mlsanityData to 7 Oct 2026

Ad Auctions for LLMs via Retrieval Augmented Generation

arXiv 2406.09459 ↗First posted 12 Jun 2024cs.GT cs.AI cs.CLcited by 17

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

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.

Key facts. Cited by 17 papers in the map (data through 7 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.

Builds on

22 of 48 references.

References · 22

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2023Visual Instruction Tuningcited by 7,792
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  6. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
  7. 2017Billion-scale similarity search with GPUscited by 2,477
  8. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  9. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
  10. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  11. 2023PaLM 2 Technical Reportcited by 1,154
  12. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 876
10 more
  1. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  2. 2024Large Language Models: A Surveycited by 506
  3. 2022InCoder: A Generative Model for Code Infilling and Synthesiscited by 492
  4. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 317
  5. 2023Improving Text Embeddings with Large Language Modelscited by 295
  6. 2022Promptagator: Few-shot Dense Retrieval From 8 Examplescited by 233
  7. 2024Truthful Aggregation of LLMs with an Application to Online Advertisingcited by 29
  8. 2024Auctions with LLM Summariescited by 24
  9. 2024Mechanism Design for LLM Fine-tuning with Multiple Reward Modelscited by 18
  10. 2023Online Advertisements with LLMs: Opportunities and Challengescited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 9.

17 papers.

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  1. 2025Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontierscited by 14
  2. 2025The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exitcited by 10
  3. 2025LLM Bidders Preserve the Mechanism-Level Orderings of Human Bidderscited by 10
  4. 2025LLM-Auction: Generative Auction towards LLM-Native Advertisingcited by 10
  5. 2026Ad Insertion in LLM-Generated Responsescited by 8
  6. 2025GEM-Bench: A Benchmark for Ad-Injected Response Generation within Generative Engine Marketingcited by 7
  7. 2025MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attributioncited by 3
  8. 2026Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibilitycited by 3
  9. 2026LLM Advertisement based on Neuron Auctionscited by 2
  10. 2026NaiAD: Initiate Data-Driven Research for LLM Advertisingcited by 2
  11. 2026LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbotscited by 2
  12. 2025Sponsored Questions and How to Auction Themcited by 1
5 more
  1. 2026Generative AI Advertising as a Problem of Trustworthy Commercial Interventioncited by 1
  2. 2026Error-Aware Reverse Auction Mechanism for Large Language Model Routingcited by 1
  3. 2025TeamCMU at Touch'e: Adversarial Co-Evolution for Advertisement Integration and Detection in Conversational Searchcited by 0
  4. 2026LLMAdBench: A Human Preference Benchmark for Advertising in LLM Responsescited by 0
  5. 2026Inference Auctionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Mechanism Design for Quality-Preserving LLM Advertising
  2. 2025Data Auctions for Retrieval Augmented Generation
  3. 2025Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising
  4. 2026LBM: Hierarchical Large Auto-Bidding Model via Reasoning and Acting
  5. 2025Accelerated Preference Elicitation with LLM-Based Proxies
  6. 2025CTR-Driven Ad Text Generation via Online Feedback Preference Optimization
  7. 2026Strategy-Aware Parameter-Efficient Adaptation for LLM-based Auto-Bidding ↗
  8. 2026On the Role of Language Representations in Auto-Bidding: Findings and Implications