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Large language models (LLMs) targeting different deployment scales and sizes are currently produced by training each variant from scratch; this is extremely compute-intensive.
On information and sufficiency
Solomon Kullback and Richard A. Leibler · 1951
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Distilling the knowledge in a neural network, 2015
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
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Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 2017
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A survey on deep neural network compression: Challenges, overview, and solutions
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang · 2018
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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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Soft filter pruning for accelerating deep convolutional neural networks
Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, and Yi Yang · 2018
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2019
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HellaSwag: Can a machine really finish your sentence?
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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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An survey of neural network compression
Jianping Gou, Baosheng Yu, Stephen J. Maybank, and Dacheng Tao · 2020
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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
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Xl-sum: Large-scale multilingual abstractive summarization for 44 languages, 2021
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2021
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Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2021
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WinoGrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2021
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MiniLMv2: Multi-head self-attention relation distillation for compressing pretrained transformers
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Rethinking soft labels for knowledge distillation: A bias-variance tradeoff perspective
Helong Zhou, Liangchen Song, Jiajie Chen, Ye Zhou, Guoli Wang, Junsong Yuan, and Qian Zhang · 2021
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Truthfulqa: Measuring how models mimic human falsehoods, 2022
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
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Knowledge distillation of transformer-based language models revisited, 2022
Chengqiang Lu, Jianwei Zhang, Yunfei Chu, Zhengyu Chen, Jingren Zhou, Fei Wu, Haiqing Chen, and Hongxia Yang · 2022
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Chain-of-thought prompting elicits reasoning in large language models
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Everybody prune now: Structured pruning of llms with only forward passes
Lucio Dery, Steven Kolawole, Jean-Francois Kagey, Virginia Smith, Graham Neubig, and Ameet Talwalkar · 2024
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How do language models put attention weights over long context?
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The unreasonable ineffectiveness of the deeper layers
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Minillm: Knowledge distillation of large language models, 2024
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Slicegpt: Compress large language models by deleting rows and columns
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Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al · 2023
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Shortened LLaMA: A simple depth pruning for large language models
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ShortGPT: Layers in Large Language Models are More Redundant Than You Expect, 2024
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A comprehensive survey of compression algorithms for language models
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Data, data everywhere: A guide for pretraining dataset construction, 2024
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Model compression and efficient inference for large language models: A survey
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Laco: Large language model pruning via layer collapse
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