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The rapid advancements in Large Language Models (LLMs) have revolutionized various natural language processing tasks.
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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Bayesian estimates of equation system parameters: an application of integration by monte carlo
Teun Kloek and Herman K Van Dijk. 1978 · 1978
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020 · 2009
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Stochastic optimization with importance sampling for regularized loss minimization
Peilin Zhao and Tong Zhang. 2015 · 2015
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Freezeout: Accelerate training by progressively freezing layers
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston. 2017 · 2017
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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Warp: Word-level adversarial reprogramming
Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May. 2021 · 2021
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Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. 2021 · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
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Bert busters: Outlier dimensions that disrupt transformers
Olga Kovaleva, Saurabh Kulshreshtha, Anna Rogers, and Anna Rumshisky. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks
Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani, and James Henderson. 2021 · 2021
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Factual probing is [mask]: Learning vs. learning to recall
Zexuan Zhong, Dan Friedman, and Danqi Chen. 2021 · 2021
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Llm. int8 (): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. 2022 · 2022
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Black-box prompt learning for pre-trained language models
Shizhe Diao, Zhichao Huang, Ruijia Xu, Xuechun Li, Yong Lin, Xiao Zhou, and Tong Zhang. 2022 · 2022
Cited alongside, same era.
Outliers dimensions that disrupt transformers are driven by frequency
Giovanni Puccetti, Anna Rogers, Aleksandr Drozd, and Felice Dell’Orletta. 2022 · 2022
Cited alongside, same era.
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. 2023 · 2023
Cited alongside, same era.
Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models
Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, and Roy Ka-Wei Lee. 2023 · 2023
Cited alongside, same era.
Is chatgpt the ultimate programming assistant–how far is it?
Haoye Tian, Weiqi Lu, Tsz On Li, Xunzhu Tang, Shing-Chi Cheung, Jacques Klein, and Tegawendé F Bissyandé. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
Later among the works it cites.
Adaptive budget allocation for parameter-efficient fine-tuning
Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao. 2023 · 2023
Later among the works it cites.
Lora learns less and forgets less
Dan Biderman, Jose Gonzalez Ortiz, Jacob Portes, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, et al. 2024 · 2024
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
Cited alongside, same era.
Is chatgpt a good translator? yes with gpt-4 as the engine
Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, Shuming Shi, and Zhaopeng Tu. 2023 · 2023
Cited alongside, same era.
Chatgpt: Jack of all trades, master of none
Jan Kocoń, Igor Cichecki, Oliwier Kaszyca, Mateusz Kochanek, Dominika Szydło, Joanna Baran, Julita Bielaniewicz, Marcin Gruza, Arkadiusz Janz, Kamil Kanclerz, et al. 2023 · 2023
Cited alongside, same era.
Vera: Vector-based random matrix adaptation
Dawid Jan Kopiczko, Tijmen Blankevoort, and Yuki Markus Asano. 2023 · 2023
Cited alongside, same era.
Relora: High-rank training through low-rank updates
Vladislav Lialin, Sherin Muckatira, Namrata Shivagunde, and Anna Rumshisky. 2023a · 2023
Cited alongside, same era.
Fine-tuning language models with just forward passes
Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D Lee, Danqi Chen, and Sanjeev Arora. 2023 · 2023
Cited alongside, same era.
Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. 2023 · 2023
Cited alongside, same era.
Tied-lora: Enhacing parameter efficiency of lora with weight tying
Adithya Renduchintala, Tugrul Konuk, and Oleksii Kuchaiev. 2023 · 2023
Cited alongside, same era.
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2024 · 2024
Closest in time.
Smartfrz: An efficient training framework using attention-based layer freezing
Sheng Li, Geng Yuan, Yue Dai, Youtao Zhang, Yanzhi Wang, and Xulong Tang. 2024 · 2024
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Alphapruning: Using heavy-tailed self regularization theory for improved layer-wise pruning of large language models
Haiquan Lu, Yefan Zhou, Shiwei Liu, Zhangyang Wang, Michael W Mahoney, and Yaoqing Yang. 2024 · 2024
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Badam: A memory efficient full parameter training method for large language models
Qijun Luo, Hengxu Yu, and Xiao Li. 2024 · 2024
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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 · 2024
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Lisa: Layerwise importance sampling for memory-efficient large language model fine-tuning
Rui Pan, Xiang Liu, Shizhe Diao, Renjie Pi, Jipeng Zhang, Chi Han, and Tong Zhang. 2024 · 2024
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Chain of lora: Efficient fine-tuning of language models via residual learning
Wenhan Xia, Chengwei Qin, and Elad Hazan. 2024 · 2024
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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. 2024 · 2024
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Galore: Memory-efficient llm training by gradient low-rank projection
Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, and Yuandong Tian. 2024 · 2024
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2024 · 2024
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