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Natural language prompts have been shown to facilitate cross-task generalization for large language models.
Probability of error of some adaptive pattern-recognition machines
H. J. Scudder III · 1965
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The pascal recognising textual entailment challenge
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Effective self-training for parsing
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Choice of plausible alternatives: An evaluation of commonsense causal reasoning
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The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern · 2012
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
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Training deep neural networks on noisy labels with bootstrapping
Scott E. Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2015
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LSDSem 2017 shared task: The story cloze test
Nasrin Mostafazadeh, Michael Roth, Annie Louis, Nathanael Chambers, and James Allen · 2017
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Transductive semi-supervised deep learning using min-max features
Weiwei Shi, Yihong Gong, Chris Ding, Zhiheng MaXiaoyu Tao, and Nanning Zheng · 2018
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The commitmentbank: Investigating projection in naturally occurring discourse
Marie-Catherine De Marneffe, Mandy Simons, and Judith Tonhauser · 2019
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Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondřej Chum · 2019
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WiC: the word-in-context dataset for evaluating context-sensitive meaning representations
Mohammad Taher Pilehvar and Jose Camacho-Collados · 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
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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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Revisiting self-training for neural sequence generation
Junxian He, Jiatao Gu, Jiajun Shen, and Marc’Aurelio Ranzato · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig · 2020
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Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela · 2020
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Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2020
Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze · 2021
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Spot: Better frozen model adaptation through soft prompt transfer
Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou, and Daniel Cer · 2021
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Towards zero-label language learning
Zirui Wang, Adams Wei Yu, Orhan Firat, and Yuan Cao · 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
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh · 2021
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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.
Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard H. Hovy, and Quoc V. Le · 2020
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, and Quoc Le · 2020
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen · 2021
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Ppt: Pre-trained prompt tuning for few-shot learning
Yuxian Gu, Xu Han, Zhiyuan Liu, and Minlie Huang · 2021
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Reordering examples helps during priming-based few-shot learning
Sawan Kumar and Partha Talukdar · 2021
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Fewnlu: Benchmarking state-of-the-art methods for few-shot natural language understanding
Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Jian Li, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, and Zhilin Yang · 2021
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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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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, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-David, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Alan Fries, Maged Saeed AlShaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Mike Tian-Jian Jiang, and Alexander M. Rush · 2022
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Rlprompt: Optimizing discrete text prompts with reinforcement learning
Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P Xing, and Zhiting Hu · 2022
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Co-training improves prompt-based learning for large language models
Hunter Lang, Monica N Agrawal, Yoon Kim, and David Sontag · 2022
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp · 2022
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Generating training data with language models: Towards zero-shot language understanding
Yu Meng, Jiaxin Huang, Yu Zhang, and Jiawei Han · 2022
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Grips: Gradient-free, edit-based instruction search for prompting large language models
Archiki Prasad, Peter Hase, Xiang Zhou, and Mohit Bansal · 2022
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Controllable natural language generation with contrastive prefixes
Jing Qian, Li Dong, Yelong Shen, Furu Wei, and Weizhu Chen · 2022
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Zeroprompt: Scaling prompt-based pretraining to 1,000 tasks improves zero-shot generalization
Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Yanggang Wang, Haiyu Li, and Zhilin Yang · 2022
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Prompt consistency for zero-shot task generalization
Chunting Zhou, Junxian He, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig · 2022
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