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We propose a new strategy for applying large pre-trained language models to novel tasks when labeled training data is limited.
Maximum likelihood estimation of observer error-rates using the EM algorithm
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
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Distant supervision for relation extraction without labeled data
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Data programming: Creating large training sets, quickly
Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher Ré · 2016
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Socratic learning: Augmenting generative models to incorporate latent subsets in training data
Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, and Christopher Ré · 2016
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Learning the structure of generative models without labeled data
Stephen H. Bach, Bryan He, Alexander Ratner, and Christopher Ré · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Snuba: Automating weak supervision to label training data
Paroma Varma and Christopher Ré · 2018
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Snorkel DryBell: A case study in deploying weak supervision at industrial scale
Stephen H. Bach, Daniel Rodriguez, Yintao Liu, Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen, Alexander Ratner, Braden Hancock, Houman Alborzi, Rahul Kuchhal, Christopher Ré, and Rob Malkin · 2019
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Osprey: Weak supervision of imbalanced extraction problems without code
Eran Bringer, Abraham Israeli, Yoav Shoham, Alex Ratner, and Christopher Ré · 2019
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Medical device surveillance with electronic health records
Alison Callahan, Jason A Fries, Christopher Ré, James I Huddleston, Nicholas J Giori, Scott Delp, and Nigam H Shah · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Training complex models with multi-task weak supervision
Alexander J Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, and Christopher Ré · 2019
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Multi-resolution weak supervision for sequential data
Frederic Sala, Paroma Varma, Shiori Sagawa, Jason Fries, Daniel Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, et al · 2019
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Learning dependency structures for weak supervision models
Paroma Varma, Fred Sala, Ann He, Alex Ratner, and Christopher Ré · 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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Active and incremental learning with weak supervision
Clemens-Alexander Brust, Christoph Käding, and Joachim Denzler · 2020
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Cross-modal data programming enables rapid medical machine learning
Jared A Dunnmon, Alexander J Ratner, Khaled Saab, Nishith Khandwala, Matthew Markert, Hersh Sagreiya, Roger Goldman, Christopher Lee-Messer, Matthew P Lungren, Daniel L Rubin, et al · 2020
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Fast and three-rious: Speeding up weak supervision with triplet methods
Daniel Fu, Mayee Chen, Frederic Sala, Sarah Hooper, Kayvon Fatahalian, and Christopher Ré · 2020
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The pile: An 800GB dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 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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Ro{bert}a: A robustly optimized {bert} pretraining approach, 2020
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2020
Cited alongside, same era.
Do prompt-based models really understand the meaning of their prompts?
Albert Webson and Ellie Pavlick · 2021
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Fine-tuning pre-trained language model with weak supervision: A contrastive-regularized self-training approach
Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, and Chao Zhang · 2021
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WRENCH: A comprehensive benchmark for weak supervision
Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, and Alexander Ratner · 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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Prompt consistency for zero-shot task generalization
Anonymous Authors · 2022
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
Cited alongside, same era.
Snorkel: Rapid training data creation with weak supervision
A. J. Ratner, S. H. Bach, H. E. Ehrenberg, J. Fries, S. Wu, and C. Ré · 2020
Cited alongside, same era.
Weakly supervised sequence tagging from noisy rules
Esteban Safranchik, Shiying Luo, and Stephen H. Bach · 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.
Leveraging organizational resources to adapt models to new data modalities
Sahaana Suri, Raghuveer Chanda, Neslihan Bulut, Pradyumna Narayana, Yemao Zeng, Peter Bailis, Sugato Basu, Girija Narlikar, Christopher Ré, and Abishek Sethi · 2020
Cited alongside, same era.
Active weasul: Improving weak supervision with active learning
Samantha Biegel, Rafah El-Khatib, Luiz Otavio Vilas Boas Oliveira, Max Baak, and Nanne Aben · 2021
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
Cited alongside, same era.
Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, and Rodrigo Nogueira · 2022
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Shoring up the foundations: Fusing model embeddings and weak supervision
Mayee F Chen, Daniel Y Fu, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, and Christopher Ré · 2022
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RelationPrompt: Leveraging prompts to generate synthetic data for zero-shot relation triplet extraction
Yew Ken Chia, Lidong Bing, Soujanya Poria, and Luo Si · 2022
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No one representation to rule them all: Overlapping features of training methods
Raphael Gontijo-Lopes, Yann Dauphin, and Ekin Dogus Cubuk · 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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Cross-task generalization via natural language crowdsourcing instruction
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe · 2022
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush · 2022
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Universalizing weak supervision
Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Roberts, and Frederic Sala · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou · 2022
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Generating data to mitigate spurious correlations in natural language inference datasets
Yuxiang Wu, Matt Gardner, Pontus Stenetorp, and Pradeep Dasigi · 2022
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ZeroGen: Efficient zero-shot learning via dataset generation
Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, and Lingpeng Kong · 2022
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Learning from multiple noisy partial labelers
P. Yu, T. Ding, and S. H. Bach · 2022
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STaR: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, and Noah D Goodman · 2022
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