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：语言对我们的时间和奖励感知具有强烈影响。这引发了一个问题：当使用不同语言提问时，大语言模型是否会在奖励的时间偏好上表现出差异，以及其选择是否与人类相似。在本研究中，我们分析了GPT-3.5（以下简称GPT）对多种语言提示的响应，探讨了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）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的偏好存在关联。然而，进一步分析揭示，对早期或晚期奖励的偏好并未随奖励差距系统性变化，表明存在一种对早期支付的字典序偏好。尽管GPT可能捕捉到语言间的有趣差异，我们的研究结果表明，这些模型做出的选择与人类决策者的选择并不对应。