
• Score: 37.51238507036326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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