Token-Level Advertising
cs.GT, cs.LG
Submitted: 2026-08-27
Updated: 2026-09-04
License: http://creativecommons.org/licenses/by/4.0/
The gist: Generative AI is transforming how people access information, challenging traditional advertising mechanisms built around predefined slots.
Terminology
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.
Sources
- Position Auctions in AI-Generated Content
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- LoRA: Low-Rank Adaptation of Large Language Models
- GEM-Bench: A Benchmark for Ad-Injected Response Generation within Generative Engine Marketing
- Truthful Aggregation of LLMs with an Application to Online Advertising
- LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots
- Ad Insertion in LLM-Generated Responses
- Qwen3 Technical Report
- LLM Advertisement based on Neuron Auctions
- Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
- LLM-Auction: Generative Auction towards LLM-Native Advertising
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