TabFM: A zero-shot foundation model for tabular data
Weihao Kong and Abhimanyu Das, Research Scientists, Google Research
We’ve seen a massive shift in how people handle time-series forecasting since we launched TimesFM. Now, we’re bringing that same "zero-shot" logic to tabular data.
We introduce TabFM, a new foundation model for tabular data to simplify classification and regression workflows.
Tabular data constitutes the backbone of enterprise data infrastructure and powers a significant fraction of critical predictive machine learning applications . From predicting customer churn to identifying financial fraud, tabular regression and classification tasks are ubiquitous. For years, supervised tree-based algorithms like AdaBoost , XGBoost and random forests , to name a few, have historically dominated this space, offering robust performance on structured data.
However, the lifecycle of deploying these traditional models presents a significant bottleneck. Fitting an XGBoost model to a new dataset is not merely a matter of a single .fit() step; it invariably requires tedious manual effort. Data scientists must invest countless hours into extensive hyperparameter optimization and domain-specific feature engineering just to extract a reliable signal from the raw data.
On the other hand, recent advances in the broader machine learning landscape — particularly the evolution of large language models (LLMs) — have changed how we interact with novel tasks. LLMs have demonstrated the remarkable power of zero-shot prediction through in-context learning (ICL). This technique lets a pretrained model learn a new task by providing examples and instructions in the input context, without updating any underlying model weights.