mlsanityData to 7 Oct 2026

Online Advertisements with LLMs: Opportunities and Challenges

arXiv 2311.07601 ↗First posted 11 Nov 2023cs.CY cs.AIcited by 17

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Abstract (arXiv)

This paper explores the potential for leveraging Large Language Models (LLM) in the realm of online advertising systems. We introduce a general framework for LLM advertisement, consisting of modification, bidding, prediction, and auction modules. Different design considerations for each module are presented. These design choices are evaluated and discussed based on essential desiderata required to maintain a sustainable system. Further fundamental questions regarding practicality, efficiency, and implementation challenges are raised for future research. Finally, we exposit how recent approaches on mechanism design for LLM can be framed in our unified perspective.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 28 of its 65 references that are in the map. First posted on arXiv 11 Nov 2023.

Builds on

28 of 65 references.

References · 28

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16 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 8; 2026: 6.

17 papers.

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  1. 2025The Invisible Leash: Why RLVR May or May Not Escape Its Origincited by 92
  2. 2024Truthful Aggregation of LLMs with an Application to Online Advertisingcited by 29
  3. 2025Human Decision-making is Susceptible to AI-driven Manipulationcited by 19
  4. 2024UniGuard: Towards Universal Safety Guardrails for Jailbreak Attacks on Multimodal Large Language Modelscited by 18
  5. 2024Ad Auctions for LLMs via Retrieval Augmented Generationcited by 17
  6. 2025Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontierscited by 14
  7. 2025LLM-Auction: Generative Auction towards LLM-Native Advertisingcited by 10
  8. 2026Ad Insertion in LLM-Generated Responsescited by 8
  9. 2025GEM-Bench: A Benchmark for Ad-Injected Response Generation within Generative Engine Marketingcited by 7
  10. 2025MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attributioncited by 3
  11. 2026NaiAD: Initiate Data-Driven Research for LLM Advertisingcited by 2
  12. 2026LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbotscited by 2
5 more
  1. 2025Advertising in AI systems: Society must be vigilantcited by 1
  2. 2025Sponsored Questions and How to Auction Themcited by 1
  3. 2026Generative AI Advertising as a Problem of Trustworthy Commercial Interventioncited by 1
  4. 2026Just Ask for a Table: A Thirty-Token User Prompt Defeats Sponsored Recommendations in Twelve LLMscited by 0
  5. 2026LLMAdBench: A Human Preference Benchmark for Advertising in LLM Responsescited by 0