In-Depth and In-Breadth: Pre-training Multimodal Language Models Customized for Comprehensive Chart Understanding

• URL: http://arxiv.org/abs/2507.14298v1
• Date: Fri, 18 Jul 2025 18:15:09 GMT
• Title: In-Depth and In-Breadth: Pre-training Multimodal Language Models Customized for Comprehensive Chart Understanding
• Authors: Wan-Cyuan Fan, Yen-Chun Chen, Mengchen Liu, Alexander Jacobson, Lu Yuan, Leonid Sigal,
• Abstract summary: We introduce ChartScope, an LVLM optimized for in-depth chart comprehension across diverse chart types.<n>We propose an efficient data generation pipeline that synthesizes paired data for a wide range of chart types.<n>We also establish ChartDQA, a new benchmark for evaluating not only question-answering at different levels but also underlying data understanding.
• Score: 113.17601814293722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent methods for customizing Large Vision Language Models (LVLMs) for domain-specific tasks have shown promising results in scientific chart comprehension. However, existing approaches face two major limitations: First, they rely on paired data from only a few chart types, limiting generalization to wide range of chart types. Secondly, they lack targeted pre-training for chart-data alignment, which hampers the model's understanding of underlying data. In this paper, we introduce ChartScope, an LVLM optimized for in-depth chart comprehension across diverse chart types. We propose an efficient data generation pipeline that synthesizes paired data for a wide range of chart types, along with a novel Dual-Path training strategy that enabling the model to succinctly capture essential data details while preserving robust reasoning capabilities by incorporating reasoning over the underlying data. Lastly, we establish ChartDQA, a new benchmark for evaluating not only question-answering at different levels but also underlying data understanding. Experimental results demonstrate that ChartScope significantly enhances comprehension on a wide range of chart types. The code and data are available at https://davidhalladay.github.io/chartscope_demo.

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