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Open-source multimodal large language models (MLLMs) excel in various tasks involving textual and visual inputs but still struggle with complex multimodal mathematical reasoning, lagging behind proprietary models like GPT-4V(ision) and Gemini-Pro.
Dual attention networks for multimodal reasoning and matching
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Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
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Chartqa: A benchmark for question answering about charts with visual and logical reasoning
Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq R. Joty, and Enamul Hoque. 2022 · 2022
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Chain-of-thought prompt distillation for multimodal named entity and multimodal relation extraction
Feng Chen and Yujian Feng. 2023 · 2023
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Llama-adapter v2: Parameter-efficient visual instruction model
Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei Zhang, Pan Lu, Conghui He, Xiangyu Yue, et al. 2023 · 2023
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Tianrui Guan, Fuxiao Liu, Xiyang Wu, Ruiqi Xian, Zongxia Li, Xiaoyu Liu, Xijun Wang, Lichang Chen, Furong Huang, Yaser Yacoob, et al. 2023 · 2023
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UNK-VQA: A dataset and A probe into multi-modal large models’ abstention ability
Yanyang Guo, Fangkai Jiao, Zhiqi Shen, Liqiang Nie, and Mohan S. Kankanhalli. 2023 · 2023
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Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes
Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023 · 2023
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Visual program distillation: Distilling tools and programmatic reasoning into vision-language models
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Faithscore: Evaluating hallucinations in large vision-language models
Liqiang Jing, Ruosen Li, Yunmo Chen, Mengzhao Jia, and Xinya Du. 2023 · 2023
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Chatgpt for good? on opportunities and challenges of large language models for education
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Scigraphqa: A large-scale synthetic multi-turn question-answering dataset for scientific graphs
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Beneath the surface: Unveiling harmful memes with multimodal reasoning distilled from large language models
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Visual instruction tuning
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IdealGPT: Iteratively decomposing vision and language reasoning via large language models
Haoxuan You, Rui Sun, Zhecan Wang, Long Chen, Gengyu Wang, Hammad Ayyubi, Kai-Wei Chang, and Shih-Fu Chang. 2023 · 2023
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Mammoth: Building math generalist models through hybrid instruction tuning
Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen. 2023 · 2023
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Auto-instruct: Automatic instruction generation and ranking for black-box language models
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Ddcot: Duty-distinct chain-of-thought prompting for multimodal reasoning in language models
Ge Zheng, Bin Yang, Jiajin Tang, Hong-Yu Zhou, and Sibei Yang. 2023 · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
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Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning
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Compositional chain-of-thought prompting for large multimodal models
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Gpt-4v(ision) system card (2023)
OpenAI · 2023
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Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. 2023 · 2023
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Distilling reasoning capabilities into smaller language models
Kumar Shridhar, Alessandro Stolfo, and Mrinmaya Sachan. 2023 · 2023
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Vipergpt: Visual inference via python execution for reasoning
Dídac Surís, Sachit Menon, and Carl Vondrick. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
Cited alongside, same era.
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023 · 2023
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Plug-and-play grounding of reasoning in multimodal large language models
Jiaxing Chen, Yuxuan Liu, Dehu Li, Xiang An, Ziyong Feng, Yongle Zhao, and Yin Xie. 2024 · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
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What you see is what you read? improving text-image alignment evaluation
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Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems?
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Tinyllava: A framework of small-scale large multimodal models
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