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

TL;DR

PolyChartQA is a comprehensive multilingual benchmark for chart question answering, addressing the lack of non-English chart understanding datasets and evaluating current models' performance across diverse languages.

Contribution

We introduce PolyChartQA, the first large-scale multilingual chart QA benchmark, and demonstrate its utility in evaluating and improving multilingual vision-language models.

Findings

Significant performance gap between English and other languages in chart understanding.

Fine-tuning on PolyChartQA-Train improves multilingual chart comprehension.

Benchmark enables development of more inclusive 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.