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Recently, the topic of table pre-training has attracted considerable researchinterest. However, how to employ table pre-training to boost the performance oftabular prediction remains an open challenge. In this paper, we propose TapTap,the first attempt that leverages table pre-training to empower models fortabular prediction. After pre-training on a large corpus of real-world tabulardata, TapTap can generate high-quality synthetic tables to support variousapplications on tabular data, including privacy protection, low resourceregime, missing value imputation, and imbalanced classification. Extensiveexperiments on 12 datasets demonstrate that TapTap outperforms a total of 16baselines in different scenarios. Meanwhile, it can be easily combined withvarious backbone models, including LightGBM, Multilayer Perceptron (MLP) andTransformer. Moreover, with the aid of table pre-training, models trained usingsynthetic data generated by TapTap can even compete with models using theoriginal dataset on half of the experimental datasets, marking a milestone inthe development of synthetic tabular data generation. The codes are availableat https://github.com/ZhangTP1996/TapTap.

Generative Table Pre-training Empowers Models for Tabular Prediction