Traditionally, social choice theory has only been applicable to choices among a few predetermined alternatives but not to more complex decisions such as collectively selecting a textual statement. We introduce generative social choice, a framework that combines the mathematical rigor of social choice theory with the capability of large language models to generate text and extrapolate preferences. This framework divides the design of AI-augmented democratic processes into two components: first, proving that the process satisfies rigorous representation guarantees when given access to oracle queries; second, empirically validating that these queries can be approximately implemented using a large language model. We apply this framework to the problem of generating a slate of statements that is representative of opinions expressed as free-form text; specifically, we develop a democratic process with representation guarantees and use this process to represent the opinions of participants in a survey about chatbot personalization. We find that 93 out of 100 participants feel "mostly" or "perfectly" represented by the slate of five statements we extracted.

翻译：传统社会选择理论仅适用于在少数预设选项间进行选择，而无法应对诸如集体撰写文本陈述等更复杂的决策。本文提出生成式社会选择框架，该框架将社会选择理论的数学严谨性与大语言模型生成文本及外推偏好的能力相结合。该框架将人工智能增强型民主进程的设计划分为两个部分：首先，证明该进程在获取预言机查询时具有严格的代表性保障；其次，实证验证这些查询可通过大语言模型近似实现。我们将该框架应用于生成能代表自由文本观点表述的陈述清单问题——具体而言，我们设计了一个具有代表性保障的民主进程，并运用该进程呈现某聊天机器人个性化调查中参与者的观点。研究发现，在100名参与者中，93人认为我们从其观点中提取的五条陈述清单能"大部分"或"完全"代表其立场。