EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding

Chart understanding enables automated data analysis for humans, which requires models to achieve highly accurate visual comprehension. While existing Visual Language Models (VLMs) have shown progress in chart understanding, the lack of high-quality training data and comprehensive evaluation benchmarks hinders VLM chart comprehension. In this paper, we introduce EvoChart, a novel self-training method for generating synthetic chart data to enhance VLMs' capabilities in real-world chart comprehension. We also propose EvoChart-QA, a noval benchmark for measuring models' chart comprehension abilities in real-world scenarios. Specifically, EvoChart is a unique self-training data synthesis approach that simultaneously produces high-quality training corpus and a high-performance chart understanding model. EvoChart-QA consists of 650 distinct real-world charts collected from 140 different websites and 1,250 expert-curated questions that focus on chart understanding. Experimental results on various open-source and proprietary VLMs tested on EvoChart-QA demonstrate that even the best proprietary model, GPT-4o, achieves only 49.8% accuracy. Moreover, the EvoChart method significantly boosts the performance of open-source VLMs on real-world chart understanding tasks, achieving 54.2% accuracy on EvoChart-QA.

Authors

• Muye Huang School of Computer Science and Technology, Xi’an Jiaotong University MOE KLINNS Lab, Xi’an Jiaotong University
• Han Lai School of Computer Science and Technology, Xi’an Jiaotong University MOE KLINNS Lab, Xi’an Jiaotong University
• Xinyu Zhang School of Computer Science and Technology, Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering
• Wenjun Wu School of Computer Science and Technology, Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering
• Jie Ma Huazhong University of Science and Technology
• Lingling Zhang School of Computer Science and Technology, Xi’an Jiaotong University MOE KLINNS Lab, Xi’an Jiaotong University
• Jun Liu

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