Fetching the paper…
Reading the bibliography…
In recent years, multimodal large language models (MLLMs) have achieved significant breakthroughs, enhancing understanding across text and vision.
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.
Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. 2022 · 2022
Earlier work this paper cites.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 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.
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.
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 Fung, and Steven Hoi. 2023 · 2023
Earlier work this paper cites.
What makes for good visual tokenizers for large language models?
Guangzhi Wang, Yixiao Ge, Xiaohan Ding, Mohan Kankanhalli, and Ying Shan. 2023 · 2023
Earlier work this paper cites.
A survey on multimodal large language models
Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing Sun, Tong Xu, and Enhong Chen. 2023 · 2023
Cited alongside, same era.
Mm-vet: Evaluating large multimodal models for integrated capabilities
Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. 2023 · 2023
Cited alongside, same era.
Mquake: Assessing knowledge editing in language models via multi-hop questions
Zexuan Zhong, Zhengxuan Wu, Christopher D Manning, Christopher Potts, and Danqi Chen. 2023 · 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 · 2023
Cited alongside, same era.
The two-hop curse: Llms trained on a-> b, b-> c fail to learn a–> c
Cracking the code of juxtaposition: Can ai models understand the humorous contradictions
Zhe Hu, Tuo Liang, Jing Li, Yiren Lu, Yunlai Zhou, Yiran Qiao, Jing Ma, and Yu Yin. 2024 · 2024
Later among the works it cites.
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
Later among the works it cites.
Mmrel: A relation understanding dataset and benchmark in the mllm era
Jiahao Nie, Gongjie Zhang, Wenbin An, Yap-Peng Tan, Alex C Kot, and Shijian Lu. 2024 · 2024
Later among the works it cites.
V?: Guided visual search as a core mechanism in multimodal llms
Penghao Wu and Saining Xie. 2024 · 2024
Later among the works it cites.
Exploring chain-of-thought for multi-modal metaphor detection
Yanzhi Xu, Yueying Hua, Shichen Li, and Zhongqing Wang. 2024 · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mikita Balesni, Tomek Korbak, and Owain Evans. 2024 · 2024
Cited alongside, same era.
Qiguang Chen, Libo Qin, Jin Zhang, Zhi Chen, Xiao Xu, and Wanxiang Che. 2024 · 2024
Cited alongside, same era.
Blink: Multimodal large language models can see but not perceive
Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin, Dan Roth, Noah A Smith, Wei-Chiu Ma, and Ranjay Krishna. 2024 · 2024
Cited alongside, same era.
Seed-bench: Benchmarking multimodal large language models
Bohao Li, Yuying Ge, Yixiao Ge, Guangzhi Wang, Rui Wang, Ruimao Zhang, and Ying Shan. 2024a
Cited in the paper.
Fine-tuning multimodal llms to follow zero-shot demonstrative instructions
Juncheng Li, Kaihang Pan, Zhiqi Ge, Minghe Gao, Wei Ji, Wenqiao Zhang, Tat-Seng Chua, Siliang Tang, Hanwang Zhang, and Yueting Zhuang. 2023a
Cited in the paper.
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023b
Cited in the paper.
Multimodal hypothetical summary for retrieval-based multi-image question answering
Peize Li, Qingyi Si, Peng Fu, Zheng Lin, and Yan Wang. 2024b
Cited in the paper.
Sok-bench: A situated video reasoning benchmark with aligned open-world knowledge
Andong Wang, Bo Wu, Sunli Chen, Zhenfang Chen, Haotian Guan, Wei-Ning Lee, Li Erran Li, and Chuang Gan. 2024a
Cited in the paper.
mplug-owl3: Towards long image-sequence understanding in multi-modal large language models
Jiabo Ye, Haiyang Xu, Haowei Liu, Anwen Hu, Ming Yan, Qi Qian, Ji Zhang, Fei Huang, and Jingren Zhou. 2024 · 2024
Later among the works it cites.
Bingchen Zhao, Yongshuo Zong, Letian Zhang, and Timothy Hospedales. 2024 · 2024
Later among the works it cites.