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.

本研究分析了GPT-3.5在多种语言中对奖励和时间的偏好，发现当以德语和汉语等未来时态参照性较弱的语言提问时，GPT表现出更高的耐心，并且发现虽然GPT可以捕捉到跨语言的变异，但它们所做出的选择并不符合人类决策者的选择。

语言、时间偏好与消费行为：基于大型语言模型的证据