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.
@article{arxiv.2404.08126,
title = {Auctions with LLM Summaries},
author = {Kumar Avinava Dubey and Zhe Feng and Rahul Kidambi and Aranyak Mehta and Di Wang},
journal= {arXiv preprint arXiv:2404.08126},
year = {2024}
}