POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911361147076608 |
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| author | Xu, Yichen Chen, Liangyu Zhang, Liang Ma, Jianzhe Wang, Wenxuan Jin, Qin |
| author_facet | Xu, Yichen Chen, Liangyu Zhang, Liang Ma, Jianzhe Wang, Wenxuan Jin, Qin |
| contents | Charts are a universally adopted medium for data communication, yet existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences. To address this limitation, we introduce PolyChartQA, the first large-scale multilingual benchmark for chart question answering, comprising 22,606 charts and 26,151 QA pairs across 10 diverse languages. PolyChartQA is constructed through a scalable pipeline that enables efficient multilingual chart generation via data translation and code reuse, supported by LLM-based translation and rigorous quality control. We systematically evaluate multilingual chart understanding with PolyChartQA on state-of-the-art LVLMs and reveal a significant performance gap between English and other languages, particularly low-resource ones. Additionally, we introduce a companion multilingual chart question answering training set, PolyChartQA-Train, on which fine-tuning LVLMs yields substantial gains in multilingual chart understanding across diverse model sizes and architectures. Together, our benchmark provides a foundation for developing globally inclusive vision-language models capable of understanding charts across diverse linguistic contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11939 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering Xu, Yichen Chen, Liangyu Zhang, Liang Ma, Jianzhe Wang, Wenxuan Jin, Qin Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Multimedia Charts are a universally adopted medium for data communication, yet existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences. To address this limitation, we introduce PolyChartQA, the first large-scale multilingual benchmark for chart question answering, comprising 22,606 charts and 26,151 QA pairs across 10 diverse languages. PolyChartQA is constructed through a scalable pipeline that enables efficient multilingual chart generation via data translation and code reuse, supported by LLM-based translation and rigorous quality control. We systematically evaluate multilingual chart understanding with PolyChartQA on state-of-the-art LVLMs and reveal a significant performance gap between English and other languages, particularly low-resource ones. Additionally, we introduce a companion multilingual chart question answering training set, PolyChartQA-Train, on which fine-tuning LVLMs yields substantial gains in multilingual chart understanding across diverse model sizes and architectures. Together, our benchmark provides a foundation for developing globally inclusive vision-language models capable of understanding charts across diverse linguistic contexts. |
| title | POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2507.11939 |