Ad Auctions for LLMs via Retrieval Augmented Generation

In the field of computational advertising, the integration of ads into the outputs of large language models (LLMs) presents an opportunity to support these services without compromising content integrity. This paper introduces novel auction mechanisms for ad allocation and pricing within the textual outputs of LLMs, leveraging retrieval-augmented generation (RAG). We propose a segment auction where an ad is probabilistically retrieved for each discourse segment (paragraph, section, or entire output) according to its bid and relevance, following the RAG framework, and priced according to competing bids. We provide a theoretical analysis of the efficiency, revenue, and incentive properties of various pricing rules, including a characterization of the incentive-compatible pricing scheme. An empirical evaluation validates the feasibility and effectiveness of our approach.