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Through their transfer learning abilities, highly-parameterized large pre-trained language models have dominated the NLP landscape for a multitude of downstream language tasks.
Trust in numbers
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Georgios Spithourakis and Sebastian Riedel · 2018
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2018
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Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
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Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner · 2019
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Devin Johnson, Denise Mak, Andrew Barker, and Lexi Loessberg-Zahl · 2020
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Dhanasekar Sundararaman, Shijing Si, Vivek Subramanian, Guoyin Wang, Devamanyu Hazarika, and Lawrence Carin · 2020
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Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Equate: A benchmark evaluation framework for quantitative reasoning in natural language inference
Abhilasha Ravichander, Aakanksha Naik, Carolyn Rose, and Eduard Hovy · 2019
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Distilbert, a distilled bersion of bert: Smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
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Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, and Matt Gardner · 2019
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Predicting neural network accuracy from weights
Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, and Ilya Tolstikhin · 2020
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Do language embeddings capture scales?
Xikun Zhang, Deepak Ramachandran, Ian Tenney, Yanai Elazar, and Dan Roth · 2020
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Innovations in neural data-to-text generation
Mandar Sharma, Ajay Gogineni, and Naren Ramakrishnan · 2022
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