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We propose RoCoFT, a parameter-efficient fine-tuning method for large-scale language models (LMs) based on updating only a few rows and columns of the weight matrices in transformers.
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Attention is all you need
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018 · 2018
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Parameter-efficient transfer learning for NLP
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Ali Edalati, Marzieh Tahaei, Ivan Kobyzev, Vahid Partovi Nia, James J Clark, and Mehdi Rezagholizadeh. 2022 · 2022
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Training compute-optimal large language models
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Crosslingual generalization through multitask finetuning
Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M Saiful Bari, Sheng Shen, Zheng-Xin Yong, Hailey Schoelkopf, et al. 2022 · 2022
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More than a toy: Random matrix models predict how real-world neural representations generalize
Alexander Wei, Wei Hu, and Jacob Steinhardt. 2022 · 2022
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Wide neural networks of any depth evolve as linear models under gradient descent
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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
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Training verifiers to solve math word problems, 2021
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021
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Parameter-efficient transfer learning with Diff Pruning
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Measuring mathematical problem solving with the math dataset
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
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VeRA: Vector-based random matrix adaptation
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BlOOM: A 176B-parameter open-access multilingual language model
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A kernel-based view of language model fine-tuning
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A simple and effective pruning approach for large language models
Mingjie Sun, Zhuang Liu, Anna Bair, and J Zico Kolter. 2023 · 2023
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Llama: Open and efficient foundation language models
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Lingling Xu, Haoran Xie, Si-Zhao Joe Qin, Xiaohui Tao, and Fu Lee Wang. 2023 · 2023
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One network, many masks: Towards more parameter-efficient transfer learning
Guangtao Zeng, Peiyuan Zhang, and Wei Lu. 2023 · 2023
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Tuning layernorm in attention: Towards efficient multi-modal LLM finetuning
Bingchen Zhao, Haoqin Tu, Chen Wei, Jieru Mei, and Cihang Xie. 2023 · 2023
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Scaling sparse fine-tuning to large language models
Alan Ansell, Ivan Vulić, Hannah Sterz, Anna Korhonen, and Edoardo M Ponti. 2024 · 2024
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Lora-xs: Low-rank adaptation with extremely small number of parameters
Klaudia Bałazy, Mohammadreza Banaei, Karl Aberer, and Jacek Tabor. 2024 · 2024
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QLoRA: Efficient finetuning of quantized LLMs
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The impact of initialization on LoRA finetuning dynamics
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Sparse matrix in large language model fine-tuning
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A survey on large language models for code generation
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Propulsion: Steering LLM with tiny fine-tuning
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Mixture-of-subspaces in low-rank adaptation
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