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Large language models (LLMs) have achieved remarkable performance on Natural Language Processing (NLP) tasks, but they are hindered by high computational costs and memory requirements.
The penn treebank: Annotating predicate argument structure
Mitchell P. Marcus, Grace Kim, Mary Ann Marcinkiewicz, Robert MacIntyre, Ann Bies, Mark Ferguson, Karen Katz, and Britta Schasberger · 1994
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Elements of information theory
Thomas M Cover · 1999
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron C. Courville · 2013
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1.1 computing’s energy problem (and what we can do about it)
Mark Horowitz · 2014
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
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Pointer sentinel mixture models
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew G. Howard, Hartwig Adam, and Dmitry Kalenichenko · 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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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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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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Up or down? adaptive rounding for post-training quantization
Markus Nagel, Rana Ali Amjad, Mart van Baalen, Christos Louizos, and Tijmen Blankevoort · 2020
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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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Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2020
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BRECQ: pushing the limit of post-training quantization by block reconstruction
Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu · 2021
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Tilenet: Hardware accelerator for ternary convolutional neural networks
Sagar Eetha, PK Sruthi, Vibha Pant, Sai Vikram, Mihir Mody, and Madhura Purnaprajna · 2021
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OPT: open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona T. Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer · 2022
Omniquant: Omnidirectionally calibrated quantization for large language models
Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, and Ping Luo · 2023
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Quip: 2-bit quantization of large language models with guarantees
Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, and Christopher De Sa · 2023
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LLM-QAT: data-free quantization aware training for large language models
Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, and Vikas Chandra · 2023
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Xiuying Wei, Yunchen Zhang, Yuhang Li, Xiangguo Zhang, Ruihao Gong, Jinyang Guo, and Xianglong Liu · 2023
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Outlier suppression: Pushing the limit of low-bit transformer language models
Xiuying Wei, Yunchen Zhang, Xiangguo Zhang, Ruihao Gong, Shanghang Zhang, Qi Zhang, Fengwei Yu, and Xianglong Liu · 2022
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GPTQ: accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2022
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Tab: Unified and optimized ternary, binary, and mixed-precision neural network inference on the edge
Shien Zhu, Luan HK Duong, and Weichen Liu · 2022
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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
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AWQ: activation-aware weight quantization for LLM compression and acceleration
Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, and Song Han · 2023
Cited alongside, same era.
Smoothquant: Accurate and efficient post-training quantization for large language models
Guangxuan Xiao, Ji Lin, Mickaël Seznec, Hao Wu, Julien Demouth, and Song Han · 2023
Cited alongside, same era.
PB-LLM: partially binarized large language models
Yuzhang Shang, Zhihang Yuan, Qiang Wu, and Zhen Dong · 2023
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Ternary weight networks
Bin Liu, Fengfu Li, Xiaogang Wang, Bo Zhang, and Junchi Yan · 2023
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Redpajama: an open dataset for training large language models, 2023
Together Computer · 2023
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Exploring the limits of transfer learning with unified model in the cybersecurity domain
Kuntal Kumar Pal, Kazuaki Kashihara, Ujjwala Anantheswaran, Kirby C. Kuznia, Siddhesh Jagtap, and Chitta Baral · 2023
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Word embeddings revisited: Do llms offer something new?, 2024
Matthew Freestone and Shubhra Kanti Karmaker Santu · 2024
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DB-LLM: accurate dual-binarization for efficient llms
Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, and Dacheng Tao · 2024
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How good are low-bit quantized llama3 models? an empirical study, 2024
Wei Huang, Xudong Ma, Haotong Qin, Xingyu Zheng, Chengtao Lv, Hong Chen, Jie Luo, Xiaojuan Qi, Xianglong Liu, and Michele Magno · 2024
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