@inproceedings{hutchinson-etal-2025-chart,
    title = "Chart Question Answering from Real-World Analytical Narratives",
    author = "Hutchinson, Maeve  and
      Jianu, Radu  and
      Slingsby, Aidan  and
      Wood, Jo  and
      Madhyastha, Pranava",
    editor = "Zhao, Jin  and
      Wang, Mingyang  and
      Liu, Zhu",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-srw.50/",
    pages = "760--773",
    ISBN = "979-8-89176-254-1",
    abstract = "We present a new dataset for chart question answering (CQA) constructed from visualization notebooks. The dataset features real-world, multi-view charts paired with natural language questions grounded in analytical narratives. Unlike prior benchmarks, our data reflects ecologically valid reasoning workflows. Benchmarking state-of-the-art multimodal large language models reveals a significant performance gap, with GPT-4.1 achieving an accuracy of 69.3{\%}, underscoring the challenges posed by this more authentic CQA setting."
}