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Scaling large language models (LLMs) significantly improves performance but comes with prohibitive computational costs.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, *Azalia Mirhoseini, *Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Crowdsourcing multiple choice science questions
Johannes Welbl, Nelson F. Liu, and Matt Gardner · 2017
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Gpipe: Efficient training of giant neural networks using pipeline parallelism
Yanping Huang, Yonglong Cheng, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, and Z. Chen · 2018
Earlier work this paper cites.
Can a suit of armor conduct electricity? a new dataset for open book question answering, 2018
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Earlier work this paper cites.
Boolq: Exploring the surprising difficulty of natural yes/no questions, 2019
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova · 2019
Earlier work this paper cites.
Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He · 2019
Earlier work this paper cites.
Megatron-lm: Training multi-billion parameter language models using model parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2019
Earlier work this paper cites.
Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi · 2020
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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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris 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
Cited alongside, same era.
Scaling laws for neural language models, 2020
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Cited alongside, same era.
Gshard: Scaling giant models with conditional computation and automatic sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam M. Shazeer, and Z. Chen · 2020
Cited alongside, same era.
Logiqa: A challenge dataset for machine reading comprehension with logical reasoning
Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang · 2020
Cited alongside, same era.
TruthfulQA: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
Later among the works it cites.
Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
Later among the works it cites.
Mixture-of-experts with expert choice routing
Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Y Zhao, Andrew M. Dai, Zhifeng Chen, Quoc V Le, and James Laudon · 2022
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Sparse moe as the new dropout: Scaling dense and self-slimmable transformers
Tianlong Chen, Zhenyu Zhang, Ajay Kumar Jaiswal, Shiwei Liu, and Zhangyang Wang · 2023
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Sparse upcycling: Training mixture-of-experts from dense checkpoints
Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, Carlos Riquelme Ruiz, Basil Mustafa, Joshua Ainslie, Yi Tay, Mostafa Dehghani, and Neil Houlsby · 2023
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Ring attention with blockwise transformers for near-infinite context
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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.
Megatron-lm: Training multi-billion parameter language models using model parallelism, 2020
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2020
Cited alongside, same era.
CCNet: Extracting high quality monolingual datasets from web crawl data
Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Edouard Grave · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam M. Shazeer · 2021
Cited alongside, same era.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2021
Cited alongside, same era.
Reducing activation recomputation in large transformer models
Vijay Anand Korthikanti, Jared Casper, Sangkug Lym, Lawrence C. McAfee, Michael Andersch, Mohammad Shoeybi, and Bryan Catanzaro · 2022
Cited alongside, same era.
Palm: scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sashank Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel
Cited in the paper.
Hao Liu, Matei Zaharia, and Pieter Abbeel · 2023
Later among the works it cites.
Pytorch fsdp: Experiences on scaling fully sharded data parallel
Yanli Zhao, Andrew Gu, Rohan Varma, Liangchen Luo, Chien chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, Alban Desmaison, Can Balioglu, Bernard Nguyen, Geeta Chauhan, Yuchen Hao, and Shen Li · 2023
Later among the works it cites.
Vision transformers need registers
Timothée Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski · 2024
Closest in time.
Upcycling large language models into mixture of experts, 2024
Ethan He, Abhinav Khattar, Ryan Prenger, Vijay Korthikanti, Zijie Yan, Tong Liu, Shiqing Fan, Ashwath Aithal, Mohammad Shoeybi, and Bryan Catanzaro · 2024
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Mixtral of experts, 2024
Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2024
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