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