As Large Language Models (LLMs) are integrated into critical real-world
applications, their strategic and logical reasoning abilities are increasingly
crucial. This paper evaluates LLMs' reasoning abilities in competitive
environments through game-theoretic tasks, e.g., board and card games that
require pure logic and strategic reasoning to compete with opponents. We first
propose GTBench, a language-driven environment composing 10 widely-recognized
tasks, across a comprehensive game taxonomy: complete versus incomplete
information, dynamic versus static, and probabilistic versus deterministic
scenarios. Then, we investigate two key problems: (1) Characterizing
game-theoretic reasoning of LLMs; (2) LLM-vs-LLM competitions as reasoning
evaluation. We observe that (1) LLMs have distinct behaviors regarding various
gaming scenarios; for example, LLMs fail in complete and deterministic games
yet they are competitive in probabilistic gaming scenarios; (2) Open-source
LLMs, e.g., CodeLlama-34b-Instruct, are less competitive than commercial LLMs,
e.g., GPT-4, in complex games. In addition, code-pretraining greatly benefits
strategic reasoning, while advanced reasoning methods such as Chain-of-Thought
(CoT) and Tree-of-Thought (ToT) do not always help. Detailed error profiles are
also provided for a better understanding of LLMs' behavior.

通过博弈论任务以及与其他模型的比赛来评估大型语言模型在竞争环境中的推理能力，研究发现大型语言模型在游戏场景中的表现因任务类型而异，然而，开源的模型相较于商业模型在复杂游戏中表现不佳。