Exploring Fine-Tuning for Tabular Foundation Models

What is it about?

This paper explores how to get the best results from a new generation of foundation models built for structured data, such as spreadsheets, business records, and medical tables. We tested whether "fine-tuning" these models on specific datasets improves their performance. Our results show that these models are often already very strong without extra training, and that fine-tuning only helps in certain settings. In some cases it improves results, but in others it can reduce accuracy or make predictions less dependable. The study gives practical guidance on when fine-tuning is useful and when it is better to rely on the original model. This can help researchers and practitioners make better choices when using AI on real-world tabular data.

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Why is it important?

This work is important because tabular foundation models are gaining attention rapidly, yet there is still limited evidence on how they should be adapted for real-world tasks. Our study is one of the first to compare different fine-tuning approaches across several leading models and benchmark datasets, while also examining reliability and fairness alongside performance. The findings show that fine-tuning is not universally helpful: in some settings it improves results, but in others it can reduce accuracy or make predictions less dependable. By offering practical guidance on when fine-tuning is worth using, this research can help both researchers and practitioners apply these models more effectively and responsibly.