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arXiv · 2404.08126

Auctions with LLM Summaries

Kumar Avinava Dubey·Zhe Feng·Rahul Kidambi·Aranyak Mehta·Di Wang
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Abstract

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.

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Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang. 2024-04-11. Auctions with LLM Summaries. https://arxiv.org/abs/2404.08126

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