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Large language models (LLMs) famously exhibit emergent in-context learning (ICL) -- the ability to rapidly adapt to new tasks using few-shot examples provided as a prompt, without updating the model's weights.
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Microsoft coco: Common objects in context
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Character-level convolutional networks for text classification
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
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Vizwiz grand challenge: Answering visual questions from blind people
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Learning to compare: Relation network for few-shot learning
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Measuring massive multitask language understanding
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Meta-Learning in Neural Networks: A Survey
Timothy M Hospedales, Antreas Antoniou, Paul Micaelli, and Amos J. Storkey · 2021
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TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text
Amanpreet Singh, Guan Pang, Mandy Toh, Jing Huang, Wojciech Galuba, and Tal Hassner · 2021
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Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill · 2021
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T-NER: An all-round python library for transformer-based named entity recognition
Asahi Ushio and Jose Camacho-Collados · 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
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Visual prompting via image inpainting
Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, and Alexei Efros · 2022
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Learn to explain: Multimodal reasoning via thought chains for science question answering
Pan Lu, Swaroop Mishra, Tony Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan · 2022
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MetaICL: Learning to learn in context
Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi · 2022
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Train short, test long: Attention with linear biases enables input length extrapolation
Phi-3 technical report: A highly capable language model locally on your phone
Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et al · 2024
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What makes multimodal in-context learning work?
Folco Bertini Baldassini, Mustafa Shukor, Matthieu Cord, Laure Soulier, and Benjamin Piwowarski · 2024
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Understanding and improving in-context learning on vision-language models
Shuo Chen, Zhen Han, Bailan He, Mark Buckley, Philip Torr, Volker Tresp, and Jindong Gu · 2024
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A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui · 2024
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Making llama see and draw with seed tokenizer
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Ofir Press, Noah Smith, and Mike Lewis · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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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, et al · 2023
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Qwen-vl: A frontier large vision-language model with versatile abilities
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Mme: A comprehensive evaluation benchmark for multimodal large language models
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, et al · 2023
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Generating images with multimodal language models
Jing Yu Koh, Daniel Fried, and Russ R Salakhutdinov · 2023
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Obelics: An open web-scale filtered dataset of interleaved image-text documents
Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander Rush, Douwe Kiela, et al · 2023
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Llm maybe longlm: Self-extend llm context window without tuning
Hongye Jin, Xiaotian Han, Jingfeng Yang, Zhimeng Jiang, Zirui Liu, Chia-Yuan Chang, Huiyuan Chen, and Xia Hu · 2024
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Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts
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Can mllms perform text-to-image in-context learning?
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Mmicl: Empowering vision-language model with multi-modal in-context learning
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AGIEval: A human-centric benchmark for evaluating foundation models
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Self-supervised multimodal learning: A survey
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