Auctions with LLM Summaries

• URL: http://arxiv.org/abs/2404.08126v1
• Date: Thu, 11 Apr 2024 21:05:56 GMT
• Title: Auctions with LLM Summaries
• Authors: Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang,
• Abstract summary: We study an auction setting in which bidders bid for placement of their content within a summary generated by a large language model (LLM) 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.
• Score: 13.797878055583531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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