Auctions with LLM Summaries
<|reference_start|>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.<|reference_end|>
@article{dubey2024auctions,
    title={Auctions with LLM Summaries},
    author={Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang},
    journal={arXiv preprint arXiv:2404.08126},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08126},
    primaryClass={cs.GT cs.AI}
}