检验表格大模型预测是否自洽，发现其结果不一致。
Do Tabular Foundation Models Agree with Themselves?
- 用边际与分解一致性检验模型自洽性
- 所有模型在分类回归任务中均不满足一致性
- 适合关注模型可信度的表格数据研究者
表格基础模型（TFMs）是当前处理表格预测问题的最佳方法，基于Transformer构建，通过预训练先验近似贝叶斯后验预测分布。这些单变量预测器可通过自回归方式采样目标并加入特征，转换为多变量模型。然而，生成联合分布的可靠性尚未被研究。此外，由于真实世界数据集的精确分布未知，无法直接评估模型与后验的匹配程度。因此，本文提出新问题：模型的预测是否可能来自某个联合分布？为此，设定两个必要条件：边际化一致性要求边缘条件等于直接预测的边际；分解一致性要求不同分解顺序产生相同的联合分布。我们在多个数据集上对各类TFM进行评估，发现其在分类和回归任务中均违反这两个条件。
原文摘要 · Abstract (English)
Tabular Foundation Models (TFMs) are currently the best approach to tabular prediction problems. They are constructed as transformers that approximate the Bayesian posterior predictive distribution based on a pre-training prior. These univariate predictors can be converted into multivariate ones autoregressively by sampling one target and adding it to the features. However, the faithfulness of the resulting joint has not been investigated. Furthermore, TFMs cannot be evaluated against the posterior itself, at least not on real-world datasets, because the ground-truth distribution is unknown. We therefore propose asking a different question: could a model's predictions result from any joint distribution? To answer this question, we pose two requirements that any such model must satisfy. The first is marginalization consistency, which demands that marginalized conditionals are equal to directly predicted marginals. The second is factorization consistency, which demands that different factorization orders result in equal joint distributions. Every TFM that we evaluate violates both of these requirements for both classification and regression across all datasets.
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