# Ad Auctions for LLMs via Retrieval Augmented Generation arXiv: https://arxiv.org/abs/2406.09459 ยท HTML: https://mlsanity.com/paper/2406.09459/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. - Builds on 22 of its 48 references that are in the map. - First posted on arXiv 12 Jun 2024. ## Placed in - Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/) ## Builds on (most cited first, 22 of 22) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023) - [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020) - [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016) - [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017) - [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023) - [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022) - [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022) - [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023) - [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022) - [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023) - [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024) - [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022) - [Chain-of-Verification Reduces Hallucination in Large Language Models](https://mlsanity.com/paper/2309.11495/) (2023) - [Improving Text Embeddings with Large Language Models](https://mlsanity.com/paper/2401.00368/) (2023) - [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://mlsanity.com/paper/2209.11755/) (2022) - [Truthful Aggregation of LLMs with an Application to Online Advertising](https://mlsanity.com/paper/2405.05905/) (2024) - [Auctions with LLM Summaries](https://mlsanity.com/paper/2404.08126/) (2024) - [Mechanism Design for LLM Fine-tuning with Multiple Reward Models](https://mlsanity.com/paper/2405.16276/) (2024) - [Online Advertisements with LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.07601/) (2023) ## Built on by (most cited first, 18 of 18) - [Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers](https://mlsanity.com/paper/2502.09053/) (2025) - [LLM-Auction: Generative Auction towards LLM-Native Advertising](https://mlsanity.com/paper/2512.10551/) (2025) - [The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit](https://mlsanity.com/paper/2501.02173/) (2025) - [LLM Bidders Preserve the Mechanism-Level Orderings of Human Bidders](https://mlsanity.com/paper/2507.09083/) (2025) - [Ad Insertion in LLM-Generated Responses](https://mlsanity.com/paper/2601.19435/) (2026) - [GEM-Bench: A Benchmark for Ad-Injected Response Generation within Generative Engine Marketing](https://mlsanity.com/paper/2509.14221/) (2025) - [MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution](https://mlsanity.com/paper/2512.05958/) (2025) - [Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility](https://mlsanity.com/paper/2604.19113/) (2026) - [LLM Advertisement based on Neuron Auctions](https://mlsanity.com/paper/2605.08326/) (2026) - [NaiAD: Initiate Data-Driven Research for LLM Advertising](https://mlsanity.com/paper/2605.09918/) (2026) - [LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots](https://mlsanity.com/paper/2605.16474/) (2026) - [TeamCMU at Touch'e: Adversarial Co-Evolution for Advertisement Integration and Detection in Conversational Search](https://mlsanity.com/paper/2507.00509/) (2025) - [Sponsored Questions and How to Auction Them](https://mlsanity.com/paper/2512.03975/) (2025) - [Generative AI Advertising as a Problem of Trustworthy Commercial Intervention](https://mlsanity.com/paper/2605.18673/) (2026) - [Error-Aware Reverse Auction Mechanism for Large Language Model Routing](https://mlsanity.com/paper/2608.12719/) (2026) - [LLMAdBench: A Human Preference Benchmark for Advertising in LLM Responses](https://mlsanity.com/paper/2609.32533/) (2026) - [Inference Auctions](https://mlsanity.com/paper/2609.40070/) (2026) - [Diffusion-Based Auctions for Multi-Winner Banner Ads](https://mlsanity.com/paper/2610.14473/) (2026) ## Abstract (arXiv) 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 show that our auction maximizes logarithmic social welfare, a new notion of welfare that balances allocation efficiency and fairness, and we characterize the associated incentive-compatible pricing rule. These results are extended to multi-ad allocation per segment. An empirical evaluation validates the feasibility and effectiveness of our approach over several ad auction scenarios, and exhibits inherent tradeoffs in metrics as we allow the LLM more flexibility to allocate ads.