Truthful Aggregation of LLMs with an Application to Online Advertising

Ermis Soumalias, Michael Curry, Sven Seuken

Keywords: mechanism design, llm, auction, online advertising

TL;DR: we give a truthful auction mechanism allowing agents to bid to influence LLM outputs, with a focus on advertising applications.

Abstract: The next frontier of online advertising is revenue generation from LLM-generated content. We consider a setting where advertisers aim to influence the responses of an LLM to align with their interests, while platforms seek to maximize advertiser value and ensure user satisfaction. The challenge is that advertisers may misreport their preferences. To address this, we introduce MOSAIC, an auction mechanism that ensures that truthful reporting is a dominant strategy for advertisers, and which aligns each advertiser’s utility with their contribution to social welfare. Importantly, the mechanism operates without LLM fine-tuning or access to model weights and provably converges to the output of the optimally fine-tuned LLM for the platform’s objective as computational resources increase. Additionally, it can incorporate contextual information about the advertisers, accelerating convergence. Via experiments with a publicly available LLM, we show that MOSAIC significantly boosts advertiser value and platform revenue with low computational overhead. While our motivating application is online advertising, our mechanism can be applied in any setting with monetary transfers, making it a general-purpose solution for truthfully aggregating the preferences of self-interested agents over LLM-generated replies.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 11839