Chart Question Answering from Real-World Analytical Narratives

Maeve Hutchinson, Radu Jianu, Aidan Slingsby, Jo Wood, Pranava Madhyastha
7/2/2025
cs.CL

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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.

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Cite this paper

@article{hutchinson2025chart,
  title  = {Chart Question Answering from Real-World Analytical Narratives},
  author = {Maeve Hutchinson and Radu Jianu and Aidan Slingsby and Jo Wood and Pranava Madhyastha},
  year   = {2025},
  eprint = {2507.01627},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.01627v1}
}

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