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Large Multimodal Models (LMMs) have achieved strong performance across a range of vision and language tasks.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning. 2019 · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Earlier work this paper cites.
A cross-linguistic study of spatial location descriptions in new zealand english and brazilian portuguese natural language
Cristiane Kutianski Marchi Fagundes, Kristin Stock, and Luciene Stamato Delazari. 2021 · 2021
Earlier work this paper cites.
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, Olivier Bousquet, Quoc Le, and Ed Chi. 2022 · 2022
Earlier work this paper cites.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Earlier work this paper cites.
Minigpt-v2: large language model as a unified interface for vision-language multi-task learning
Jun Chen, Deyao Zhu, Xiaoqian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, and Mohamed Elhoseiny. 2023 · 2023
Earlier work this paper cites.
What’s" up" with vision-language models? investigating their struggle with spatial reasoning
Amita Kamath, Jack Hessel, and Kai-Wei Chang. 2023 · 2023
Earlier work this paper cites.
BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models
Junnan Li, Dongxu Li, Silvio Savarese, and Steven C. H. Hoi. 2023c · 2023
Cited alongside, same era.
Video-llava: Learning united visual representation by alignment before projection
Bin Lin, Bin Zhu, Yang Ye, Munan Ning, Peng Jin, and Li Yuan. 2023 · 2023
Cited alongside, same era.
Kosmos-2.5: A multimodal literate model
Tengchao Lv, Yupan Huang, Jingye Chen, Lei Cui, Shuming Ma, Yaoyao Chang, Shaohan Huang, Wenhui Wang, Li Dong, Weiyao Luo, Shaoxiang Wu, Guoxin Wang, Cha Zhang, and Furu Wei. 2023 · 2023
Cited alongside, same era.
Video-chatgpt: Towards detailed video understanding via large vision and language models
Muhammad Maaz, Hanoona Abdul Rasheed, Salman H. Khan, and Fahad Shahbaz Khan. 2023 · 2023
Cited alongside, same era.
Spatialvlm: Endowing vision-language models with spatial reasoning capabilities
Boyuan Chen, Zhuo Xu, Sean Kirmani, Brian Ichter, Danny Driess, Pete Florence, Dorsa Sadigh, Leonidas Guibas, and Fei Xia. 2024 · 2024
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Gemini: A family of highly capable multimodal models
Gemini Team. 2024 · 2024
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II-MMR: identifying and improving multi-modal multi-hop reasoning in visual question answering
Jihyung Kil, Farideh Tavazoee, Dongyeop Kang, and Joo-Kyung Kim. 2024 · 2024
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Automatic programming: Large language models and beyond
Michael R Lyu, Baishakhi Ray, Abhik Roychoudhury, Shin Hwei Tan, and Patanamon Thongtanunam. 2024 · 2024
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Direct evaluation of chain-of-thought in multi-hop reasoning with knowledge graphs
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OpenAI. 2023 · 2023
Cited alongside, same era.
Kosmos-2: Grounding multimodal large language models to the world
Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, and Furu Wei. 2023 · 2023
Cited alongside, same era.
Rephrase, augment, reason: Visual grounding of questions for vision-language models
Archiki Prasad, Elias Stengel-Eskin, and Mohit Bansal. 2023 · 2023
Cited alongside, same era.
Is chatgpt a good nlg evaluator? a preliminary study
Jiaan Wang, Yunlong Liang, Fandong Meng, Zengkui Sun, Haoxiang Shi, Zhixu Li, Jinan Xu, Jianfeng Qu, and Jie Zhou. 2023 · 2023
Cited alongside, same era.
Scaffolding coordinates to promote vision-language coordination in large multi-modal models
Xuanyu Lei, Zonghan Yang, Xinrui Chen, Peng Li, and Yang Liu. 2024a
Cited in the paper.
Scaffolding coordinates to promote vision-language coordination in large multi-modal models
Xuanyu Lei, Zonghan Yang, Xinrui Chen, Peng Li, and Yang Liu. 2024b
Cited in the paper.
Seed-bench: Benchmarking multimodal llms with generative comprehension
Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, and Ying Shan. 2023a
Cited in the paper.
Seed-bench: Benchmarking multimodal llms with generative comprehension
Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, and Ying Shan. 2023b
Cited in the paper.
Minh-Vuong Nguyen, Linhao Luo, Fatemeh Shiri, Dinh Phung, Yuan-Fang Li, Thuy-Trang Vu, and Gholamreza Haffari. 2024 · 2024
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Hello gpt-4o
OpenAI. 2024 · 2024
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Eyes wide shut? exploring the visual shortcomings of multimodal llms
Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. 2024 · 2024
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Don’t trust: Verify–grounding llm quantitative reasoning with autoformalization
Jin Peng Zhou, Charles Staats, Wenda Li, Christian Szegedy, Kilian Q Weinberger, and Yuhuai Wu. 2024 · 2024
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