MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image

TL;DR

MulTaBench introduces a comprehensive benchmark of 40 datasets for multimodal tabular learning with text and images, emphasizing task-specific tuning of embeddings to improve predictive performance.

Contribution

It presents the largest image-tabular benchmark to date, highlighting the importance of target-aware representations and providing a platform for developing multimodal tabular foundation models.

Findings

Target-aware representation tuning improves performance across modalities.

Benchmark spans healthcare and e-commerce domains.

Task-specific tuning reduces variance and enhances generalization.

Abstract

Tabular Foundation Models have recently established the state of the art in supervised tabular learning, by leveraging pretraining to learn generalizable representations of numerical and categorical structured data. However, they lack native support for unstructured modalities such as text and image, and rely on frozen, pretrained embeddings to process them. On established Multimodal Tabular Learning benchmarks, we show that tuning the embeddings to the task improves performance. Existing benchmarks, however, often focus on the mere co-occurrence of modalities; this leads to high variance across datasets and masks the benefits of task-specific tuning. To address this gap, we introduce MulTaBench, a benchmark of 40 datasets, split equally between image-tabular and text-tabular tasks. We focus on predictive tasks where the modalities provide complementary predictive signal, and where generic embeddings lose critical information, necessitating Target-Aware Representations that are aligned with the task. Our experimental results demonstrate that the gains from target-aware representation tuning generalize across both text and image modalities, several tabular learners, encoder scales, and embedding dimensions. MulTaBench constitutes the largest image-tabular benchmarking effort to date, spanning high-impact domains such as healthcare and e-commerce. It is designed to enable the research of novel architectures which incorporate joint modeling and target-aware representations, paving the way for the development of novel Multimodal Tabular Foundation Models.