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Full-parameter fine-tuning has become the go-to choice for adapting language models (LMs) to downstream tasks due to its excellent performance.
Uniform convergence may be unable to explain generalization in deep learning
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Roberta: A robustly optimized BERT pretraining approach
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Megatron-lm: Training multi-billion parameter language models using model parallelism
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
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Greedy layer-wise training of deep networks
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Adaptive subgradient methods for online learning and stochastic optimization
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Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S Gordon. 2011 · 2011
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The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern. 2012 · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
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Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Semeval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
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The e2e dataset: New challenges for end-to-end generation
Jekaterina Novikova, Ondřej Dušek, and Verena Rieser. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018 · 2018
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Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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Mesh-tensorflow: Deep learning for supercomputers
Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, and Blake A. Hechtman. 2018 · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern. 2018 · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman. 2018 · 2018
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Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al. 2018 · 2018
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Record: Bridging the gap between human and machine commonsense reading comprehension
Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme. 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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The commitmentbank: Investigating projection in naturally occurring discourse
Marie-Catherine De Marneffe, Mandy Simons, and Judith Tonhauser. 2019 · 2019
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Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Staged training for transformer language models
Sheng Shen, Pete Walsh, Kurt Keutzer, Jesse Dodge, Matthew E. Peters, and Iz Beltagy. 2022 · 2022
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Efficient fine-tuning of bert models on the edge
Danilo Vucetic, Mohammadreza Tayaranian, Maryam Ziaeefard, James J Clark, Brett H Meyer, and Warren J Gross. 2022 · 2022
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben Zaken, Yoav Goldberg, and Shauli Ravfogel. 2022 · 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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Fine-Tuning LLMs: LoRA or Full-Parameter? An in-depth Analysis with Llama-2
Niederfahrenhorst Artur, Hakhamaneshi Kourosh, and Ahmad Rehaan. 2023 · 2023
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Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
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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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Viggo: A video game corpus for data-to-text generation in open-domain conversation
Juraj Juraska, Kevin Bowden, and Marilyn Walker. 2019 · 2019
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Wic: the word-in-context dataset for evaluating context-sensitive meaning representations
Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R Bowman. 2019 · 2019
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Exploring versatile generative language model via parameter-efficient transfer learning
Zhaojiang Lin, Andrea Madotto, and Pascale Fung. 2020 · 2020
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Multilingual denoising pre-training for neural machine translation
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer. 2020 · 2020
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sql-create-context dataset
b mc2. 2023 · 2023
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Parameter-efficient fine-tuning design spaces
Jiaao Chen, Aston Zhang, Xingjian Shi, Mu Li, Alex Smola, and Diyi Yang. 2023 · 2023
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Bpipe: Memory-balanced pipeline parallelism for training large language models
Taebum Kim, Hyoungjoo Kim, Gyeong-In Yu, and Byung-Gon Chun. 2023 · 2023
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Fine-Tuning Llama-2: A Comprehensive Case Study for Tailoring Models to Unique Applications
Hakhamaneshi Kourosh and Ahmad Rehaan. 2023 · 2023
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Scaling down to scale up: A guide to parameter-efficient fine-tuning
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Full parameter fine-tuning for large language models with limited resources
Kai Lv, Yuqing Yang, Tengxiao Liu, Qinghui Gao, Qipeng Guo, and Xipeng Qiu. 2023 · 2023
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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
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Task-specific skill localization in fine-tuned language models
Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao, and Sanjeev Arora. 2023 · 2023
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Finetuning LLMs with LoRA and QLoRA: Insights from Hundreds of Experiments
Sebastian Raschka. 2023 · 2023
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Xianghui Sun, Yunjie Ji, Baochang Ma, and Xiangang Li. 2023 · 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 · 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, 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, Aurélien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
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YUAN 2.0: A large language model with localized filtering-based attention
Shaohua Wu, Xudong Zhao, Shenling Wang, Jiangang Luo, Lingjun Li, Xi Chen, Bing Zhao, Wei Wang, Tong Yu, Rongguo Zhang, Jiahua Zhang, and Chao Wang. 2023 · 2023
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Mpipemoe: Memory efficient moe for pre-trained models with adaptive pipeline parallelism
Zheng Zhang, Donglin Yang, Yaqi Xia, Liang Ding, Dacheng Tao, Xiaobo Zhou, and Dazhao Cheng · 2023
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Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2023
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Parameter-efficient fine-tuning with discrete fourier transform
Ziqi Gao, Qichao Wang, Aochuan Chen, Zijing Liu, Bingzhe Wu, Liang Chen, and Jia Li. 2024 · 2024
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