  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.
