  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.
