| title | TabFM: A Zero-Shot Foundation Model for Tabular Data |
|---|---|
| authors | Weihao Kong, Erez Louidor Ilan, Shuxin Nie, Taman Narayan, Rajat Sen, Yichen Zhou, Deqing Fu, Samet Oymak, Abhimanyu Das |
| date | 2026-09-29 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.37959 |
| affiliation | |
| upvotes | 17 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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). |
cite
@misc{kong2026tabfm,
title={TabFM: A Zero-Shot Foundation Model for Tabular Data},
author={Weihao Kong and Erez Louidor Ilan and Shuxin Nie and Taman Narayan and Rajat Sen and Yichen Zhou and Deqing Fu and Samet Oymak and Abhimanyu Das},
year={2026},
eprint={2609.37959},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.37959}
}