Online Advertisements with LLMs: Opportunities and Challenges

2023/11/11 by Soheil Feizi, Feizi, Soheil, MohammadTaghi Hajiaghayi +5 · 3 voices · 4 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #FOS: Computer and information sciences #cs.AI #cs.CY

paper · pdf · doi:10.48550/arxiv.2311.07601

arxiv published 2023/11/11 · arxiv updated 2024/09/09

Abstract

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.

Cited by

• LLM-Auction: Generative Auction towards LLM-Native Advertising
• MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution
• Sponsored Questions and How to Auction Them
• GEM-Bench: A Benchmark for Ad-Injected Response Generation within Generative Engine Marketing

Discussions

• Online Advertisements with LLMs: Opportunities and Challenges [hn, 3 points, 0 comments]
• I'll do you one worse. Natural language ad injections into prompt responses. Here's an illustrative example (taken from arxiv.org/abs/2311.07601 , more in appendix). [bsky, 1 points, 1 comments]
• Ads for AI Apps/LLM's [hn, 1 points, 1 comments]

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• Ad Auctions for LLMs via Retrieval Augmented Generation
• Auctions with LLM Summaries
• LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System