让表格大模型的预测符合经济逻辑，避免价格越高需求越高的荒谬结果。
Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice
- 用经济理论约束的模型先建模，再用适配器修正大模型预测。
- 在交通数据上提升13个百分点准确率，同时保证价格与需求单调关系。
- 适合需要可解释、合规决策的经济学或政策分析场景。
表格基础模型在离散选择预测任务中表现优异，但其预测常违背经济逻辑：提价反而可能提高需求，且估算的支付意愿常为负值或不合理。本文提出一种两阶段适配器，将基础模型预测嵌入效用最大化框架。第一阶段估计一个参数受经济理论约束的标准选择模型；第二阶段冻结该模型参数，训练一个修正项，引入基础模型的预测作为额外信息。结果模型既继承了基础模型的高精度，又保证在政策扰动下价格与需求关系单调，且能解析计算权衡指标。在两个交通数据集上，该方法相比标准Logit模型最高提升13个百分点准确率，同时实现完全经济一致性，这是原始基础模型或传统蒸馏方法无法达到的。
原文摘要 · Abstract (English)
Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require: raising a price sometimes increases predicted demand, and implied willingness-to-pay estimates are frequently negative or implausible. We propose a two-stage adapter that embeds foundation model predictions within a utility-maximization framework. In the first stage, we estimate a standard choice model whose parameters are constrained to obey economic theory. In the second stage, we freeze those parameters and train a correction term that incorporates the foundation model's predictions as additional information. The result is a model that inherits the foundation model's accuracy gains while guaranteeing monotonic price-demand relationships under policy perturbation and producing analytically computable trade-off measures. On two transportation datasets, the adapter recovers up to 13 percentage points of accuracy over a standard logit model while maintaining perfect economic consistency, something neither the raw foundation models nor conventional distillation achieve.
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