Introducing TabFM: A zero-shot foundation model for tabular data

Google Research · 2 weeks ago

Google introduced TabFM, a foundation model that applies zero-shot in-context learning to tabular data classification and regression tasks, eliminating the need for manual hyperparameter tuning and feature engineering. The model was trained on hundreds of millions of synthetically generated datasets and evaluated on TabArena spanning 51 datasets with up to 150,000 samples, consistently outperforming tree-based algorithms like XGBoost. TabFM will be integrated into Google BigQuery as an AI.PREDICT SQL command, allowing users to generate predictions on new tables in a single forward pass without machine learning expertise.