@inproceedings{zhang-etal-2026-realchart2code,
title = "{R}eal{C}hart2{C}ode: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation",
author = "Zhang, Jiajun and
Li, Yuying and
Li, Zhixun and
Guo, Xingyu and
Wu, Jingzhuo and
Zheng, Leqi and
Yang, Yiran and
Zhang, Jianke and
Li, Qingbin and
Yan, Shannan and
Jia, Changguo and
Wu, Junfei and
Wang, Zilei and
Liu, Qiang and
Wang, Liang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1945/",
pages = "41995--42032",
ISBN = "979-8-89176-390-6",
abstract = "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 \textbf{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."
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<abstract>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 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.</abstract>
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%0 Conference Proceedings
%T RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation
%A Zhang, Jiajun
%A Li, Yuying
%A Li, Zhixun
%A Guo, Xingyu
%A Wu, Jingzhuo
%A Zheng, Leqi
%A Yang, Yiran
%A Zhang, Jianke
%A Li, Qingbin
%A Yan, Shannan
%A Jia, Changguo
%A Wu, Junfei
%A Wang, Zilei
%A Liu, Qiang
%A Wang, Liang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhang-etal-2026-realchart2code
%X 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 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.
%U https://aclanthology.org/2026.acl-long.1945/
%P 41995-42032
Markdown (Informal)
[RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation](https://aclanthology.org/2026.acl-long.1945/) (Zhang et al., ACL 2026)
ACL
- Jiajun Zhang, Yuying Li, Zhixun Li, Xingyu Guo, Jingzhuo Wu, Leqi Zheng, Yiran Yang, Jianke Zhang, Qingbin Li, Shannan Yan, Changguo Jia, Junfei Wu, Zilei Wang, Qiang Liu, and Liang Wang. 2026. RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 41995–42032, San Diego, California, United States. Association for Computational Linguistics.