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Pruning large language models (LLMs) is a challenging task due to their enormous size.
Optimal brain damage
Yann LeCun, John Denker, and Sara Solla · 1989
Earlier work this paper cites.
Optimal brain surgeon and general network pruning
Babak Hassibi, David G Stork, and Gregory J Wolff · 1993
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Distributed optimization and statistical learning via the alternating direction method of multipliers
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Pointer sentinel mixture models
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord · 2018
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Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
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A systematic dnn weight pruning framework using alternating direction method of multipliers
Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Jian Tang, Wujie Wen, Makan Fardad, and Yanzhi Wang · 2018
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To prune, or not to prune: Exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2018
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Boolq: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova · 2019
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Model compression with adversarial robustness: A unified optimization framework
Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, and Ji Liu · 2019
Earlier work this paper cites.
Adversarial robustness vs. model compression, or both?
Shaokai Ye, Kaidi Xu, Sijia Liu, Hao Cheng, Jan-Henrik Lambrechts, Huan Zhang, Aojun Zhou, Kaisheng Ma, Yanzhi Wang, and Xue Lin · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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What is the state of neural network pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 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, et al · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Woodfisher: Efficient second-order approximation for neural network compression
Sidak Pal Singh and Dan Alistarh · 2020
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Sparsegpt: Massive language models can be accurately pruned in one-shot
Elias Frantar and Dan Alistarh · 2023
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Specializing smaller language models towards multi-step reasoning
Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, and Tushar Khot · 2023
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Minillm: Knowledge distillation of large language models
Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang · 2023
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Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister · 2023
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Llm-pruner: On the structural pruning of large language models
Xinyin Ma, Gongfan Fang, and Xinchao Wang · 2023
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A framework for few-shot language model evaluation
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Lora: Low-rank adaptation of large language models
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Accelerated sparse neural training: A provable and efficient method to find n: m transposable masks
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Winogrande: An adversarial winograd schema challenge at scale
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Gradient descent on neurons and its link to approximate second-order optimization
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Llm. int8 (): 8-bit matrix multiplication for transformers at scale
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Optimal brain compression: A framework for accurate post-training quantization and pruning
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Mingjie Sun, Zhuang Liu, Anna Bair, and J Zico Kolter · 2023
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Haojun Xia, Zhen Zheng, Yuchao Li, Donglin Zhuang, Zhongzhu Zhou, Xiafei Qiu, Yong Li, Wei Lin, and Shuaiwen Leon Song · 2023
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Smoothquant: Accurate and efficient post-training quantization for large language models
Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han · 2023
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Outlier weighed layerwise sparsity (owl): A missing secret sauce for pruning llms to high sparsity
Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Mykola Pechenizkiy, Yi Liang, Zhangyang Wang, and Shiwei Liu · 2023
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Dynamic sparse no training: Training-free fine-tuning for sparse llms
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Enabling high-sparsity foundational llama models with efficient pretraining and deployment
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The unreasonable ineffectiveness of the deeper layers
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Shortgpt: Layers in large language models are more redundant than you expect
Xin Men, Mingyu Xu, Qingyu Zhang, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, and Weipeng Chen · 2024
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