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Many NLP tasks benefit from using large language models (LLMs) that often have more than 100 billion parameters.
Kademlia: A peer-to-peer information system based on the xor metric
Petar Maymounkov and David Mazieres. 2002 · 2002
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Bharadwaj Pudipeddi, Maral Mesmakhosroshahi, Jinwen Xi, and Sujeeth Bharadwaj. 2020 · 2002
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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 · 2005
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Dmitry Lepikhin, H. Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Y. Huang, M. Krikun, Noam Shazeer, and Z. Chen. 2020 · 2006
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Adapterhub: A framework for adapting transformers
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A pragmatic introduction to secure multi-party computation
David Evans, Vladimir Kolesnikov, Mike Rosulek, et al. 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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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 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Accelerate: Run your raw pytorch training script on any kind of device
Hugging Face and contributors. 2020 · 2020
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Scaling laws for neural language models
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Hivemind: a Library for Decentralized Deep Learning
Team Learning@home. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
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Nvidia ampere ga102 gpu architecture
NVIDIA. 2020 · 2020
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Towards crowdsourced training of large neural networks using decentralized mixture-of-experts
Max Ryabinin and Anton Gusev. 2020 · 2020
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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, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, and Laurent Sifre. 2021 · 2021
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Glam: Efficient scaling of language models with mixture-of-experts
Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathy Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V. Le, Yonghui Wu, Zhifeng Chen, and Claire Cui. 2021 · 2021
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer. 2021 · 2021
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A framework for few-shot language model evaluation
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 · 2021
Training neural networks with fixed sparse masks
Yi-Lin Sung, Varun Nair, and Colin Raffel. 2021 · 2021
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TensorFlow Hub
TensorFlow Hub · 2021
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Wei Zeng, Xiaozhe Ren, Teng Su, Hui Wang, Yi Liao, Zhiwei Wang, Xin Jiang, ZhenZhang Yang, Kaisheng Wang, Xiaoda Zhang, Chen Li, Ziyan Gong, Yifan Yao, Xinjing Huang, Jun Wang, Jianfeng Yu, Qi Guo, Yue Yu, Yan Zhang, Jin Wang, Hengtao Tao, Dasen Yan, Zexuan Yi, Fang Peng, Fangqing Jiang, Han Zhang, Lingfeng Deng, Yehong Zhang, Zhe Lin, Chao Zhang, Shaojie Zhang, Mingyue Guo, Shanzhi Gu, Gaojun Fan, Yaowei Wang, Xuefeng Jin, Qun Liu, and Yonghong Tian. 2021 · 2021
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Jurassic-1 language models
AI21 · 2022
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Parameter-efficient transfer learning with diff pruning
Demi Guo, Alexander M Rush, and Yoon Kim. 2021 · 2021
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Lora: Low-rank adaptation of large language models
Edward Hu, Yelong Shen, Phil Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Lu Wang, and Weizhu Chen. 2021 · 2021
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Dynabench: Rethinking benchmarking in nlp
Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, and Adina Williams. 2021 · 2021
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Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Dong-Hyun Kwak, Dong Hyeon Jeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, Heungsub Lee, Minyoung Jeong, Sungjae Lee, Minsub Kim, SukHyun Ko, Seokhun Kim, Taeyong Park, Jinuk Kim, Soyoung Kang, Na-Hyeon Ryu, Kang Min Yoo, Minsuk Chang, Soobin Suh, Sookyo In, Jinseong Park, Kyungduk Kim, Hiun Kim, Jisu Jeong, Yong Goo Yeo, Donghoon Ham, Dongju Park, Min Young Lee, Jaewook Kang, Inho Kang, Jung-Woo Ha, Woo-Myoung Park, and Nako Sung. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
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Efficient large-scale language model training on gpu clusters
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PyTorch Hub
PyTorch Hub · 2021
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Yalm 100b
Michael Khrushchev, Ruslan Vasilev, Nikolay Zinov, Alexey Petrov, and Yandex. 2022 · 2022
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Crosslingual generalization through multitask finetuning
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Nvidia confidential computing
NVIDIA. 2022 · 2022
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Build next-gen apps with openai’s powerful models
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BLOOM: a 176B-parameter open-access multilingual language model
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GLM-130B: An open bilingual pre-trained model
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Swarm parallelism: Training large models can be surprisingly communication-efficient
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