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Fine-tuning Large Language Models (LLMs) and storing them for each downstream task or domain is impractical because of the massive model size (e.g., 350GB in GPT-3).
Parameter-Efficient Transfer Learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 1902
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
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Dynamic model pruning with feedback
Tao Lin, Sebastian U Stich, Luis Barba, Daniil Dmitriev, and Martin Jaggi · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Cifar-10 and cifar-100 datasets
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
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Caltech-ucsd birds 200
Peter Welinder, Steve Branson, Takeshi Mita, Catherine Wah, Florian Schroff, Serge Belongie, and Pietro Perona · 2010
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A. Ehinger, Aude Oliva, and Antonio Torralba · 2010
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Cats and dogs
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation, 2013
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew B. Blaschko, and Andrea Vedaldi · 2013
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Food-101 – mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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The cifar-10 dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
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Compressing neural networks with the hashing trick
Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, and Yixin Chen · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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The webnlg challenge: Generating text from rdf data
Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini · 2017
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The e2e dataset: New challenges for end-to-end generation
Jekaterina Novikova, Ondřej Dušek, and Verena Rieser · 2017
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Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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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 Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
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Measuring the intrinsic dimension of objective landscapes
Chunyuan Li, Heerad Farkhoor, Rosanne Liu, and Jason Yosinski · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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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 image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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WARP: Word-level Adversarial ReProgramming
Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May · 2020
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Robust pruning at initialization
Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, and Yee Whye Teh · 2020
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Scaling laws for neural language models, 2020
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang · 2021
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Compacter: Efficient low-rank hypercomplex adapter layers, 2021
Rabeeh Karimi Mahabadi, James Henderson, and Sebastian Ruder · 2021
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Adapterfusion: Non-destructive task composition for transfer learning, 2021
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, and Iryna Gurevych · 2021
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Dynamic pruning of a neural network via gradient signal-to-noise ratio
Julien Niklas Siems, Aaron Klein, Cedric Archambeau, and Maren Mahsereci · 2021
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Chipnet: Budget-aware pruning with heaviside continuous approximations
Rishabh Tiwari, Udbhav Bamba, Arnav Chavan, and Deepak K Gupta · 2021
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Neuron merging: Compensating for pruned neurons
Woojeong Kim, Suhyun Kim, Mincheol Park, and Geunseok Jeon · 2020
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Soft threshold weight reparameterization for learnable sparsity
Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham Kakade, and Ali Farhadi · 2020
Cited alongside, same era.
Exploring versatile generative language model via parameter-efficient transfer learning
Zhaojiang Lin, Andrea Madotto, and Pascale Fung · 2020
Cited alongside, same era.
Dart: Open-domain structured data record to text generation
Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, et al · 2020
Cited alongside, same era.
Adapterdrop: On the efficiency of adapters in transformers, 2020
Andreas Rücklé, Gregor Geigle, Max Glockner, Tilman Beck, Jonas Pfeiffer, Nils Reimers, and Iryna Gurevych · 2020
Cited alongside, same era.
Neural pruning via growing regularization
Huan Wang, Can Qin, Yulun Zhang, and Yun Fu · 2020
Cited alongside, same era.
Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Armen Aghajanyan, Sonal Gupta, and Luke Zettlemoyer · 2021
Cited alongside, same era.
Training data-efficient image transformers and distillation through attention, 2021
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Herve Jegou · 2021
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben Zaken, Shauli Ravfogel, and Yoav Goldberg · 2021
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Adaptformer: Adapting vision transformers for scalable visual recognition
Shoufa Chen, Chongjian Ge, Zhan Tong, Jiangliu Wang, Yibing Song, Jue Wang, and Ping Luo · 2022
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An information-theoretic justification for model pruning
Berivan Isik, Tsachy Weissman, and Albert No · 2022
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Se Jung Kwon, Jeonghoon Kim, Jeongin Bae, Kang Min Yoo, Jin-Hwa Kim, Baeseong Park, Byeongwook Kim, Jung-Woo Ha, Nako Sung, and Dongsoo Lee · 2022
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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
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Pranc: Pseudo random networks for compacting deep models
Parsa Nooralinejad, Ali Abbasi, Soroush Abbasi Koohpayegani, Kossar Pourahmadi Meibodi, Rana Muhammad Shahroz Khan, Soheil Kolouri, and Hamed Pirsiavash · 2022
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Neural prompt search, 2022
Yuanhan Zhang, Kaiyang Zhou, and Ziwei Liu · 2022
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Qlora: Efficient finetuning of quantized llms, 2023
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2023
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Prequant: A task-agnostic quantization approach for pre-trained language models, 2023
Zhuocheng Gong, Jiahao Liu, Qifan Wang, Yang Yang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, and Rui Yan · 2023
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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Llama 2: Open foundation and fine-tuned chat models, 2023
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom · 2023
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Qa-lora: Quantization-aware low-rank adaptation of large language models, 2023
Yuhui Xu, Lingxi Xie, Xiaotao Gu, Xin Chen, Heng Chang, Hengheng Zhang, Zhensu Chen, Xiaopeng Zhang, and Qi Tian · 2023
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Adaptive budget allocation for parameter-efficient fine-tuning, 2023
Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao · 2023
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