Since the introduction of ChatGPT and GPT-4, these models have been tested across a large number of tasks. Their adeptness across domains is evident, but their aptitude in playing games and specifically their aptitude in the realm of poker has remained unexplored. Poker is a game that requires decision making under uncertainty and incomplete information. In this paper, we put ChatGPT and GPT-4 through the poker test and evaluate their poker skills. Our findings reveal that while both models display an advanced understanding of poker, encompassing concepts like the valuation of starting hands, playing positions and other intricacies of game theory optimal (GTO) poker, both ChatGPT and GPT-4 are NOT game theory optimal poker players. Through a series of experiments, we first discover the characteristics of optimal prompts and model parameters for playing poker with these models. Our observations then unveil the distinct playing personas of the two models. We first conclude that GPT-4 is a more advanced poker player than ChatGPT. This exploration then sheds light on the divergent poker tactics of the two models: ChatGPT's conservativeness juxtaposed against GPT-4's aggression. In poker vernacular, when tasked to play GTO poker, ChatGPT plays like a Nit, which means that it has a propensity to only engage with premium hands and folds a majority of hands. When subjected to the same directive, GPT-4 plays like a maniac, showcasing a loose and aggressive style of play. Both strategies, although relatively advanced, are not game theory optimal.

翻译：摘要：自ChatGPT和GPT-4问世以来，这些模型已在大量任务中接受测试。它们跨领域的熟练程度显而易见，但在游戏竞技，特别是扑克领域的表现尚未被探索。扑克是一种需要在信息不完整和不确定条件下做出决策的游戏。本文通过扑克测试对ChatGPT和GPT-4的扑克技能进行了评估。研究结果揭示，尽管两个模型都展现出对扑克的深入理解，包括起手牌估值、位置玩法及博弈论最优（GTO）扑克的复杂概念，但ChatGPT和GPT-4均非博弈论最优的扑克玩家。通过一系列实验，我们首先发现了使用这些模型进行扑克游戏的最佳提示词和模型参数特征。随后，我们的观察揭示了两个模型截然不同的游戏风格。我们首先得出结论，GPT-4是比ChatGPT更高级的扑克玩家。这一探索进一步揭示了两个模型扑克策略的分歧：ChatGPT的保守性与GPT-4的激进性形成鲜明对比。用扑克术语来说，当被要求执行GTO扑克策略时，ChatGPT表现得像个"紧弱型玩家"，即倾向于仅参与优质手牌并放弃大部分手牌；而GPT-4在相同指令下则表现得像个"疯狂型玩家"，展现出松散激进的游戏风格。尽管这两种策略相对高级，但均不是博弈论最优策略。