LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots
The integration of advertising auction mechanisms into large language model (LLM)-based chatbots presents a significant opportunity for commercialization, yet poses unique challenges in balancing relevance, efficiency, and user experience. Recently, Feizi et al.~\citep{feizi2023online} and Hajiaghayi et al.~\citep{hajiaghayi2024ad} outlined a retrieve-then-generate paradigm that decouples retrieval and generation, offering lightweight ad insertion and payment determination. However, current retrieval relies solely on text embedding similarity, which may lead to commercial misinterpretation and issues such as repetitive insertions. In this paper, we propose LERA, a two-stage retrieve-then-...
| Authors | Haoran Sun, Xinrui Song, Xinyu Zhang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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