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.

社会选择理论在传统上只适用于在少数预定的选择中进行选择，而不适用于更复杂的决策，如集体选择一项文本陈述。我们引入生成式社会选择，这是一个将社会选择理论的数学严谨性与大型语言模型生成文本和推断偏好的能力相结合的框架。这个框架将增强人工智能民主进程的设计分为两个组成部分：首先，证明在具有oracle查询条件下，该过程满足严格的表征保证；其次，通过实验证实可以使用大型语言模型近似实现这些查询。我们通过将这个框架应用于生成一系列陈述的问题来说明它，这些陈述代表以自由形式文本表达的意见，例如在线协商过程中。

生成社会选择