Multimodal Tabular Reasoning with Privileged Structured Information

TL;DR

This paper introduces Turbo, a framework that improves multimodal large language models' ability to perform reasoning over table images by leveraging privileged structured information during training, achieving state-of-the-art results.

Contribution

The paper proposes Turbo, a novel multimodal reasoning framework that uses privileged structured data and a reasoning trace generator to enhance reasoning over table images.

Findings

Turbo achieves +7.2% performance over previous SOTA.

The framework effectively aligns structured information with visual data.

Limited data (9k samples) suffices for high performance.

Abstract

Tabular reasoning involves multi-step information extraction and logical inference over tabular data. While recent advances have leveraged large language models (LLMs) for reasoning over structured tables, such high-quality textual representations are often unavailable in real-world settings, where tables typically appear as images. In this paper, we tackle the task of tabular reasoning from table images, leveraging privileged structured information available during training to enhance multimodal large language models (MLLMs). The key challenges lie in the complexity of accurately aligning structured information with visual representations, and in effectively transferring structured reasoning skills to MLLMs despite the input modality gap. To address these, we introduce TabUlar Reasoning with Bridged infOrmation ({\sc Turbo}), a new framework for multimodal tabular reasoning with privileged structured tables. {\sc Turbo} benefits from a structure-aware reasoning trace generator based on DeepSeek-R1, contributing to high-quality modality-bridged data. On this basis, {\sc Turbo} repeatedly generates and selects the advantageous reasoning paths, further enhancing the model's tabular reasoning ability. Experimental results demonstrate that, with limited ($9$k) data, {\sc Turbo} achieves state-of-the-art performance ($+7.2\%$ vs. previous SOTA) across multiple datasets.