ChartAssistant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning
Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, Ping Luo
Abstract
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 chartrelated 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 methods, especially outperforming them on real-world chart data with zero-shot setting.