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Online Advertisements with LLMs: Opportunities and Challenges
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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.
Forward citations
Cited by 2 Pith papers
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PILA: Plug-and-Play Insertion for LLM-native Advertising
Ads can be inserted into LLM answers after the fact by an external rewriter model, improving measured ad quality without retraining or modifying the base chatbot.
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Advertising in AI systems: Society must be vigilant
Generative AI outputs will likely carry embedded commercial content, and the paper proposes design principles, provenance tracking, and two debiasing strategies to preserve transparency.
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