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Large language models (LLMs) have demonstrated impressive reasoning capabilities, particularly in textual mathematical problem-solving.
Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
Earlier work this paper cites.
Solving geometry problems: Combining text and diagram interpretation
Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi, Oren Etzioni, and Clint Malcolm. 2015 · 2015
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A diagram is worth a dozen images
Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi. 2016 · 2016
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017 · 2017
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Figureqa: An annotated figure dataset for visual reasoning
Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Ákos Kádár, Adam Trischler, and Yoshua Bengio. 2017 · 2017
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Are you smarter than a sixth grader? textbook question answering for multimodal machine comprehension
Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
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Vizwiz grand challenge: Answering visual questions from blind people
Danna Gurari, Qing Li, Abigale J Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P Bigham. 2018 · 2018
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Dvqa: Understanding data visualizations via question answering
Kushal Kafle, Brian Price, Scott Cohen, and Christopher Kanan. 2018 · 2018
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A dataset of clinically generated visual questions and answers about radiology images
Jason J Lau, Soumya Gayen, Asma Ben Abacha, and Dina Demner-Fushman. 2018 · 2018
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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
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Plotqa: Reasoning over scientific plots
Nitesh Methani, Pritha Ganguly, Mitesh M Khapra, and Pratyush Kumar. 2020 · 2020
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Graph-to-tree learning for solving math word problems
Jipeng Zhang, Lei Wang, Roy Ka-Wei Lee, Yi Bin, Yan Wang, Jie Shao, and Ee-Peng Lim. 2020 · 2020
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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, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2021
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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
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An augmented benchmark dataset for geometric question answering through dual parallel text encoding
Jie Cao and Jing Xiao. 2022 · 2022
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Mapqa: A dataset for question answering on choropleth maps
Shuaichen Chang, David Palzer, Jialin Li, Eric Fosler-Lussier, and Ningchuan Xiao. 2022 · 2022
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UniGeo: Unifying geometry logical reasoning via reformulating mathematical expression
Jiaqi Chen, Tong Li, Jinghui Qin, Pan Lu, Liang Lin, Chongyu Chen, and Xiaodan Liang. 2022 · 2022
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022 · 2022
Cited alongside, same era.
Clevr-math: A dataset for compositional language, visual and mathematical reasoning
Adam Dahlgren Lindström and Savitha Sam Abraham. 2022 · 2022
Cited alongside, same era.
ChartQA: A benchmark for question answering about charts with visual and logical reasoning
Ahmed Masry, Xuan Long Do, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. 2022 · 2022
Cited alongside, same era.
Infographicvqa
Minesh Mathew, Viraj Bagal, Rubèn Tito, Dimosthenis Karatzas, Ernest Valveny, and CV Jawahar. 2022 · 2022
Cited alongside, same era.
A-okvqa: A benchmark for visual question answering using world knowledge
Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi. 2022 · 2022
Cited alongside, same era.
Logical implications for visual question answering consistency
Sergio Tascon-Morales, Pablo Márquez-Neila, and Raphael Sznitman. 2023 · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023 · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
The Vicuna Team. 2023 · 2023
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023 · 2023
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mplug-owl: Modularization empowers large language models with multimodality
Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, et al. 2023 · 2023
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Qwen-vl: A frontier large vision-language model with versatile abilities
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023 · 2023
Cited alongside, same era.
Non-autoregressive math word problem solver with unified tree structure
Yi Bin, Mengqun Han, Wenhao Shi, Lei Wang, Yang Yang, See-Kiong Ng, and Heng Tao Shen. 2023 · 2023
Cited alongside, same era.
Chain-of-thought prompt distillation for multimodal named entity and multimodal relation extraction
Feng Chen and Yujian Feng. 2023 · 2023
Cited alongside, same era.
Tora: A tool-integrated reasoning agent for mathematical problem solving
Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Minlie Huang, Nan Duan, and Weizhu Chen. 2023 · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Super-clevr: A virtual benchmark to diagnose domain robustness in visual reasoning
Zhuowan Li, Xingrui Wang, Elias Stengel-Eskin, Adam Kortylewski, Wufei Ma, Benjamin Van Durme, and Alan L Yuille. 2023 · 2023
Cited alongside, same era.
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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 A. Ayyubi, Kai-Wei Chang, and Shih-Fu Chang. 2023 · 2023
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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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Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, and Ed H. Chi. 2023 · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023 · 2023
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The claude 3 model family: Opus, sonnet, haiku
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Gallerygpt: Analyzing paintings with large multimodal models
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Instructblip: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi. 2024 · 2024
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Minicpm: Unveiling the potential of small language models with scalable training strategies
Shengding Hu, Yuge Tu, Xu Han, Chaoqun He, Ganqu Cui, Xiang Long, Zhi Zheng, Yewei Fang, Yuxiang Huang, Weilin Zhao, et al. 2024 · 2024
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Mm-forecast: A multimodal approach to temporal event forecasting with large language models
Haoxuan Li, Zhengmao Yang, Yunshan Ma, Yi Bin, Yang Yang, and Tat-Seng Chua. 2024a · 2024
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Improved baselines with visual instruction tuning
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. 2024 · 2024
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Large multi-modal models for strong performance and efficient deployment
OpenBMB. 2024 · 2024
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Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems?
Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Peng Gao, et al. 2024 · 2024
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