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Recent studies introduced effective compression techniques for Large Language Models (LLMs) via post-training quantization or low-bit weight representation.
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 · 1901
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Measuring calibration in deep learning
Jeremy Nixon, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran. 2019 · 1904
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeff Wu, and Dario Amodei. 2020 · 2001
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht. 2015 · 2015
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LSDSem 2017 shared task: The story cloze test
Nasrin Mostafazadeh, Michael Roth, Annie Louis, Nathanael Chambers, and James Allen. 2017 · 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 · 2018
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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. 2017 · 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 · 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 · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam M. Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019 · 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 · 2019
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PiQA: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al. 2020 · 2020
Cited alongside, same era.
Calibration of pre-trained transformers
Shrey Desai and Greg Durrett. 2020 · 2020
Cited alongside, same era.
Compression of deep learning models for text: A survey
Manish Gupta and Puneet Agrawal. 2020 · 2020
Cited alongside, same era.
How can we know when language models know? On the calibration of language models for question answering
Zhengbao Jiang, J. Araki, Haibo Ding, and Graham Neubig. 2020 · 2020
Cited alongside, same era.
Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
Cited alongside, same era.
How can we know when language models know? on the calibration of language models for question answering
BLOOM: A 176b-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022 · 2022
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Compression of generative pre-trained language models via quantization
Chaofan Tao, Lu Hou, Wei Zhang, Lifeng Shang, Xin Jiang, Qun Liu, Ping Luo, and Ngai Wong. 2022 · 2022
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Exploring predictive uncertainty and calibration in NLP: A study on the impact of method & data scarcity
Dennis Ulmer, Jes Frellsen, and Christian Hardmeier. 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 Huai hsin Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022 · 2022
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Uncertainty quantification with pre-trained language models: A large-scale empirical analysis
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Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig. 2021 · 2021
Cited alongside, same era.
Language models are few-shot multilingual learners
Genta Indra Winata, Andrea Madotto, Zhaojiang Lin, Rosanne Liu, Jason Yosinski, and Pascale Fung. 2021 · 2021
Cited alongside, same era.
On the calibration of massively multilingual language models
Kabir Ahuja, Sunayana Sitaram, Sandipan Dandapat, and Monojit Choudhury. 2022 · 2022
Cited alongside, same era.
LLM.int8(): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
GPTQ: Accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. 2022 · 2022
Cited alongside, same era.
Accelerate: Training and inference at scale made simple, efficient and adaptable
Sylvain Gugger, Lysandre Debut, Thomas Wolf, Philipp Schmid, Zachary Mueller, Sourab Mangrulkar, Marc Sun, and Benjamin Bossan. 2022 · 2022
Cited alongside, same era.
Bridging fairness and environmental sustainability in natural language processing
Marius Hessenthaler, Emma Strubell, Dirk Hovy, and Anne Lauscher. 2022 · 2022
Cited alongside, same era.
Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2022b · 2022
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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 · 2022
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OPTQ: Accurate quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. 2023 · 2023
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A framework for few-shot language model evaluation
Leo Gao, Jonathan Tow, Baber Abbasi, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Alain Le Noac’h, Haonan Li, Kyle McDonell, Niklas Muennighoff, Chris Ociepa, Jason Phang, Laria Reynolds, Hailey Schoelkopf, Aviya Skowron, Lintang Sutawika, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou. 2023 · 2023
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Albert Qiaochu Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, L’elio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
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Do emergent abilities exist in quantized large language models: An empirical study
Peiyu Liu, Zikang Liu, Ze-Feng Gao, Dawei Gao, Wayne Xin Zhao, Yaliang Li, Bolin Ding, and Ji rong Wen. 2023 · 2023
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A comparative study on the impact of model compression techniques on fairness in language models
Krithika Ramesh, Arnav Chavan, Shrey Pandit, and Sunayana Sitaram. 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, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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