Recent text and image foundation models are incredibly impressive, and these models are attracting an ever-increasing portion of research resources. In this position piece we aim to shift the ML research community's priorities ever so slightly to a different modality: tabular data. Tabular data is the dominant modality in many fields, yet it is given hardly any research attention and significantly lags behind in terms of scale and power. We believe the time is now to start developing tabular foundation models, or what we coin a Large Tabular Model (LTM). LTMs could revolutionise the way science and ML use tabular data: not as single datasets that are analyzed in a vacuum, but contextualized with respect to related datasets. The potential impact is far-reaching: from few-shot tabular models to automating data science; from out-of-distribution synthetic data to empowering multidisciplinary scientific discovery. We intend to excite reflections on the modalities we study, and convince some researchers to study large tabular models.

翻译：近期，文本和图像基础模型令人印象深刻，这些模型正吸引着日益增长的研究资源。在这篇立场文章中，我们旨在稍稍调整机器学习研究界的优先方向，转向另一种模态：表格数据。表格数据是许多领域中的主导模态，然而它几乎没有受到研究关注，且在规模和能力方面显著落后。我们认为，现在是时候开始开发表格基础模型，即我们称之为大型表格模型（LTM）。LTM可能彻底改变科学和机器学习使用表格数据的方式：不再是孤立分析单个数据集，而是将其置于相关数据集的背景下进行关联。潜在影响深远：从少样本表格模型到自动化数据科学；从分布外合成数据到赋能多学科科学发现。我们希望能够激发对所学模态的反思，并说服一些研究人员投身大型表格模型的研究。