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

Paper · 2026 · Yichen Xu, Liangyu Chen, Liang Zhang et al.

Summary

Benchmarks for chart understanding are almost entirely in English, so little is known about how large vision-language models (LVLMs) handle charts in other languages. POLYCHARTQA fills this gap as the first large-scale multilingual chart question answering benchmark. Its 10 languages are Arabic, Bengali, Chinese, English, French, Hindi, Japanese, Russian, Spanish and Urdu.

The authors built it in two stages. First, they cleaned English seed data and split each chart into a structured JSON specification plus reusable rendering code. Second, an LLM translated the JSON and the QA pairs, and the original code re-rendered the charts in each target language. Automated consistency checks and human verification controlled quality.

Testing state-of-the-art LVLMs showed a large drop from English to other languages, especially low-resource and non-Latin-script ones. Diagnostic experiments trace most of this drop to reading language-specific text in images, not to reasoning. Fine-tuning on the companion POLYCHARTQA-Train set improved results across model sizes and families. The resource supports building chart-capable LVLMs for non-English users.

Key points

  • Scale: the test set has 22,606 charts and 26,151 QA pairs across 16 chart types. POLYCHARTQA-Train adds about 751K QA pairs over 131K charts.
  • Seed data and quality control:
    • Seeds come from ChartQA, ChartX (test) and ChartQA, ChartLlama (train).
    • Gemini-2.5-Pro checked the seed answers.
    • A manual audit of 10% of the cleaned data passed at over 98%.
  • Error sources: OCR failures (27.8–51.4%) and language bias (26.8–61.5%) dominate. Reasoning gaps account for 9.7–22.2% and hallucination for under 7%. Multi-axes, 3D-bar and candlestick charts, along with arithmetic questions, are the hardest.
  • Text transcription: Qwen2.5-VL-7B's chart text transcription F1 falls from 55.27 in English to 16.45 in Urdu. Numerical value extraction stays fairly stable across languages.
  • Perception vs. reasoning: feeding models gold JSON instead of images shows the perception gap far exceeds the reasoning gap. GPT-4o loses 14.99 points from perception versus 6.03 from reasoning; Qwen2.5-VL-3B loses 19.33 versus 6.50.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 3Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 828 worksVision & language · 657 worksQuestion answering · 441 works

Cite

Cite as Yichen Xu, Liangyu Chen, Liang Zhang, Zihao Yue, Jianzhe Ma, Wenxuan Wang and Qin Jin. 2026. POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.2043

@inproceedings{xu2026polychartqa,
  title = {{POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering}},
  author = {Yichen Xu, Liangyu Chen, Liang Zhang, Zihao Yue, Jianzhe Ma, Wenxuan Wang and Qin Jin},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.2043},
  url = {https://doi.org/10.18653/v1/2026.acl-long.2043}
}

References

66 references, 27 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed Awadallah, Ammar Ahmad Awan, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, and 1 others. 2024a. Phi-3 technical report: A highly capable language model locally on your phone. arXiv preprint arXiv:2404.14219.
  2. Marah Abdin, Jyoti Aneja, Harkirat Behl, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell J Hewett, Mojan Javaheripi, Piero Kauffmann, and 1 others. 2024b. Phi-4 technical report. arXiv preprint arXiv:2412.08905.
  3. Nahid Alam, Karthik Reddy Kanjula, Surya Guthikonda, Timothy Chung, Bala Krishna S Vegesna, Abhip-sha Das, Anthony Susevski, Ryan Sze-Yin Chan, SM Uddin, Shayekh Bin Islam, and 1 others. 2024. Maya: An instruction finetuned multilingual multimodal model. arXiv preprint arXiv:2412.07112. — [arXiv:2412.07112](https://arxiv.org/abs/2412.07112) find
  4. Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023. Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond. arXiv preprint arXiv:2308.12966.
  5. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, and 1 others. 2025. Qwen2.5-vl technical report. arXiv preprint arXiv:2502.13923.
  6. Satanjeev Banerjee and Alon Lavie. 2005. Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pages 65–72.
  7. Lucas Beyer, Andreas Steiner, André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, and 1 others. 2024. Paligemma: A versatile 3b vlm for transfer. arXiv preprint arXiv:2407.07726. find
  8. Soravit Changpinyo, Linting Xue, Michal Yarom, Ashish V Thapliyal, Idan Szpektor, Julien Amelot, Xi Chen, and Radu Soricut. 2022. Maxm: Towards multilingual visual question answering. arXiv preprint arXiv:2209.05401. find
  9. Jinyue Chen, Lingyu Kong, Haoran Wei, Chenglong Liu, Zheng Ge, Liang Zhao, Jianjian Sun, Chunrui Han, and Xiangyu Zhang. 2024a. Onechart: Purify the chart structural extraction via one auxiliary token. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 147–155. find
  10. Nuo Chen, Zinan Zheng, Ning Wu, Ming Gong, Dongmei Zhang, and Jia Li. 2024b. Breaking language barriers in multilingual mathematical reasoning: Insights and observations. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 7001–7016. find
  11. Xi Chen, Xiao Wang, Soravit Changpinyo, AJ Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, and 1 others. 2022. Pali: A jointly-scaled multilingual language-image model. arXiv preprint arXiv:2209.06794.
  12. Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, and 1 others. 2024c. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. arXiv preprint arXiv:2412.05271.
  13. Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye, Zhangwei Gao, Erfei Cui, Wenwen Tong, Kongzhi Hu, Jiapeng Luo, Zheng Ma, and 1 others. 2024d. How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites. Science China Information Sciences, 67(12):220101.
  14. Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, and 1 others. 2024e. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and P…
  15. Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, and 1 others. 2025. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities. arX…
  16. Rocktim Das, Simeon Hristov, Haonan Li, Dimitar Dimitrov, Ivan Koychev, and Preslav Nakov. 2024. Exams-v: A multi-discipline multilingual multimodal exam benchmark for evaluating vision language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volum… find
  17. Gregor Geigle, Abhay Jain, Radu Timofte, and Goran Glavaš. 2024. mblip: Efficient bootstrapping of multilingual vision-llms. In Proceedings of the 3rd Workshop on Advances in Language and Vision Research (ALVR), pages 7–25. find
  18. Gregor Geigle, Florian Schneider, Carolin Holtermann, Chris Biemann, Radu Timofte, Anne Lauscher, and Goran Glavaš. 2025. Centurio: On drivers of multilingual ability of large vision-language model. arXiv preprint arXiv:2501.05122.
  19. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783.
  20. Yucheng Han, Chi Zhang, Xin Chen, Xu Yang, Zhibin Wang, Gang Yu, Bin Fu, and Hanwang Zhang. 2023. Chartllama: A multimodal llm for chart understanding and generation. arXiv preprint arXiv:2311.16483. find
  21. Ahmed Heakl, Abdullah Sohail, Mukul Ranjan, Rania Hossam, Ghazi Ahmed, Mohamed El-Geish, Omar Maher, Zhiqiang Shen, Fahad Khan, and Salman Khan. 2025. Kitab-bench: A comprehensive multi-domain benchmark for arabic ocr and document understanding. arXiv preprint arXiv:2502.14949.
  22. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, and 1 others. 2022. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3.
  23. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, and 1 others. 2024. Gpt-4o system card. arXiv preprint arXiv:2410.21276.
  24. Kushal Kafle, Brian Price, Scott Cohen, and Christopher Kanan. 2018. Dvqa: Understanding data visualizations via question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5648–5656. find
  25. Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Ákos Kádár, Adam Trischler, and Yoshua Bengio. 2017. Figureqa: An annotated figure dataset for visual reasoning. arXiv preprint arXiv:1710.07300. find
  26. Shankar Kantharaj, Xuan Long Do, Rixie Tiffany Leong, Jia Qing Tan, Enamul Hoque, and Shafiq Joty. 2022a. Opencqa: Open-ended question answering with charts. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11817–11837. find
  27. Shankar Kantharaj, Rixie Tiffany Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, and Shafiq Joty. 2022b. Chart-to-text: A large-scale benchmark for chart summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), page…
  28. Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, and 1 others. 2024. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326.
  29. Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730–19742. PMLR.
  30. Fangyu Liu, Emanuele Bugliarello, Edoardo Maria Ponti, Siva Reddy, Nigel Collier, and Desmond Elliott. 2021. Visually grounded reasoning across languages and cultures. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10467–10485. find
  31. Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, and Dong Yu. 2024a. Mmc: Advancing multimodal chart understanding with large-scale instruction tuning. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computation…
  32. Omar Moured, Sara Alzalabny, Anas Osman, Thorsten Schwarz, Karin Müller, and Rainer Stiefelhagen. 2024. Chartformer: A large vision language model for converting chart images into tactile accessible svgs. In International Conference on Computers Helping People with Special Needs, pages 299–305. Spri… find
  33. Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. 2024b. Lla-vanext: Improved reasoning, ocr, and world knowledge. find
  34. Jonas Pfeiffer, Gregor Geigle, Aishwarya Kamath, Jan-Martin Steitz, Stefan Roth, Ivan Vulić, and Iryna Gurevych. 2022. xgqa: Cross-lingual visual question answering. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2497–2511. find
  35. Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, and 1 others. 2024c. Mmbench: Is your multi-modal model an all-around player? In European conference on computer vision, pages 216–233. Springer.
  36. Chen Qiu, Dan Oneat , ă, Emanuele Bugliarello, Stella Frank, and Desmond Elliott. 2022. Multilingual multimodal learning with machine translated text. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4178–4193. find
  37. Muhammad Maaz, Hanoona Rasheed, Abdelrahman Shaker, Salman Khan, Hisham Cholakal, Rao M Anwer, Tim Baldwin, Michael Felsberg, and Fahad S Khan. 2024. Palo: A polyglot large multimodal model for 5b people. arXiv preprint arXiv:2402.14818. — [arXiv:2402.14818](https://arxiv.org/abs/2402.14818) find
  38. Raian Rahman, Rizvi Hasan, Abdullah Al Farhad, Md Tahmid Rahman Laskar, Md Hamjajul Ashmafee, and Abu Raihan Mostofa Kamal. 2023. Chartsumm: A comprehensive benchmark for automatic chart summarization of long and short summaries. arXiv preprint arXiv:2304.13620.
  39. Ahmed Masry, Xuan Long Do, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. 2022. Chartqa: A benchmark for question answering about charts with visual and logical reasoning. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2263– 2279.
  40. David Romero, Chenyang Lyu, Haryo Wibowo, Santiago Góngora, Aishik Mandal, Sukannya Purkayastha, Jesus-German Ortiz-Barajas, Emilio Cueva, Jinheon Baek, Soyeong Jeong, and 1 others. 2024. Cvqa: Culturally-diverse multilingual visual question answering benchmark. Advances in Neural Information Proces… find
  41. Ahmed Masry, Mohammed Saidul Islam, Mahir Ahmed, Aayush Bajaj, Firoz Kabir, Aaryaman Kartha, Md Tahmid Rahman Laskar, Mizanur Rahman, Shadikur Rahman, Mehrad Shahmohammadi, and 1 others. 2025a. Chartqapro: A more diverse and challenging benchmark for chart question answering. arXiv preprint arXiv:25… find
  42. Ahmed Masry, Mehrad Shahmohammadi, Md Rizwan Parvez, Enamul Hoque, and Shafiq Joty. 2024. Chartinstruct: Instruction tuning for chart comprehension and reasoning. arXiv preprint arXiv:2403.09028. find
  43. Ahmed Masry, Megh Thakkar, Aayush Bajaj, Aaryaman Kartha, Enamul Hoque, and Shafiq Joty. 2025b. Chartgemma: Visual instruction-tuning for chart reasoning in the wild. In Proceedings of the 31st International Conference on Computational Linguistics: Industry Track, pages 625–643. find
  44. Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, and Ping Luo. 2024. Chartassisstant: A universal chart multimodal language model via chart-to-table pre-training and multitask instruction tuning. arXiv preprint arXiv:2401.02384. — [arXiv:2401.02384](https://arxiv.org/abs/2401.02384) find
  45. Nitesh Methani, Pritha Ganguly, Mitesh M Khapra, and Pratyush Kumar. 2020. Plotqa: Reasoning over scientific plots. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1527–1536. find
  46. Uri Shaham, Jonathan Herzig, Roee Aharoni, Idan Szpektor, Reut Tsarfaty, and Matan Eyal. 2024. Multilingual instruction tuning with just a pinch of multilinguality. arXiv preprint arXiv:2401.01854.
  47. Risa Shinoda, Kuniaki Saito, Shohei Tanaka, Tosho Hirasawa, and Yoshitaka Ushiku. 2024. Sbs figures: Pre-training figure qa from stage-by-stage synthesized images. arXiv preprint arXiv:2412.17606. — [arXiv:2412.17606](https://arxiv.org/abs/2412.17606) find
  48. Andreas Steiner, André Susano Pinto, Michael Tschannen, Daniel Keysers, Xiao Wang, Yonatan Bitton, Alexey Gritsenko, Matthias Minderer, Anthony Sherbondy, Shangbang Long, and 1 others. 2024. Paligemma 2: A family of versatile vlms for transfer. arXiv preprint arXiv:2412.03555. — [arXiv:2412.03555](https://arxiv.org/abs/2412.03555) find
  49. Benny Tang, Angie Boggust, and Arvind Satyanarayan. 2023. Vistext: A benchmark for semantically rich chart captioning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7268–7298. find
  50. Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu, Shu Wei, Chunhui Lin, Wanqing Li, Mohamad Fitri Faiz Bin Mahmood, Hao Feng, Zhen Zhao, and 1 others. 2024. Mtvqa: Benchmarking multilingual text-centric visual question answering. arXiv preprint arXiv:2405.11985. find
  51. Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, and 1 others. 2024a. Gemma: Open models based on gemini research and technology. arXiv preprint arXiv:2403.08295. find
  52. Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, and 1 others. 2024b. Gemma 2: Improving open language models at a practical size. arXiv preprint arXiv:2408.00118. find
  53. Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana, Noor Ahsan, Nevasini Sasikumar, Omkar Thawakar, Henok Biadglign Ademtew, Yahya Hmaiti, Amandeep Kumar, Kartik Kukreja, and 1 others. 2025. All languages matter: Evaluating lmms on culturally diverse 100 languages. In Proceedings of the Computer V… find
  54. Hongyu Wang, Jiayu Xu, Senwei Xie, Ruiping Wang, Jialin Li, Zhaojie Xie, Bin Zhang, Chuyan Xiong, and Xilin Chen. 2024a. M4u: Evaluating multilingual understanding and reasoning for large multimodal models. arXiv preprint arXiv:2405.15638. — [arXiv:2405.15638](https://arxiv.org/abs/2405.15638) find
  55. Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, and 1 others. 2024b. Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution. arXiv preprint arXiv:2409.12191.
  56. Zirui Wang, Mengzhou Xia, Luxi He, Howard Chen, Yitao Liu, Richard Zhu, Kaiqu Liang, Xindi Wu, Haotian Liu, Sadhika Malladi, and 1 others. 2024c. Charxiv: Charting gaps in realistic chart understanding in multimodal llms. Advances in Neural Information Processing Systems, 37:113569–113697. find
  57. Jingxuan Wei, Nan Xu, Junnan Zhu, Gaowei Wu, Qi Chen, Bihui Yu, Lei Wang, and 1 others. 2025. Chartmind: A comprehensive benchmark for complex real-world multimodal chart question answering. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 4555–4569. find
  58. Zhiyu Wu, Xiaokang Chen, Zizheng Pan, Xingchao Liu, Wen Liu, Damai Dai, Huazuo Gao, Yiyang Ma, Chengyue Wu, Bingxuan Wang, and 1 others. 2024. Deepseek-vl2: Mixture-of-experts vision-language models for advanced multimodal understanding. arXiv preprint arXiv:2412.10302.
  59. Renqiu Xia, Bo Zhang, Haoyang Peng, Hancheng Ye, Xiangchao Yan, Peng Ye, Botian Shi, Yu Qiao, and Junchi Yan. 2023. Structchart: Perception, structuring, reasoning for visual chart understanding. arXiv preprint arXiv:2309.11268. — [arXiv:2309.11268](https://arxiv.org/abs/2309.11268) find
  60. Renqiu Xia, Bo Zhang, Hancheng Ye, Xiangchao Yan, Qi Liu, Hongbin Zhou, Zijun Chen, Peng Ye, Min Dou, Botian Shi, and 1 others. 2024. Chartx & chartvlm: A versatile benchmark and foundation model for complicated chart reasoning. arXiv preprint arXiv:2402.12185. — [arXiv:2402.12185](https://arxiv.org/abs/2402.12185) find
  61. Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Yun Xing, Junjue Wang, Huitao Li, Xin Li, Kunyu Yu, and 1 others. 2025. Mmlu-prox: A multilingual benchmark for advanced large language model evaluation. arXiv preprint arXiv:2503.10497.
  62. Cheng Yang, Chufan Shi, Yaxin Liu, Bo Shui, Junjie Wang, Mohan Jing, Linran XU, Xinyu Zhu, Siheng Li, Yuxiang Zhang, and 1 others. Chartmimic: Evaluating lmm’s cross-modal reasoning capability via chart-to-code generation. In The Thirteenth International Conference on Learning Representations. Xinmi… find
  63. Xiang Yue, Yueqi Song, Akari Asai, Seungone Kim, Jean de Dieu Nyandwi, Simran Khanuja, Anjali Kantharuban, Lintang Sutawika, Sathyanarayanan Ramamoorthy, and Graham Neubig. 2024. Pangea: A fully open multilingual multimodal llm for 39 languages. In The Thirteenth International Conference on Learning… find
  64. Liang Zhang, Anwen Hu, Haiyang Xu, Ming Yan, Yichen Xu, Qin Jin, Ji Zhang, and Fei Huang. 2024. Tinychart: Efficient chart understanding with visual token merging and program-of-thoughts learning. arXiv preprint arXiv:2404.16635. — [arXiv:2404.16635](https://arxiv.org/abs/2404.16635) find
  65. Wenxuan Zhang, Mahani Aljunied, Chang Gao, Yew Ken Chia, and Lidong Bing. 2023. M3exam: A multilingual, multimodal, multilevel benchmark for examining large language models. Advances in Neural Information Processing Systems, 36:5484–5505. find
  66. Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Yuchen Duan, Hao Tian, Weijie Su, Jie Shao, and 1 others. 2025. Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.10479.

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