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.

近期的文本和图像基础模型受到了极大的关注，本文旨在将机器学习研究社区的重点稍稍转向不同的模态：表格数据。表格数据是许多领域的主要模态，但在规模和能力方面明显落后，我们认为现在是开发表格基础模型（或我们所称的大型表格模型）的时候了。大型表格模型将可能从多个角度革新科学和机器学习对表格数据的使用：不仅仅是分析单个数据集，而是联系相关数据集进行分析。潜在的影响是广泛的：从少样本表格模型到自动化数据科学；从超分布合成数据到促进多学科科学发现。我们希望激发对我们所研究模态的思考，并使一些研究人员开始研究大型表格模型。