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Visual instruction tuning (VIT) datasets have grown rapidly in scale, yet the informativeness of individual training samples has largely been overlooked.
Towards vqa models that can read
Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach · 2019
Earlier work this paper cites.
Deep learning on a data diet: Finding important examples early in training
Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite · 2021
Earlier work this paper cites.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 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
Earlier work this paper cites.
Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
Earlier work this paper cites.
Infographicvqa
Minesh Mathew, Viraj Bagal, Rubèn Tito, Dimosthenis Karatzas, Ernest Valveny, and CV Jawahar · 2022
Earlier work this paper cites.
Beyond neural scaling laws: beating power law scaling via data pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari Morcos · 2022
Earlier work this paper cites.
A benchmark for compositional visual reasoning
Aimen Zerroug, Mohit Vaishnav, Julien Colin, Sebastian Musslick, and Thomas Serre · 2022
Earlier work this paper cites.
Semdedup: Data-efficient learning at web-scale through semantic deduplication
Amro Abbas, Kushal Tirumala, Dániel Simig, Surya Ganguli, and Ari S Morcos · 2023
Earlier work this paper cites.
Qwen-vl: A versatile vision-language model for understanding, localization
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou · 2023
Earlier work this paper cites.
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Zhenyu Qiu, Wei Lin, Jinrui Yang, Xiawu Zheng, et al · 2023
Earlier work this paper cites.
Sugarcrepe: Fixing hackable benchmarks for vision-language compositionality
Cheng-Yu Hsieh, Jieyu Zhang, Zixian Ma, Aniruddha Kembhavi, and Ranjay Krishna · 2023
Cited alongside, same era.
Visual instruction tuning towards general-purpose multimodal model: A survey
Jiaxing Huang, Jingyi Zhang, Kai Jiang, Han Qiu, and Shijian Lu · 2023
Cited alongside, same era.
Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
Cited alongside, same era.
D2 pruning: Message passing for balancing diversity and difficulty in data pruning
Adyasha Maharana, Prateek Yadav, and Mohit Bansal · 2023
Cited alongside, same era.
When less is more: Investigating data pruning for pretraining llms at scale
Max Marion, Ahmet Üstün, Luiza Pozzobon, Alex Wang, Marzieh Fadaee, and Sara Hooker · 2023
Mmcomposition: Revisiting the compositionality of pre-trained vision-language models
Hang Hua, Yunlong Tang, Ziyun Zeng, Liangliang Cao, Zhengyuan Yang, Hangfeng He, Chenliang Xu, and Jiebo Luo · 2024
Later among the works it cites.
Concept-skill transferability-based data selection for large vision-language models
Jaewoo Lee, Boyang Li, and Sung Ju Hwang · 2024
Later among the works it cites.
Prompting large vision-language models for compositional reasoning
Timothy Ossowski, Ming Jiang, and Junjie Hu · 2024
Later among the works it cites.
Eagle: Exploring the design space for multimodal llms with mixture of encoders
Min Shi, Fuxiao Liu, Shihao Wang, Shijia Liao, Subhashree Radhakrishnan, De-An Huang, Hongxu Yin, Karan Sapra, Yaser Yacoob, Humphrey Shi, et al · 2024
Later among the works it cites.
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Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al · 2023
Cited alongside, same era.
Lingling Xu, Haoran Xie, Si-Zhao Joe Qin, Xiaohui Tao, and Fu Lee Wang · 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
Cited alongside, same era.
Instruction-following evaluation for large language models
Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou · 2023
Cited alongside, same era.
A closer look at the limitations of instruction tuning
Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar, Deepali Aneja, Zeyu Jin, Ramani Duraiswami, Dinesh Manocha, et al · 2024
Cited alongside, same era.
Does prompt formatting have any impact on llm performance?
Jia He, Mukund Rungta, David Koleczek, Arshdeep Sekhon, Franklin X Wang, and Sadid Hasan · 2024
Cited alongside, same era.
Decomposing complex visual comprehension into atomic visual skills for vision language models
Hyunsik Chae, Seungwoo Yoon, Chloe Yewon Chun, Gyehun Go, Yongin Cho, Gyeongmin Lee, and Ernest K Ryu
Cited in the paper.
Nishant Balepur, Rachel Rudinger, and Jordan Lee Boyd-Graber · 2025
Closest in time.
Understanding the limits of vision language models through the lens of the binding problem
Declan Campbell, Sunayana Rane, Tyler Giallanza, Camillo Nicolò De Sabbata, Kia Ghods, Amogh Joshi, Alexander Ku, Steven Frankland, Tom Griffiths, Jonathan D Cohen, et al · 2025
Closest in time.
Explain before you answer: A survey on compositional visual reasoning
Fucai Ke, Joy Hsu, Zhixi Cai, Zixian Ma, Xin Zheng, Xindi Wu, Sukai Huang, Weiqing Wang, Pari Delir Haghighi, Gholamreza Haffari, et al · 2025
Closest in time.
Eagle 2: Building post-training data strategies from scratch for frontier vision-language models
Zhiqi Li, Guo Chen, Shilong Liu, Shihao Wang, Vibashan VS, Yishen Ji, Shiyi Lan, Hao Zhang, Yilin Zhao, Subhashree Radhakrishnan, et al · 2025
Closest in time.
Eva Sánchez Salido, Julio Gonzalo, and Guillermo Marco · 2025
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Cambrian-1: A fully open, vision-centric exploration of multimodal llms
Peter Tong, Ellis Brown, Penghao Wu, Sanghyun Woo, Adithya Jairam Vedagiri IYER, Sai Charitha Akula, Shusheng Yang, Jihan Yang, Manoj Middepogu, Ziteng Wang, et al · 2025
Closest in time.