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Recent studies customizing Multimodal Large Language Models (MLLMs) for domain-specific tasks have yielded promising results, especially in the field of scientific chart comprehension.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Earlier work this paper cites.
Plotqa: Reasoning over scientific plots
Nitesh Methani, Pritha Ganguly, Mitesh M Khapra, and Pratyush Kumar · 2020
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 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
Earlier work this paper cites.
Chartqa: A benchmark for question answering about charts with visual and logical reasoning
Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque · 2022
Earlier work this paper cites.
Rt-2: Vision-language-action models transfer web knowledge to robotic control
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al · 2023
Earlier work this paper cites.
Videollm: Modeling video sequence with large language models
Guo Chen, Yin-Dong Zheng, Jiahao Wang, Jilan Xu, Yifei Huang, Junting Pan, Yi Wang, Yali Wang, Yu Qiao, Tong Lu, et al · 2023
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
Earlier work this paper cites.
Pix2struct: Screenshot parsing as pretraining for visual language understanding
Kenton Lee, Mandar Joshi, Iulia Raluca Turc, Hexiang Hu, Fangyu Liu, Julian Martin Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, and Kristina Toutanova · 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
Cited alongside, same era.
Gorilla: Large language model connected with massive apis
Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez · 2023
Cited alongside, same era.
Chartbench: A benchmark for complex visual reasoning in charts
Zhengzhuo Xu, Sinan Du, Yiyan Qi, Chengjin Xu, Chun Yuan, and Jian Guo · 2023
Cited alongside, same era.
Large language models for robotics: A survey
Fanlong Zeng, Wensheng Gan, Yongheng Wang, Ning Liu, and Philip S Yu · 2023
Cited alongside, same era.
Chartinstruct: Instruction tuning for chart comprehension and reasoning
Ahmed Masry, Mehrad Shahmohammadi, Md Rizwan Parvez, Enamul Hoque, and Shafiq Joty · 2024
Closest in time.
Mm1: Methods, analysis & insights from multimodal llm pre-training
Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, et al · 2024
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Functionary, 2024
MeetkAI · 2024
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Chartassisstant: A universal chart multimodal language model via chart-to-table pre-training and multitask instruction tuning
Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, and Ping Luo · 2024
Closest in time.
Goex: Perspectives and designs towards a runtime for autonomous llm applications
Shishir G Patil, Tianjun Zhang, Vivian Fang, Roy Huang, Aaron Hao, Martin Casado, Joseph E Gonzalez, Raluca Ada Popa, Ion Stoica, et al · 2024
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Mingyang Zhou, Yi R Fung, Long Chen, Christopher Thomas, Heng Ji, and Shih-Fu Chang · 2023
Cited alongside, same era.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
Cited alongside, same era.
Mmc: Advancing multimodal chart understanding with large-scale instruction tuning
Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, and Dong Yu · 2024
Cited alongside, same era.
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, Yaofeng Sun, et al · 2024
Cited alongside, same era.
Deplot: One-shot visual language reasoning by plot-to-table translation
Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, and Yasemin Altun
Cited in the paper.
Matcha: Enhancing visual language pretraining with math reasoning and chart derendering
Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, and Julian Martin Eisenschlos
Cited in the paper.
Improved baselines with visual instruction tuning
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee
Cited in the paper.
Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee
Cited in the paper.
Closest in time.
Chameleon: Mixed-modal early-fusion foundation models
Chameleon Team · 2024
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Charxiv: Charting gaps in realistic chart understanding in multimodal llms
Zirui Wang, Mengzhou Xia, Luxi He, Howard Chen, Yitao Liu, Richard Zhu, Kaiqu Liang, Xindi Wu, Haotian Liu, Sadhika Malladi, et al · 2024
Closest in time.
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
Closest in time.