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Auctions with LLM Summaries

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arxiv 2404.08126 v1 pith:KPNWEYQS submitted 2024-04-11 cs.GT cs.AI

Auctions with LLM Summaries

Kumar Avinava Dubey , Zhe Feng , Rahul Kidambi , Aranyak Mehta , Di Wang This is my paper
classification cs.GT cs.AI
keywords auctionsummaryauctionsdisplayframeworkgeneratedmodelmodule
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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Cited by 3 Pith papers

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    cs.CL 2026-07 conditional novelty 6.0

    Ads can be inserted into LLM answers after the fact by an external rewriter model, improving measured ad quality without retraining or modifying the base chatbot.

  3. Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing

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    A novel online weighted aggregation mechanism for truthful preference feedback in mobile crowdsourcing achieves sublinear regret O(sqrt(T)) and truthfulness in a dynamic Bayesian game, with an extension for limited fe...