mlsanityData to 7 Oct 2026

Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts

arXiv 2604.21344 ↗First posted 23 Apr 2026cs.CL cs.AI cs.CVcited by 0

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Abstract (arXiv)

Charts are widely used to present complex information. Deriving meaningful insights in real-world contexts often requires interpreting multiple related charts together. Research on understanding multi-chart images has not been extensively explored. We introduce PolyChartQA, a mid-scale dataset specifically designed for question answering over multi-chart images. PolyChartQA comprises 534 multi-chart images (with a total of 2,297 sub-charts) sourced from peer-reviewed computer science research publications and 2,694 QA pairs. We evaluate the performance of nine state-of-the-art Multimodal Language Models (MLMs) on PolyChartQA across question type, difficulty, question source, and key structural characteristics of multi-charts. Our results show a 27.4% LLM-based accuracy (L-Accuracy) drop on human-authored questions compared to MLM-generated questions, and a 5.39% L-accuracy gain with our proposed prompting method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 33 of its 51 references that are in the map. First posted on arXiv 23 Apr 2026.

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References · 33

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21 more
  1. 2025ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answeringcited by 105
  2. 2024ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wildcited by 79
  3. 2024SPIQA: A Dataset for Multimodal Question Answering on Scientific Paperscited by 75
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  8. 2023DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding