arXiv：cs.LG· Alessandro Barro, Francesco Bacchiocchi, Francesco Emanuele Stradi, Alberto Marchesi·· 4 小时前AI 评分31
用 LLM 同时定价与生成广告：一种在线 actor-critic 方法
Marrying Pricing and Advertising with LLMs
AI 导读
研究者提出一种在线 actor-critic 算法，将预训练 LLM 的 LoRA 微调与需求模型结合，让卖方在未知需求下同时发布价格和 LLM 生成的广告以最大化收入。在三个合成需求模型和真实市场数据模拟器上，该方法相对未联合优化定价与广告生成的基准策略分别取得 5.69%、5.18%、55.96% 和 5.81% 的预期收入提升。
正文
Abstract:We study a sequential pricing problem in which a seller jointly posts a price and an advertisement generated by a large language model (LLM). The seller aims to maximize revenue under an unknown product demand that depends on both decisions, while observing only whether each offer leads to a purchase. We propose an online actor-critic algorithm that combines low-rank adaptation (LoRA) of a pretrained LLM with a demand model fitted to available data. At each round, the actor generates an advertisement, and the critic estimates purchase probabilities to guide price selection. Then, the resulting feedback is used to update both the actor and the critic, with the critic's revenue estimates providing a baseline for policy gradient updates of the actor. To evaluate our approach, we develop an evaluation framework with three synthetic demand models and a demand simulator built from real-world marketplace data. Finally, we compare our algorithm with benchmarks that do not jointly optimize price selection and advertisement generation, achieving expected revenue gains over the reference policy of 5.69%, 5.18% and 55.96% under the three synthetic demand models and 5.81% under the marketplace simulator.
| Subjects: | Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.09985 [cs.GT] |
| (or arXiv:2610.09985v1 [cs.GT] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09985 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Alessandro Barro [view email]
[v1]
Wed, 7 Oct 2026 12:50:08 UTC (1,372 KB)
来源：arXiv：cs.LG · arxiv.org