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arXiv · 2506.05584

TabFlex: Scaling Tabular Learning to Millions with Linear Attention

Yuchen Zeng·Tuan Dinh·Wonjun Kang·Andreas C Mueller
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Abstract

Leveraging the in-context learning (ICL) capability of Large Language Models (LLMs) for tabular classification has gained significant attention for its training-free adaptability across diverse datasets. Recent advancements, like TabPFN, excel in small-scale tabular datasets but struggle to scale for large and complex datasets. Our work enhances the efficiency and scalability of TabPFN for larger datasets by incorporating linear attention mechanisms as a scalable alternative to complexity-quadratic self-attention. Our model, TabFlex, efficiently handles tabular datasets with thousands of features and hundreds of classes, scaling seamlessly to millions of samples. For instance, TabFlex processes the poker-hand dataset with over a million samples in just 5 seconds. Our extensive evaluations demonstrate that TabFlex can achieve over a 2x speedup compared to TabPFN and a 1.5x speedup over XGBoost, outperforming 25 tested baselines in terms of efficiency across a diverse range of datasets. Furthermore, TabFlex remains highly effective on large-scale datasets, delivering strong performance with significantly reduced computational costs, especially when combined with data-efficient techniques such as dimensionality reduction and data sampling.

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Yuchen Zeng, Tuan Dinh, Wonjun Kang, Andreas C Mueller. 2025-06-05. TabFlex: Scaling Tabular Learning to Millions with Linear Attention. https://arxiv.org/abs/2506.05584

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