The paper discusses the opportunities and challenges of incorporating online advertisements within the outputs generated by Large Language Models (LLMs). It first outlines the essential requirements that such an LLM-based advertising system must fulfill, including privacy, reliability, latency, user satisfaction, advertiser satisfaction, and platform revenue.
The paper then introduces a general framework for LLM-based advertising, consisting of four key modules: modification, bidding, prediction, and auction. For each module, the authors discuss various design considerations and the technical challenges involved in implementing them in a practical manner.
The modification module is responsible for generating the modified output that incorporates the advertisements. Two approaches are considered: the advertiser modification model and the LLMA modification model, each with its own advantages and disadvantages in terms of privacy, reliability, and latency.
The bidding module generates bids based on the modified outputs. The dynamic bidding model allows advertisers to customize their bids based on the modified output, while the static bidding model relies on pre-committed contracts. The authors discuss the trade-offs between these two models in terms of advertiser satisfaction and implementation complexity.
The prediction module is tasked with estimating the user satisfaction rate (SR) and click-through-rate (CTR) for the modified outputs. The authors propose using a combination of distance-based measures and online learning from user feedback to effectively predict these metrics.
The auction module determines the final output and the corresponding payment from the advertisers. The authors discuss the challenges of incorporating multiple advertisements in a single output and the need to balance the trade-off between short-term revenue and long-term user retention.
Finally, the paper explores the prospect of leveraging LLMs for dynamic creative optimization (DCO), where the content of the advertisements is dynamically tailored to individual user contexts. The authors highlight the technical challenges in implementing LLM-based DCO, such as ensuring low latency, reliability, and cost-sharing models.
Throughout the paper, the authors raise various research questions and discuss potential solutions to address the identified challenges, calling for further academic and industrial research in this emerging field of LLM-based online advertising.
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by Soheil Feizi... at arxiv.org 04-19-2024
https://arxiv.org/pdf/2311.07601.pdfDeeper Inquiries