ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering
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Cited by 57 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Charts are ubiquitous, as people often use them to analyze data, answer questions, and discover critical insights. However, performing complex analytical tasks with charts requires significant perceptual and cognitive effort. Chart Question Answering (CQA) systems automate this process by enabling models to interpret and reason with visual representations of data. However, existing benchmarks like ChartQA lack real-world diversity and have recently shown performance saturation with modern large vision-language models (LVLMs). To address these limitations, we introduce ChartQAPro, a new benchmark that includes 1,341 charts from 157 diverse sources, spanning various chart types, including infographics and dashboards, and featuring 1,948 questions in various types, such as multiple-choice, conversational, hypothetical, and unanswerable questions, to better reflect real-world challenges. Our evaluations with 21 models show a substantial performance drop for LVLMs on ChartQAPro; e.g., Claude Sonnet 3.5 scores 90.5% on ChartQA but only 55.81% on ChartQAPro, underscoring the complexity of chart reasoning. We complement our findings with detailed error analyses and ablation studies, identifying key challenges and opportunities for advancing LVLMs in chart understanding and reasoning. We release ChartQAPro at https://github.com/vis-nlp/ChartQAPro.
Key facts. Cited by 108 papers in the map (data through 14 Oct 2026). Builds on 26 of its 42 references that are in the map. First posted on arXiv 7 Apr 2025.
Builds on
26 of 42 references.
References · 26
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
- 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
- 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
- 2024LLaVA-OneVision: Easy Visual Task Transfercited by 2,719
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- 2024Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scalingcited by 1,599
- 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoningcited by 1,479
- 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,076
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- 2018CoQA: A Conversational Question Answering Challengecited by 683
- 2019Scene Text Visual Question Answeringcited by 331
- 2019PlotQA: Reasoning over Scientific Plotscited by 321
- 2017FigureQA: An Annotated Figure Dataset for Visual Reasoningcited by 319
- 2024CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMscited by 234
- 2022Chart-to-Text: A Large-Scale Benchmark for Chart Summarizationcited by 169
- 2023UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoningcited by 160
- 2024ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wildcited by 79
- 2024ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoningcited by 68
- 2022OpenCQA: Open-ended Question Answering with Chartscited by 61
- 2019LEAF-QA: Locate, Encode & Attend for Figure Question Answeringcited by 54
- 2023Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checkingcited by 35
- 2024DataNarrative: Automated Data-Driven Storytelling with Visualizations and Textscited by 25
- 2019Searching the Visual Style and Structure of D3 Visualizationscited by 11
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