@inproceedings{masry-etal-2025-chartqapro,
title = "{C}hart{QAP}ro: A More Diverse and Challenging Benchmark for Chart Question Answering",
author = "Masry, Ahmed and
Islam, Mohammed Saidul and
Ahmed, Mahir and
Bajaj, Aayush and
Kabir, Firoz and
Kartha, Aaryaman and
Laskar, Md Tahmid Rahman and
Rahman, Mizanur and
Rahman, Shadikur and
Shahmohammadi, Mehrad and
Thakkar, Megh and
Parvez, Md Rizwan and
Hoque, Enamul and
Joty, Shafiq",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.978/",
doi = "10.18653/v1/2025.findings-acl.978",
pages = "19123--19151",
ISBN = "979-8-89176-256-5",
abstract = "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 99 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."
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<abstract>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 99 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.</abstract>
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%0 Conference Proceedings
%T ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering
%A Masry, Ahmed
%A Islam, Mohammed Saidul
%A Ahmed, Mahir
%A Bajaj, Aayush
%A Kabir, Firoz
%A Kartha, Aaryaman
%A Laskar, Md Tahmid Rahman
%A Rahman, Mizanur
%A Rahman, Shadikur
%A Shahmohammadi, Mehrad
%A Thakkar, Megh
%A Parvez, Md Rizwan
%A Hoque, Enamul
%A Joty, Shafiq
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F masry-etal-2025-chartqapro
%X 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 99 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.
%R 10.18653/v1/2025.findings-acl.978
%U https://aclanthology.org/2025.findings-acl.978/
%U https://doi.org/10.18653/v1/2025.findings-acl.978
%P 19123-19151
Markdown (Informal)
[ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering](https://aclanthology.org/2025.findings-acl.978/) (Masry et al., Findings 2025)
ACL
- Ahmed Masry, Mohammed Saidul Islam, Mahir Ahmed, Aayush Bajaj, Firoz Kabir, Aaryaman Kartha, Md Tahmid Rahman Laskar, Mizanur Rahman, Shadikur Rahman, Mehrad Shahmohammadi, Megh Thakkar, Md Rizwan Parvez, Enamul Hoque, and Shafiq Joty. 2025. ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering. In Findings of the Association for Computational Linguistics: ACL 2025, pages 19123–19151, Vienna, Austria. Association for Computational Linguistics.