Language has a strong influence on our perceptions of time and rewards. This raises the question of whether large language models, when asked in different languages, show different preferences for rewards over time and if their choices are similar to those of humans. In this study, we analyze the responses of GPT-3.5 (hereafter referred to as GPT) to prompts in multiple languages, exploring preferences between smaller, sooner rewards and larger, later rewards. Our results show that GPT displays greater patience when prompted in languages with weak future tense references (FTR), such as German and Mandarin, compared to languages with strong FTR, like English and French. These findings are consistent with existing literature and suggest a correlation between GPT's choices and the preferences of speakers of these languages. However, further analysis reveals that the preference for earlier or later rewards does not systematically change with reward gaps, indicating a lexicographic preference for earlier payments. While GPT may capture intriguing variations across languages, our findings indicate that the choices made by these models do not correspond to those of human decision-makers.

翻译：语言对我们感知时间和奖励的方式具有重大影响。这引出一个问题：当大型语言模型以不同语言接受提问时，是否会对跨期奖励表现出不同的偏好，且其选择是否与人类相似？本研究分析了GPT-3.5（以下简称GPT）对多种语言提示的回答，探讨其在较小-即时奖励与较大-延迟奖励之间的偏好。结果显示，当使用弱将来时态指涉（FTR）的语言（如德语和中文）进行提示时，GPT表现出比使用强FTR语言（如英语和法语）时更强的耐心。这些发现与现有文献一致，表明GPT的选择与这些语言使用者偏好之间存在相关性。然而，进一步分析表明，对不同时间点奖励的偏好并未随奖励差距系统性变化，这反映出对提前支付的词典式偏好。尽管GPT可能捕捉到语言间的有趣差异，但我们的研究结果表明，这些模型所作的选择与人类决策者的选择并不对应。