This paper investigates the strategic decision-making capabilities of three
Large Language Models (LLMs): GPT-3.5, GPT-4, and LLaMa-2, within the framework
of game theory. Utilizing four canonical two-player games -- Prisoner's
Dilemma, Stag Hunt, Snowdrift, and Prisoner's Delight -- we explore how these
models navigate social dilemmas, situations where players can either cooperate
for a collective benefit or defect for individual gain. Crucially, we extend
our analysis to examine the role of contextual framing, such as diplomatic
relations or casual friendships, in shaping the models' decisions. Our findings
reveal a complex landscape: while GPT-3.5 is highly sensitive to contextual
framing, it shows limited ability to engage in abstract strategic reasoning.
Both GPT-4 and LLaMa-2 adjust their strategies based on game structure and
context, but LLaMa-2 exhibits a more nuanced understanding of the games'
underlying mechanics. These results highlight the current limitations and
varied proficiencies of LLMs in strategic decision-making, cautioning against
their unqualified use in tasks requiring complex strategic reasoning.

通过四个典型的双人博弈游戏，研究了 GPT-3.5、GPT-4 和 LLaMa-2 这三种大型语言模型在战略决策能力方面，同时考察了上下文框架对模型决策的影响，结果发现 LLM 在战略决策方面存在限制和熟练程度差异，不宜在需要复杂战略推理的任务中不加区分地使用。