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Multimodal in-context learning (ICL) has emerged as a key mechanism for harnessing the capabilities of large vision-language models (LVLMs).
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2012
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
Earlier work this paper cites.
From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. 2014 · 2014
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Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
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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
Earlier work this paper cites.
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
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
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Ok-vqa: A visual question answering benchmark requiring external knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. 2019 · 2019
Earlier work this paper cites.
The hateful memes challenge: Detecting hate speech in multimodal memes
Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, and Davide Testuggine. 2020 · 2020
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Interpreting gpt: the logit lens
nostalgebraist. 2020 · 2020
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What makes good in-context examples for gpt-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021 · 2021
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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.
Rethinking the role of demonstrations: what makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Earlier work this paper cites.
In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al. 2022 · 2022
Earlier work this paper cites.
A-okvqa: A benchmark for visual question answering using world knowledge
Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi. 2022 · 2022
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Openflamingo: An open-source framework for training large autoregressive vision-language models
Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, and Ludwig Schmidt. 2023 · 2023
Earlier work this paper cites.
What can transformers learn in-context? a case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy Liang, and Gregory Valiant. 2023 · 2023
Cited alongside, same era.
In-context demonstration selection with cross entropy difference
Dan Iter, Reid Pryzant, Ruochen Xu, Shuohang Wang, Yang Liu, Yichong Xu, and Chenguang Zhu. 2023 · 2023
Cited alongside, same era.
Z-icl: zero-shot in-context learning with pseudo-demonstrations
Xinxi Lyu, Sewon Min, Iz Beltagy, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023 · 2023
Cited alongside, same era.
Shiwen Ni, Dingwei Chen, Chengming Li, Xiping Hu, Ruifeng Xu, and Min Yang. 2023 · 2023
Cited alongside, same era.
Order-independence without fine tuning
Reid McIlroy-Young, Katrina Brown, Conlan Olson, Linjun Zhang, and Cynthia Dwork. 2024 · 2024
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Emu: generative pretraining in multimodality
Quan Sun, Qiying Yu, Yufeng Cui, Fan Zhang, Xiaosong Zhang, Yueze Wang, Hongcheng Gao, Jingjing Liu, Tiejun Huang, and Xinlong Wang. 2024 · 2024
Later among the works it cites.
In-context learning demonstration selection via influence analysis
Minh-Hao Van, Xintao Wu, et al. 2024 · 2024
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Learning to retrieve in-context examples for large language models
Liang Wang, Nan Yang, and Furu Wei. 2024 · 2024
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Lever lm: configuring in-context sequence to lever large vision language models
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OpenAI. 2023 · 2023
Cited alongside, same era.
What in-context learning “learns” in-context: Disentangling task recognition and task learning
Jane Pan, Tianyu Gao, Howard Chen, and Danqi Chen. 2023 · 2023
Cited alongside, same era.
Multimodal neurons in pretrained text-only transformers
Sarah Schwettmann, Neil Chowdhury, Samuel Klein, David Bau, and Antonio Torralba. 2023 · 2023
Cited alongside, same era.
Larger language models do in-context learning differently
Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, et al. 2023 · 2023
Cited alongside, same era.
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 Le, and Ed Chi. 2023 · 2023
Cited alongside, same era.
Mc-llava: Multi-concept personalized vision-language model
Ruichuan An, Sihan Yang, Ming Lu, Renrui Zhang, Kai Zeng, Yulin Luo, Jiajun Cao, Hao Liang, Ying Chen, Qi She, et al. 2024 · 2024
Cited alongside, same era.
Learning to retrieve iteratively for in-context learning
Yunmo Chen, Tongfei Chen, Harsh Jhamtani, Patrick Xia, Richard Shin, Jason Eisner, and Benjamin Van Durme. 2024b · 2024
Cited alongside, same era.
A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Tianyu Liu, Baobao Chang, Xu Sun, Lei Li, and Zhifang Sui. 2024 · 2024
Cited alongside, same era.
Xu Yang, Yingzhe Peng, Haoxuan Ma, Shuo Xu, Chi Zhang, Yucheng Han, and Hanwang Zhang. 2024 · 2024
Later among the works it cites.
Do llms overcome shortcut learning? an evaluation of shortcut challenges in large language models
Yu Yuan, Lili Zhao, Kai Zhang, Guangting Zheng, and Qi Liu. 2024 · 2024
Later among the works it cites.
Unveiling in-context learning: a coordinate system to understand its working mechanism
Anhao Zhao, Fanghua Ye, Jinlan Fu, and Xiaoyu Shen. 2024 · 2024
Later among the works it cites.
Visual in-context learning for large vision-language models
Yucheng Zhou, Xiang Li, Qianning Wang, and Jianbing Shen. 2024 · 2024
Later among the works it cites.
Vl-icl bench: the devil in the details of multimodal in-context learning
Yongshuo Zong, Ondrej Bohdal, and Timothy Hospedales. 2024 · 2024
Later among the works it cites.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. 2025 · 2025
Closest in time.
Optiseq: Ordering examples on-the-fly for in-context learning
Rahul Atul Bhope, Praveen Venkateswaran, KR Jayaram, Vatche Isahagian, Vinod Muthusamy, and Nalini Venkatasubramanian. 2025 · 2025
Closest in time.
Rethinking visual layer selection in multimodal llms
Haoran Chen, Junyan Lin, Xinhao Chen, Yue Fan, Xin Jin, Hui Su, Jianfeng Dong, Jinlan Fu, and Xiaoyu Shen. 2025 · 2025
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Breaking the modality barrier: Universal embedding learning with multimodal llms
Tiancheng Gu, Kaicheng Yang, Ziyong Feng, Xingjun Wang, Yanzhao Zhang, Dingkun Long, Yingda Chen, Weidong Cai, and Jiankang Deng. 2025 · 2025
Closest in time.
Modrwkv: Transformer multimodality in linear time
Jiale Kang, Ziyin Yue, Qingyu Yin, Jiang Rui, Weile Li, Zening Lu, and Zhouran Ji. 2025 · 2025
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Yanshu Li. 2025 · 2025
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Fairreason: Balancing reasoning and social bias in mllms
Zhenyu Pan, Yutong Zhang, Jianshu Zhang, Haoran Lu, Haozheng Luo, Yuwei Han, Philip S Yu, Manling Li, and Han Liu. 2025 · 2025
Closest in time.