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 large language models' capability 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 illustrate this framework by applying it to the problem of generating a slate of statements that is representative of opinions expressed as free-form text, for instance in an online deliberative process.

翻译：传统上，社会选择理论仅适用于对少数预定备选方案的选择，而无法处理更复杂的决策，例如集体选择文本陈述。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推断偏好的能力相结合。该框架将人工智能增强型民主进程的设计分为两个部分：首先，证明该进程在访问预言机查询时满足严格的代表性保证；其次，通过实证验证这些查询可使用大语言模型近似实现。我们通过将该框架应用于生成一组代表性陈述的问题来加以说明——这些陈述需反映以自由形式文本表达的观点（例如在线审议过程）。