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TabFM：400M 参数的零样本表格数据基础模型
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论文提出 TabFM，一个 400M 参数的表格基础模型，将监督式表格预测建模为 in-context learning，单次前向传播即可产生校准的零样本预测，无需任务特定调参。
正文
Abstract:Tabular machine learning typically relies on per-dataset workflows, fitting tree ensembles or running AutoML searches from scratch for every task. We present TabFM, a 400M-parameter tabular foundation model that formulates supervised tabular prediction as in-context learning. TabFM produces calibrated zero-shot predictions in a single forward pass without task-specific tuning. Trained entirely on synthetic tables generated from structural causal models, TabFM learns general tabular representations that transfer zero-shot to real-world tasks. Across all 51 benchmark datasets in TabArena (38 classification and 13 regression), zero-shot TabFM ranks first among default tabular foundation models and outperforms tuned AutoML pipelines. Two extensions over the same frozen weights improve performance further on both tracks: multi-view feature expansion with ensembling and post-hoc calibration (TabFM+), and LLM-guided, dataset-specific data processing and feature engineering (TabFM-Auto).
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.37959 [cs.LG] |
| (or arXiv:2609.37959v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.37959 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Deqing Fu [view email]
[v1]
Tue, 29 Sep 2026 16:30:44 UTC (556 KB)
来源：HuggingFace Daily Papers（社区热门论文） · arxiv.org