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.

翻译：将广告嵌入大语言模型输出会引发根本性矛盾：收入优化可能扭曲内容并降低用户体验。现有方法大多忽视这一权衡，常将无关广告强加至回复中。我们提出一种质量保持的拍卖框架，将内容保真度显式融入机制设计。基于检索增强生成技术，该方法将有机内容作为参考基准，推导出能筛除非正边际社会贡献广告的内生保留价格。我们设计了含Myerson支付的KL正则化单分配机制，以及含筛选VCG的多分配机制，两者均满足占优策略激励相容与个体理性约束。跨场景实验表明，我们的机制在单广告收入、与无广告回复的语义相似度等指标上优于现有基线。研究结果为兼顾内容质量与商业变现的大语言模型广告建立了新范式。