发表机构
Politecnico di Milano（米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种结合大语言模型低秩适配的在线演员-评论家算法，用于联合优化定价与广告生成，在多个需求模型下实现显著收益提升。
AI 中文摘要
我们研究了一个序贯定价问题，其中卖家联合发布一个价格和一个由大语言模型（LLM）生成的广告。卖家旨在未知产品需求下最大化收益，该需求同时取决于这两个决策，而卖家仅能观察到每个报价是否导致购买。我们提出了一种在线演员-评论家算法，该算法将预训练大语言模型的低秩适配（LoRA）与基于可用数据拟合的需求模型相结合。在每一轮中，演员生成广告，评论家估计购买概率以指导价格选择。随后，产生的反馈用于更新演员和评论家，其中评论家的收益估计为演员的策略梯度更新提供基线。为了评估我们的方法，我们开发了一个评估框架，包含三个合成需求模型和一个基于真实市场数据构建的需求模拟器。最后，我们将我们的算法与不同时优化价格选择和广告生成的基准进行比较，在三个合成需求模型下，相对于参考策略的预期收益增益分别为5.69%、5.18%和55.96%，在市场模拟器下为5.81%。
英文摘要
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.
