Understanding the Surprising Generalization Properties of Tabular Foundation Models
Tabular Foundation Models (TFMs) increasingly rely on in-context learning, where a model receives labelled examples at inference time and predicts labels for new inputs without updating its weights. Existing TFMs are typically trained on either massive synthetic corpora or very large collections of real datasets. In contrast, we show that surprisingly strong transfer can emerge from self-supervised pre-training on just a single real table. In this setting, we also find that tables tend to be either broadly useful or broadly poor regardless of downstream prediction task, and that the strongest predictor of usefulness is the number of features rather than the number of instances. This leads...
| Authors | Nour Shaheen, Junwei Ma, Alex Labach et al. |
|---|---|
| Primary category | cs.LG |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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