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Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training examples as part of the input.
The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini · 2005
Earlier work this paper cites.
Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S. Gordon · 2011
Earlier work this paper cites.
The numpy array: a structure for efficient numerical computation
Stefan Van Der Walt, S. Chris Colbert, and Gael Varoquaux · 2011
Earlier work this paper cites.
The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern · 2012
Earlier work this paper cites.
Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin Dogus Cubuk, and Ian Goodfellow · 2018
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Tackling the story ending biases in the story cloze test
Rishi Sharma, James Allen, Omid Bakhshandeh, and Nasrin Mostafazadeh · 2018
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WiC: the word-in-context dataset for evaluating context-sensitive meaning representations
Mohammad Taher Pilehvar and Jose Camacho-Collados · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern · 2018
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Earlier work this paper cites.
Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston · 2019
Earlier work this paper cites.
Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
Earlier work this paper cites.
Simple, scalable adaptation for neural machine translation
Ankur Bapna, Naveen Arivazhagan, and Orhan Firat · 2019
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HellaSwag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
Earlier work this paper cites.
Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela · 2019
Earlier work this paper cites.
The CommitmentBank: Investigating projection in naturally occurring discourse
Marie-Catherine de Marneffe, Mandy Simons, and Judith Tonhauser · 2019
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Fast and flexible multi-task classification using conditional neural adaptive processes
James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, and Richard E. Turner · 2019
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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, et al · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam M. Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
Earlier work this paper cites.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, T. J. Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeff Wu, and Dario Amodei · 2020
Earlier work this paper cites.
Parameter-efficient transfer learning with diff pruning
Demi Guo, Alexander M. Rush, and Yoon Kim · 2020
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Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Armen Aghajanyan, Luke Zettlemoyer, and Sonal Gupta · 2020
Earlier work this paper cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, et al · 2020
Earlier work this paper cites.
Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2020
Cited alongside, same era.
Exploiting cloze questions for few shot text classification and natural language inference
Timo Schick and Hinrich Schütze · 2020
Cited alongside, same era.
AutoPrompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh · 2020
Cited alongside, same era.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen · 2020
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization
P-Tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, and Jie Tang · 2021
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Prompt waywardness: The curious case of discretized interpretation of continuous prompts
Daniel Khashabi, Shane Lyu, Sewon Min, Lianhui Qin, Kyle Richardson, Sameer Singh, Sean Welleck, Hannaneh Hajishirzi, Tushar Khot, Ashish Sabharwal, et al · 2021
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Learning to prompt for continual learning
Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister · 2021
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Controllable generation from pre-trained language models via inverse prompting
Xu Zou, Da Yin, Qingyang Zhong, Hongxia Yang, Zhilin Yang, and Jie Tang · 2021
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Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al · 2021
Cited alongside, same era.
RAFT: A real-world few-shot text classification benchmark
Neel Alex, Eli Lifland, Lewis Tunstall, Abhishek Thakur, Pegah Maham, C Jess Riedel, Emmie Hine, Carolyn Ashurst, Paul Sedille, Alexis Carlier, et al · 2021
Cited alongside, same era.
Meta-learning via language model in-context tuning
Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, and He He · 2021
Cited alongside, same era.
Do prompt-based models really understand the meaning of their prompts?
Albert Webson and Ellie Pavlick · 2021
Cited alongside, same era.
Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh · 2021
Cited alongside, same era.
LoRA: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Cited alongside, same era.
Improving and simplifying pattern exploiting training
Derek Tam, Rakesh R Menon, Mohit Bansal, Shashank Srivastava, and Colin Raffel · 2021
Cited alongside, same era.
Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, et al · 2021
Later among the works it cites.
How many data points is a prompt worth?
Teven Le Scao and Alexander M. Rush · 2021
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Differentiable prompt makes pre-trained language models better few-shot learners
Ningyu Zhang, Luoqiu Li, Xiang Chen, Shumin Deng, Zhen Bi, Chuanqi Tan, Fei Huang, and Huajun Chen · 2021
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Learning a universal template for few-shot dataset generalization
Eleni Triantafillou, Hugo Larochelle, Richard Zemel, and Vincent Dumoulin · 2021
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Universal representation learning from multiple domains for few-shot classification
Wei-Hong Li, Xialei Liu, and Hakan Bilen · 2021
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A framework for few-shot language model evaluation, September 2021
Leo Gao, Jonathan Tow, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Kyle McDonell, Niklas Muennighoff, Jason Phang, Laria Reynolds, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou · 2021
Later among the works it cites.
Can language models learn from explanations in context?
Andrew Kyle Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane X. Wang, and Felix Hill · 2022
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Internet-augmented language models through few-shot prompting for open-domain question answering
Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, and Nikolai Grigorev · 2022
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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
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PromptSource: An integrated development environment and repository for natural language prompts
Stephen H. Bach, Victor Sanh, Zheng-Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Févry, et al · 2022
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Input-Tuning: Adapting unfamiliar inputs to frozen pretrained models
Shengnan An, Yifei Li, Zeqi Lin, Qian Liu, Bei Chen, Qiang Fu, Weizhu Chen, Nanning Zheng, and Jian-Guang Lou · 2022
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AdaPrompt: Adaptive model training for prompt-based NLP
Yulong Chen, Yang Liu, Li Dong, Shuohang Wang, Chenguang Zhu, Michael Zeng, and Yue Zhang · 2022
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Black-box prompt learning for pre-trained language models
Shizhe Diao, Xuechun Li, Yong Lin, Zhichao Huang, and Tong Zhang · 2022
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On robust prefix-tuning for text classification
Zonghan Yang and Yang Liu · 2022
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PSP: Pre-trained soft prompts for few-shot abstractive summarization
Xiaochen Liu, Yu Bai, Jiawei Li, Yinan Hu, and Yang Gao · 2022
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Using natural language prompts for machine translation
Xavier Garcia and Orhan Firat · 2022
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Co-training improves prompt-based learning for large language models
Hunter Lang, Monica Agrawal, Yoon Kim, and David Sontag · 2022
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HyperPrompt: Prompt-based task-conditioning of transformers
Yun He, Huaixiu Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, YaGuang Li, Zhao Chen, Donald Metzler, et al · 2022
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Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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PERFECT: Prompt-free and efficient few-shot learning with language models
Rabeeh Karimi Mahabadi, Luke Zettlemoyer, James Henderson, Marzieh Saeidi, Lambert Mathias, Veselin Stoyanov, and Majid Yazdani · 2022
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Nafise Sadat Moosavi, Quentin Delfosse, Kristian Kersting, and Iryna Gurevych · 2022
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