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' preferences generally conflict with those of the user, and 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 that aligns the utility of each advertiser 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 as computational resources increase. Additionally, it can incorporate contextual information about advertisers, which significantly improves social welfare. Through experiments with a publicly available LLM, we show that MOSAIC leads to high 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.

翻译：在线广告的下一个前沿领域是从大语言模型生成内容中创造收入。我们研究这样一种场景：广告主试图影响大语言模型的回复以符合其利益，而平台则致力于最大化广告主价值并确保用户满意度。核心挑战在于广告主的偏好通常与用户偏好相冲突，且广告主可能虚报其偏好。为此，我们提出MOSAIC拍卖机制，该机制能确保如实报告成为广告主的占优策略，并使每个广告主的效用与其对社会福利的贡献保持一致。值得注意的是，该机制无需对大语言模型进行微调或获取模型权重，并能在计算资源增加时严格收敛至最优微调大语言模型的输出。此外，该机制可整合广告主的上下文信息，从而显著提升社会福利。通过对公开可用大语言模型的实验，我们证明MOSAIC能以较低计算开销实现高广告主价值与平台收入。虽然我们的研究动机源于在线广告，但该机制可应用于任何存在货币转移的场景，从而为真实聚合自利智能体对大语言模型生成回复的偏好提供了一个通用解决方案。