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Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but demand massive GPU resources for training.
Training large neural networks with constant memory using a new execution algorithm
Bharadwaj Pudipeddi, Maral Mesmakhosroshahi, Jinwen Xi, and Sujeeth Bharadwaj. 2020 · 2002
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The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S. Gordon. 2011 · 2011
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The winograd schema challenge
Hector J. Levesque, Ernest Davis, and Leora Morgenstern. 2012 · 2012
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Adam: A method for stochastic optimization
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Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin. 2016 · 2016
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An overview of gradient descent optimization algorithms
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Automatic differentiation in pytorch
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Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
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Mixed precision training
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Superneurons: dynamic gpu memory management for training deep neural networks
Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Song, Zenglin Xu, and Tim Kraska. 2018 · 2018
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Boolq: Exploring the surprising difficulty of natural yes/no questions
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Visualizing and understanding the effectiveness of BERT
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Every local minimum value is the global minimum value of induced model in nonconvex machine learning
Kenji Kawaguchi, Jiaoyang Huang, and Leslie Pack Kaelbling. 2019 · 2019
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Efficient large-scale language model training on gpu clusters using megatron-lm
Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, et al. 2021 · 2021
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Zero-infinity: Breaking the gpu memory wall for extreme scale deep learning
Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, and Yuxiong He. 2021 · 2021
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Sentinel: Efficient tensor migration and allocation on heterogeneous memory systems for deep learning
Jie Ren, Jiaolin Luo, Kai Wu, Minjia Zhang, Hyeran Jeon, and Dong Li. 2021a · 2021
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8-bit optimizers via block-wise quantization
Tim Dettmers, Mike Lewis, Sam Shleifer, and Luke Zettlemoyer. 2022 · 2022
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Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, Jing Yi, Weilin Zhao, Xiaozhi Wang, Zhiyuan Liu, Hai-Tao Zheng, Jianfei Chen, Yang Liu, Jie Tang, Juanzi Li, and Maosong Sun. 2022 · 2022
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Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Wic: the word-in-context dataset for evaluating context-sensitive meaning representations
Mohammad Taher Pilehvar and José Camacho-Collados. 2019 · 2019
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Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019
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Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
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Ultra-low precision 4-bit training of deep neural networks
Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan. 2020b · 2020
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Zero-offload: Democratizing billion-scale model training
Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, and Yuxiong He. 2021b
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Lora: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022 · 2022
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Fine-tuning language models with just forward passes
Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, and Sanjeev Arora. 2023 · 2023
Closest in time.
Xianghui Sun, Yunjie Ji, Baochang Ma, and Xiangang Li. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Galore: Memory-efficient LLM training by gradient low-rank projection
Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, and Yuandong Tian. 2024 · 2024
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