用大模型生成用户偏好分布，辅助低价场景下的决策优化。
LLM-SAA: LLM-persona Generated Distributions for Decision-making
- 让大模型模拟用户支付意愿，构建决策所需分布
- 在数据少时表现优于传统方法，提升定价与库存决策效果
- 适合小样本、需快速生成策略的商业场景
大模型可生成丰富数据，如模拟人类估值和偏好的虚拟角色，或基于世界知识的需求预测。但这些由大模型生成的分布对下游决策有多大帮助？例如，在为新产品定价时，企业可提示大模型根据产品描述模拟消费者支付意愿，但该分布对优化价格有多实用？我们称此方法为LLM-SAA：利用大模型构建估计分布，并在此分布下优化决策。本文研究评估此类分布质量的指标，基于其引发的决策表现。以三种经典决策问题（商品组合优化、定价、报童问题）为例，发现大模型生成的分布具有实际效用，尤其在低数据条件下。同时表明，如Wasserstein距离等不依赖决策的评价指标，在评估此类分布时可能产生误导。
原文摘要 · Abstract (English)
LLMs can generate a wealth of data, ranging from simulated personas imitating human valuations and preferences, to demand forecasts based on world knowledge. But how well do such LLM-generated distributions support downstream decision-making? For example, when pricing a new product, a firm could prompt an LLM to simulate how much consumers are willing to pay based on a product description, but how useful is the resulting distribution for optimizing the price? We refer to this approach as LLM-SAA, in which an LLM is used to construct an estimated distribution and the decision is then optimized under that distribution. In this paper, we study metrics to evaluate the quality of these LLM-generated distributions, based on the decisions they induce. Taking three canonical decision-making problems (assortment optimization, pricing, and newsvendor) as examples, we find that LLM-generated distributions are practically useful, especially in low-data regimes. We also show that decision-agnostic metrics such as Wasserstein distance can be misleading when evaluating these distributions for decision-making.
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