RealChart2Code: Advancing Chart-to-Code Generation with Real Data and Multi-Task Evaluation
Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains. However, their ability to replicate complex, multi-panel visualizations from real-world data remains largely unassessed. To address this gap, we introduce \texttt{RealChart2Code}, a new large-scale benchmark with over 2,800 instances grounded in authentic datasets and featuring tasks with clear analytical intent. Crucially, it is the first benchmark to systematically evaluate chart generation from large-scale raw data and assess iterative code refinement in a multi-turn conversational setting. Our comprehensive evaluation of 14 leading VLMs on RealChart2Code reveals significant performance degradation compared to simpler benchmarks, highlighting their struggles with complex plot structures and authentic data. Our analysis uncovers a substantial performance gap between proprietary and open-weight models and confirms that even state-of-the-art VLMs often fail to accurately replicate intricate, multi-panel charts. These findings provide valuable insights into the current limitations of VLMs and guide future research directions. We release the benchmark and code at https://github.com/Speakn0w/RealChart2Code.
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Jiajun Zhang
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2026-03-31T09:29:30.164Z
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Yuying Li
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Zhixun Li
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Xingyu Guo
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Jingzhuo Wu
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Leqi Zheng
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Yiran Yang
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Jianke Zhang
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Qingbin Li
69ca51214c8e8981b7fa7c41
Shannan Yan
69ca51214c8e8981b7fa7c42
Zhetong Li
69ca51214c8e8981b7fa7c43
Changguo Jia
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Junfei Wu
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Zilei Wang
69ca51214c8e8981b7fa7c46
Qiang Liu
69ca51214c8e8981b7fa7c47
Liang Wang
RealChart2Code: Advancing Chart-to-Code Generation with Real Data and Multi-Task Evaluation
Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains. However, their ability to replicate complex, multi-panel visualizations from real-world data remains largely unassessed. To address this gap, we introduce \texttt{RealChart2Code}, a new large-scale benchmark with over 2,800 instances grounded in authentic datasets and featuring tasks with clear analytical intent. Crucially, it is the first benchmark to systematically evaluate chart generation from large-scale raw data and assess iterative code refinement in a multi-turn conversational setting. Our comprehensive evaluation of 14 leading VLMs on RealChart2Code reveals significant performance degradation compared to simpler benchmarks, highlighting their struggles with complex plot structures and authentic data. Our analysis uncovers a substantial performance gap between proprietary and open-weight models and confirms that even state-of-the-art VLMs often fail to accurately replicate intricate, multi-panel charts. These findings provide valuable insights into the current limitations of VLMs and guide future research directions. We release the benchmark and code at https://github.com/Speakn0w/RealChart2Code.
Vision-Language Models face significant challenges in generating complex multi-panel charts from real-world data, as demonstrated by a new large-scale benchmark that reveals performance gaps between proprietary and open-weight models.
Vision-Language Models chart generation multi-panel visualization real-world data large-scale benchmark iterative code refinement multi-turn conversational setting