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arXiv Computation and Language By Alan Arazi, Eilam Shapira, Shoham Grunblat, Mor Ventura, Elad Hoffer, Gioia Blayer, David Holzm\"uller, Lennart Purucker, Ga\"el Varoquaux, Frank Hutter, Roi Reichart

MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image

Read the original on arXiv Computation and Language →

MulTaBench is a new benchmark comprising 40 datasets split evenly between image‑tabular and text‑tabular tasks, designed to evaluate multimodal tabular learning where modalities provide complementary predictive signals. The study shows that tuning embeddings to the specific task—target‑aware representation learning—improves performance across both modalities, various tabular learners, encoder sizes, and embedding dimensions. MulTaBench is the largest image‑tabular benchmark to date, covering high‑impact domains such as healthcare and e‑commerce, and aims to spur research into joint modeling and multimodal tabular foundation models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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