Auctions with LLM Summaries

We study an auction setting in which bidders bid for placement of their content within
a summary generated by a large language model (LLM), e.g., an ad auction in which the
display is a summary paragraph of multiple ads. This generalizes the classic ad settings such
as position auctions to an LLM generated setting, which allows us to handle general display
formats. We propose a novel factorized framework in which an auction module and an LLM
module work together via a prediction model to provide welfare maximizing summary outputs
in an incentive compatible manner. We provide a theoretical analysis of this framework and
synthetic experiments to demonstrate the feasibility and validity of the system together with
welfare comparisons.