Chart Question Answering from Real-World Analytical Narratives

• URL: http://arxiv.org/abs/2507.01627v1
• Date: Wed, 02 Jul 2025 11:58:04 GMT
• Title: Chart Question Answering from Real-World Analytical Narratives
• Authors: Maeve Hutchinson, Radu Jianu, Aidan Slingsby, Jo Wood, Pranava Madhyastha,
• Abstract summary: We present a new dataset for chart question answering (CQA) constructed from visualization notebooks.<n>The dataset features real-world, multi-view charts paired with natural language questions grounded in analytical narratives.
• Score: 5.051297047598238
• License: http://creativecommons.org/licenses/by/4.0/
• 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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