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Recent explorations of large-scale pre-trained language models (PLMs) have revealed the power of PLMs with huge amounts of parameters, setting off a wave of training ever-larger PLMs.
Reducing bert pre-training time from 3 days to 76 minutes
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Roy Schwartz, Jesse Dodge, Noah A Smith, and Oren Etzioni. 2019 · 1907
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Megatron-lm: Training multi-billion parameter language models using model parallelism
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Scaling laws for neural language models
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Distilling the knowledge in a neural network
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
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Chemprot-3.0: a global chemical biology diseases mapping
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Born-again neural networks
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Measuring the evolution of a scientific field through citation frames
David Jurgens, Srijan Kumar, Raine Hoover, Dan McFarland, and Dan Jurafsky. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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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 · 2019
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Efficient training of BERT by progressively stacking
Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, and Tie-Yan Liu. 2019 · 2019
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Patient knowledge distillation for BERT model compression
Siqi Sun, Yu Cheng, Zhe Gan, and Jingjing Liu. 2019 · 2019
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Xu Tan, Yi Ren, Di He, Tao Qin, Zhou Zhao, and Tie-Yan Liu. 2019 · 2019
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019
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Training deep neural networks in generations: A more tolerant teacher educates better students
Contrastive distillation on intermediate representations for language model compression
Siqi Sun, Zhe Gan, Yuwei Fang, Yu Cheng, Shuohang Wang, and Jingjing Liu. 2020 · 2020
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Large batch optimization for deep learning: Training BERT in 76 minutes
Yang You, Jing Li, Sashank J. Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh. 2020 · 2020
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Revisiting knowledge distillation via label smoothing regularization
Li Yuan, Francis EH Tay, Guilin Li, Tao Wang, and Jiashi Feng. 2020 · 2020
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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 · 2021
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
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Chenglin Yang, Lingxi Xie, Siyuan Qiao, and Alan L Yuille. 2019 · 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, 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
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Don’t stop pretraining: Adapt language models to domains and tasks
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TinyBERT: Distilling BERT for natural language understanding
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Thieves on sesame street! model extraction of bert-based apis
Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, and Mohit Iyyer. 2020 · 2020
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Budgeted training: Rethinking deep neural network training under resource constraints
Mengtian Li, Ersin Yumer, and Deva Ramanan. 2020a · 2020
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Train big, then compress: Rethinking model size for efficient training and inference of transformers
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William Fedus, Barret Zoph, and Noam Shazeer. 2021 · 2021
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On the transformer growth for progressive BERT training
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Pre-trained models: Past, present and future
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Recent advances in natural language processing via large pre-trained language models: A survey
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Carbon emissions and large neural network training
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Is label smoothing truly incompatible with knowledge distillation: An empirical study
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bert2bert: Towards reusable pretrained language models
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