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Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart understanding tasks.
Towards vqa models that can read
Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. 2019 · 2019
Earlier work this paper cites.
Plotqa: Reasoning over scientific plots
Nitesh Methani, Pritha Ganguly, Mitesh M Khapra, and Pratyush Kumar. 2020 · 2020
Earlier work this paper cites.
Scicap: Generating captions for scientific figures
Ting-Yao Hsu, C Lee Giles, and Ting-Hao’Kenneth’ Huang. 2021 · 2021
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021 · 2021
Earlier work this paper cites.
Chart-to-text: A large-scale benchmark for chart summarization
Shankar Kantharaj, Rixie Tiffany Ko Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, and Shafiq Joty. 2022 · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Earlier work this paper cites.
Chartllama: A multimodal llm for chart understanding and generation
Yucheng Han, Chi Zhang, Xin Chen, Xu Yang, Zhibin Wang, Gang Yu, Bin Fu, and Hanwang Zhang. 2023 · 2023
Earlier work this paper cites.
Unichart: A universal vision-language pretrained model for chart comprehension and reasoning
Ahmed Masry, Parsa Kavehzadeh, Xuan Long Do, Enamul Hoque, and Shafiq Joty. 2023 · 2023
Earlier work this paper cites.
Gpt-4v(ision) system card
OpenAI. 2023 · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. 2023 · 2023
Earlier work this paper cites.
Wizardlm: Empowering large language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023 · 2023
Earlier work this paper cites.
Sigmoid loss for language image pre-training
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. 2023a · 2023
Earlier work this paper cites.
Introducing the next generation of claude
Anthropic. 2024 · 2024
Cited alongside, same era.
Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks
Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. 2024 · 2024
Cited alongside, same era.
Deepseek-coder: When the large language model meets programming–the rise of code intelligence
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Yu Wu, YK Li, et al. 2024 · 2024
Cited alongside, same era.
Mmcode: Benchmarking multimodal large language models for code generation with visually rich programming problems
Kaixin Li, Yuchen Tian, Qisheng Hu, Ziyang Luo, Zhiyong Huang, and Jing Ma. 2024b · 2024
Cited alongside, same era.
Llava-next: Improved reasoning, ocr, and world knowledge
Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. 2024 · 2024
Cited alongside, same era.
Chartx & chartvlm: A versatile benchmark and foundation model for complicated chart reasoning
Renqiu Xia, Bo Zhang, Hancheng Ye, Xiangchao Yan, Qi Liu, Hongbin Zhou, Zijun Chen, Min Dou, Botian Shi, Junchi Yan, et al. 2024 · 2024
Later among the works it cites.
Scicap+: A knowledge augmented dataset to study the challenges of scientific figure captioning
Zhishen Yang, Raj Dabre, Hideki Tanaka, and Naoaki Okazaki. 2024 · 2024
Later among the works it cites.
Minicpm-v: A gpt-4v level mllm on your phone
Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, et al. 2024 · 2024
Later among the works it cites.
Web2code: A large-scale webpage-to-code dataset and evaluation framework for multimodal llms
Sukmin Yun, Haokun Lin, Rusiru Thushara, Mohammad Qazim Bhat, Yongxin Wang, Zutao Jiang, Mingkai Deng, Jinhong Wang, Tianhua Tao, Junbo Li, et al. 2024 · 2024
Later among the works it cites.
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Deepseek-vl: towards real-world vision-language understanding
Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, et al. 2024 · 2024
Cited alongside, same era.
Python is not always the best choice: Embracing multilingual program of thoughts
Xianzhen Luo, Qingfu Zhu, Zhiming Zhang, Libo Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, and Wanxiang Che. 2024 · 2024
Cited alongside, same era.
Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, and Ping Luo. 2024 · 2024
Cited alongside, same era.
Qwen2.5: A party of foundation models
Qwen Team. 2024 · 2024
Cited alongside, same era.
Chartmimic: Evaluating lmm’s cross-modal reasoning capability via chart-to-code generation
Chufan Shi, Cheng Yang, Yaxin Liu, Bo Shui, Junjie Wang, Mohan Jing, Linran Xu, Xinyu Zhu, Siheng Li, Yuxiang Zhang, et al. 2024 · 2024
Cited alongside, same era.
Design2code: How far are we from automating front-end engineering?
Chenglei Si, Yanzhe Zhang, Zhengyuan Yang, Ruibo Liu, and Diyi Yang. 2024 · 2024
Cited alongside, same era.
Chengyue Wu, Yixiao Ge, Qiushan Guo, Jiahao Wang, Zhixuan Liang, Zeyu Lu, Ying Shan, and Ping Luo. 2024 · 2024
Cited alongside, same era.
Is gpt-4v (ision) all you need for automating academic data visualization? exploring vision-language models’ capability in reproducing academic charts
Zhehao Zhang, Weicheng Ma, and Soroush Vosoughi. 2024d · 2024
Later among the works it cites.
Outline, then details: Syntactically guided coarse-to-fine code generation
Wenqing Zheng, S P Sharan, Ajay Jaiswal, Kevin Wang, Yihan Xi, Dejia Xu, and Zhangyang Wang. 2023 · 2024
Later among the works it cites.
Prism: Self-pruning intrinsic selection method for training-free multimodal data selection
Jinhe Bi, Yifan Wang, Danqi Yan, Xun Xiao, Artur Hecker, Volker Tresp, and Yunpu Ma. 2025 · 2025
Closest in time.
Llava-uhd: an lmm perceiving any aspect ratio and high-resolution images
Zonghao Guo, Ruyi Xu, Yuan Yao, Junbo Cui, Zanlin Ni, Chunjiang Ge, Tat-Seng Chua, Zhiyuan Liu, and Gao Huang. 2025 · 2025
Closest in time.
Progressive lora for multimodal continual instruction tuning
Yahan Yu, Duzhen Zhang, Yong Ren, Xuanle Zhao, Xiuyi Chen, and Chenhui Chu. 2025 · 2025
Closest in time.
Enhancing multimodal continual instruction tuning with branchlora
Duzhen Zhang, Yong Ren, Zhong-Zhi Li, Yahan Yu, Jiahua Dong, Chenxing Li, Zhilong Ji, and Jinfeng Bai. 2025 · 2025
Closest in time.
Xuanle Zhao, Xuexin Liu, Haoyue Yang, Xianzhen Luo, Fanhu Zeng, Jianling Li, Qi Shi, and Chi Chen. 2025 · 2025
Closest in time.