@inproceedings{niu-etal-2025-chart2code53,
    title = "{C}hart2{C}ode53: A Large-Scale Diverse and Complex Dataset for Enhancing Chart-to-Code Generation",
    author = "Niu, Tianhao  and
      Cui, Yiming  and
      Wang, Baoxin  and
      Xu, Xiao  and
      Yao, Xin  and
      Zhu, Qingfu  and
      Wu, Dayong  and
      Wang, Shijin  and
      Che, Wanxiang",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.799/",
    pages = "15839--15855",
    ISBN = "979-8-89176-332-6",
    abstract = "Chart2code has recently received significant attention in the multimodal community due to its potential to reduce the burden of visualization and promote a more detailed understanding of charts. However, existing Chart2code-related training datasets suffer from at least one of the following issues: (1) limited scale, (2) limited type coverage, and (3) inadequate complexity. To address these challenges, we seek more diverse sources that better align with real-world user distributions and propose dual data synthesis pipelines: (1) synthesize based on online plotting code. (2) synthesize based on chart images in the academic paper. We create a large-scale Chart2code training dataset Chart2code53, including 53 chart types, 130K Chart-code pairs based on the pipeline. Experimental results demonstrate that even with few parameters, the model finetuned on Chart2code53 achieves state-of-the-art performance on multiple Chart2code benchmarks within open-source models."
}