Problem
Research questions and friction points this paper is trying to address.
Generative AI
Token-Level Advertising
Advertiser Influence
Token-Level Advertising
🤖 AI Summary
为解决生成式AI对传统广告机制的挑战，提出LAMA方法，在生成过程中直接嵌入广告主影响，优化平台福利和收入。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Latent Advertiser Mixture Auction
token-level advertising
generation-native advertising
Markov DSIC and IR
KL-regularized welfare
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