Large Scale Transfer Learning for Tabular Data via Language Modeling Josh Gardner, Juan C. Perdomo # Ludwig Schmidt

Tabular data - structured, heterogeneous, spreadsheet-style data with rows and columns - is widely used in practice across many domains. However, while recent foundation models have reduced the need for developing task-specific datasets and predictors in domains such as language modeling and computer vision, this transfer learning paradigm has not had similar impact in the tabular domain.