POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering

ACL ARR 2026 January Submission5459 Authors

Keywords: Chart Understanding, Multilinguality, Vision-Language Models

Abstract:

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.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: multimodality, vision question answering, cross-modal information extraction

Contribution Types: Model analysis & interpretability, Data resources

Languages Studied: English, Chinese, Russian, French, Spanish, Japanese, Arabic, Urdu, Hindi, Bengali

Submission Number: 5459