  Charts play a vital role in data visualization, understanding data patterns,
and informed decision-making. However, their unique combination of graphical
elements (e.g., bars, lines) and textual components (e.g., labels, legends)
poses challenges for general-purpose multimodal models. While vision-language
models trained on chart data excel in comprehension, they struggle with
generalization. To address these challenges, we propose ChartAssistant, a
chart-based vision-language model for universal chart comprehension and
reasoning. ChartAssistant leverages ChartSFT, a comprehensive dataset covering
diverse chart-related tasks with basic (e.g. bars and pies) and specialized
(e.g. radars, and bubbles) chart types. It undergoes a two-stage training
process, starting with pre-training on chart-to-table parsing to align chart
and text, followed by multitask instruction-following fine-tuning. This
approach enables ChartAssistant to achieve competitive performance across
various chart tasks. Experimental results demonstrate significant performance
gains over the state-of-the-art UniChart and Chartllama method, especially
outperforming them on real-world chart data with zero-shot setting. The code
and data are available at https://github.com/OpenGVLab/ChartAst.
