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Large language models (LLMs) are increasingly recognized for their exceptional generative capabilities and versatility across various tasks.
Quantifying the carbon emissions of machine learning
Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres. 2019 · 1910
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel. 2022 · 1965
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Lasse F. Wolff Anthony, Benjamin Kanding, and Raghavendra Selvan. 2020 · 2007
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Qingqing Cao, Aruna Balasubramanian, and Niranjan Balasubramanian. 2020 · 2010
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Abstractive text summarization using sequence-to-sequence rnns and beyond
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SQuAD: 100,000+ questions for machine comprehension of text
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Attention is all you need
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Amazon ec2 update–inf1 instances with aws inferentia chips for high performance cost-effective inferencing
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Towards the systematic reporting of the energy and carbon footprints of machine learning
Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, and Joelle Pineau. 2020 · 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 · 2020
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Green ai
Roy Schwartz, Jesse Dodge, Noah A Smith, and Oren Etzioni. 2020 · 2020
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Tree of thoughts: Deliberate problem solving with large language models, 2023
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. 2023 · 2020
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Compute and energy consumption trends in deep learning inference
Radosvet Desislavov, Fernando Martínez-Plumed, and José Hernández-Orallo. 2021 · 2021
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Green algorithms: quantifying the carbon footprint of computation
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Towards quantifying the carbon emissions of differentially private machine learning
Rakshit Naidu, Harshita Diddee, Ajinkya Mulay, Aleti Vardhan, Krithika Ramesh, and Ahmed Zamzam. 2021 · 2021
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Carbon emissions and large neural network training
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean. 2021 · 2021
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Codecarbon: estimate and track carbon emissions from machine learning computing
Victor Schmidt, Kamal Goyal, Aditya Joshi, Boris Feld, Liam Conell, Nikolas Laskaris, Doug Blank, Jonathan Wilson, Sorelle Friedler, and Sasha Luccioni. 2021 · 2021
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Towards efficient post-training quantization of pre-trained language models
Chain-of-thought prompting elicits reasoning in large language models
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Sustainable ai: Environmental implications, challenges and opportunities
Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga, Jinshi Huang, Charles Bai, et al. 2022 · 2022
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How to estimate carbon footprint when training deep learning models? a guide and review
Lucía Bouza, Aurélie Bugeau, and Loïc Lannelongue. 2023 · 2023
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Evaluating the carbon impact of large language models at the inference stage
Brad Everman, Trevor Villwock, Dayuan Chen, Noe Soto, Oliver Zhang, and Ziliang Zong. 2023 · 2023
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An experimental comparison of software-based power meters: focus on cpu and gpu
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Haoli Bai, Lu Hou, Lifeng Shang, Xin Jiang, Irwin King, and Michael R Lyu. 2022 · 2022
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Measuring the carbon intensity of ai in cloud instances
Jesse Dodge, Taylor Prewitt, Remi Tachet des Combes, Erika Odmark, Roy Schwartz, Emma Strubell, Alexandra Sasha Luccioni, Noah A Smith, Nicole DeCario, and Will Buchanan. 2022 · 2022
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The carbon footprint of machine learning training will plateau, then shrink
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Eco2ai: carbon emissions tracking of machine learning models as the first step towards sustainable ai
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Efficiently scaling transformer inference. corr, abs/2211.05102 (2022)
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Embedding recycling for language models
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Fast inference from transformers via speculative decoding
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Counting carbon: A survey of factors influencing the emissions of machine learning
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From words to watts: Benchmarking the energy costs of large language model inference
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Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. 2023 · 2023
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mlco2/codecarbon: v2.4.1
Benoit Courty, Victor Schmidt, Sasha Luccioni, Goyal-Kamal, MarionCoutarel, Boris Feld, Jérémy Lecourt, LiamConnell, Amine Saboni, Inimaz, supatomic, Mathilde Léval, Luis Blanche, Alexis Cruveiller, ouminasara, Franklin Zhao, Aditya Joshi, Alexis Bogroff, Hugues de Lavoreille, Niko Laskaris, Edoardo Abati, Douglas Blank, Ziyao Wang, Armin Catovic, Marc Alencon, Michał Stęchły, Christian Bauer, Lucas-Otavio, JPW, and MinervaBooks. 2024 · 2024
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Eldar Kurtić, Elias Frantar, and Dan Alistarh. 2024 · 2024
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Baolin Li, Yankai Jiang, Vijay Gadepally, and Devesh Tiwari. 2024 · 2024
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Power hungry processing: Watts driving the cost of ai deployment?
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