  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.
