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The goal of meta-learning is to learn to adapt to a new task with only a few labeled examples.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich. 2019 · 1903
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UNIFIEDQA: Crossing format boundaries with a single QA system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020 · 1907
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Testing robustness against unforeseen adversaries
Daniel Kang, Yi Sun, Dan Hendrycks, Tom Brown, and Jacob Steinhardt. 2019 · 1908
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Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang. 2019 · 1911
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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Building machines that learn and think like people
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman. 2016 · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Christopher Maddison, Arthur Guez, Laurent Sifre, George Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis. 2016 · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
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Meta-learning for low-resource neural machine translation
Jiatao Gu, Yong Wang, Yun Chen, Victor O. K. Li, and Kyunghyun Cho. 2018 · 2018
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden Lake and Marco Baroni. 2018 · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman. 2018 · 2018
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Diverse few-shot text classification with multiple metrics
Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, and Bowen Zhou. 2018 · 2018
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Massively multilingual neural machine translation
Roee Aharoni, Melvin Johnson, and Orhan Firat. 2019 · 2019
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How to train your MAML
Antreas Antoniou, Harrison Edwards, and Amos Storkey. 2019 · 2019
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Transformer-XL: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, and Ruslan Salakhutdinov. 2019 · 2019
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Investigating meta-learning algorithms for low-resource natural language understanding tasks
Zi-Yi Dou, Keyi Yu, and Antonios Anastasopoulos. 2019 · 2019
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Induction networks for few-shot text classification
Ruiying Geng, Binhua Li, Yongbin Li, Xiaodan Zhu, Ping Jian, and Jian Sun. 2019 · 2019
Cited alongside, same era.
Attentive task-agnostic meta-learning for few-shot text classification
Xiang Jiang, Mohammad Havaei, Gabriel Chartrand, Hassan Chouaib, Thomas Vincent, Andrew Jesson, Nicolas Chapados, and Stan Matwin. 2019 · 2019
Cited alongside, same era.
Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019 · 2019
Cited alongside, same era.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Meta-learning with implicit gradients
Muppet: Massive multi-task representations with pre-finetuning
Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, Xilun Chen, Luke Zettlemoyer, and Sonal Gupta. 2021 · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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KaggleDBQA: Realistic evaluation of text-to-SQL parsers
Chia-Hsuan Lee, Oleksandr Polozov, and Matthew Richardson. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine. 2019 · 2019
Cited alongside, same era.
Towards a human-like open-domain chatbot
Daniel Adiwardana, Minh-Thang Luong, David R. So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, and Quoc V. Le. 2020 · 2020
Cited alongside, same era.
Learning to few-shot learn across diverse natural language classification tasks
Trapit Bansal, Rishikesh Jha, and Andrew McCallum. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Meta-learning with dynamic-memory-based prototypical network for few-shot event detection
Shumin Deng, Ningyu Zhang, Jiaojian Kang, Yichi Zhang, Wei Zhang, and Huajun Chen. 2020 · 2020
Cited alongside, same era.
Learning to learn to disambiguate: Meta-learning for few-shot word sense disambiguation
Nithin Holla, Pushkar Mishra, Helen Yannakoudakis, and Ekaterina Shutova. 2020 · 2020
Cited alongside, same era.
How Can We Know What Language Models Know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Cited alongside, same era.
What makes good in-context examples for gpt- 3 3 ?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021 · 2021
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Metaicl: Learning to learn in context
Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2021 · 2021
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Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. 2021 · 2021
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True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
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Learning how to ask: Querying LMs with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021b · 2021
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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 · 2021
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Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, and Ludwig Schmidt. 2021 · 2021
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Incremental few-shot text classification with multi-round new classes: Formulation, dataset and system
Congying Xia, Wenpeng Yin, Yihao Feng, and Philip Yu. 2021 · 2021
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Crossfit: A few-shot learning challenge for cross-task generalization in nlp
Qinyuan Ye, Bill Yuchen Lin, and Xiang Ren. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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Adapting language models for zero-shot learning by meta-tuning on dataset and prompt collections
Ruiqi Zhong, Kristy Lee, Zheng Zhang, and Dan Klein. 2021 · 2021
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