Abstract:Embedding advertisements into large language model (LLM) outputs introduces a fundamental tension: revenue optimization can distort content and degrade user experience. Existing approaches largely ignore this trade-off, often forcing irrelevant ads into responses. We propose a quality-preserving auction framework that explicitly integrates content fidelity into the mechanism design. Built on retrieval-augmented generation (RAG), our approach treats organic content as a reference and derives an endogenous reserve price that screens out ads with non-positive marginal social welfare contributions. We develop a KL-regularized single-allocation mechanism with Myerson payments and a screened VCG multi-allocation mechanism, both satisfying dominant-strategy incentive compatibility and individual rationality. Experiments across diverse scenarios demonstrate that our mechanisms outperform existing baselines in metrics such as revenue per ad and semantic similarity to no-ad responses. Our results establish a new paradigm for LLM advertising that enables monetization without compromising output quality.
| Subjects: | Computer Science and Game Theory (cs.GT) |
| Cite as: | arXiv:2605.10964 [cs.GT] |
| (or arXiv:2605.10964v1 [cs.GT] for this version) | |
| https://doi.org/10.48550/arXiv.2605.10964 arXiv-issued DOI via DataCite |
From: Xiaowu Dai [view email]
[v1]
Thu, 7 May 2026 18:01:21 UTC (462 KB)
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