令牌级广告
Token-Level Advertising
- Renmin University of China（中国人民大学）
- Baidu Inc.（百度公司）
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对生成式AI对传统广告机制的挑战，提出令牌级广告机制LAMA，证明其满足马尔可夫DSIC和IR，经实验验证可提升平台福利与收入且维持响应质量，为原生生成式广告提供可行性证据。
AI中文摘要：
生成式AI正在改变人们获取信息的方式，对围绕预定义广告位构建的传统广告机制构成挑战。针对原生生成式广告，我们提出了潜在广告商混合拍卖（Latent Advertiser Mixture Auction, LAMA），这是一种令牌级广告机制，可将广告商影响直接嵌入生成过程。广告商上报能诱导出广告商特定的下一个令牌策略的局部续报价值，平台通过潜在混合模型进行解码，同时更新分配后验。我们证明LAMA满足马尔可夫DSIC和IR，并实现了接近最优的KL正则化福利。我们进一步开发了基于学习的实现方式，可从学习到的局部优势和根值在线重构所需的上报。在真实商业搜索查询拆分上的概念验证实验表明，LAMA在维持面向用户的响应质量的同时，提升了平台福利和收入，为原生生成式广告的可行性提供了初步证据。
英文摘要：
Generative AI is transforming how people access information, challenging traditional advertising mechanisms built around predefined slots. Towards generation-native advertising, we propose the Latent Advertiser Mixture Auction (LAMA), a token-level advertising mechanism that embeds advertiser influence directly into the generation process. Advertisers report local continuation values that induce advertiser-specific next-token policies, from which the platform decodes through a latent mixture while updating an allocation posterior. We show that LAMA satisfies Markov DSIC and IR, and achieves near-optimal KL-regularized welfare. We further develop a learning-based implementation that reconstructs the required reports online from learned local advantages and root values. Proof-of-concept experiments on real-world commercial-search query splits show that LAMA improves platform welfare and revenue while maintaining user-facing response quality, providing initial evidence for the feasibility of generation-native advertising.
