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Parameter-efficient fine-tuning techniques like Low-Rank Adaptation (LoRA) have revolutionized the adaptation of large language models (LLMs) to diverse 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
Earlier work this paper cites.
Multitask learning
Rich Caruana. 1997 · 1997
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An overview of multi-task learning in deep neural networks
Sebastian Ruder. 2017 · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017 · 2017
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern. 2018 · 2018
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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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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer. 2022 · 2022
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Branch-train-merge: Embarrassingly parallel training of expert language models
Margaret Li, Suchin Gururangan, Tim Dettmers, Mike Lewis, Tim Althoff, Noah A. Smith, and Luke Zettlemoyer. 2022 · 2022
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Lorahub: Efficient cross-task generalization via dynamic lora composition
Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, and Min Lin. 2023 · 2023
Cited alongside, same era.
Moelora: An moe-based parameter efficient fine-tuning method for multi-task medical applications
Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Derong Xu, Feng Tian, and Yefeng Zheng. 2023 · 2023
Cited alongside, same era.
Ziplora: Any subject in any style by effectively merging loras
Viraj Shah, Nataniel Ruiz, Forrester Cole, Erika Lu, Svetlana Lazebnik, Yuanzhen Li, and Varun Jampani. 2023 · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023 · 2023
Cited alongside, same era.
Mixlora: Enhancing large language models fine-tuning with lora based mixture of experts
Dengchun Li, Yingzi Ma, Naizheng Wang, Zhiyuan Cheng, Lei Duan, Jie Zuo, Cal Yang, and Mingjie Tang. 2024 · 2024
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Multitask multilingual model adaptation with featurized low-rank mixtures
Chu-Cheng Lin, Xinyi Wang, Jonathan H Clark, Han Lu, Yun Zhu, Chenxi Whitehouse, and Hongkun Yu. 2024 · 2024
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Intuition-aware mixture-of-rank-1-experts for parameter efficient finetuning
Yijiang Liu, Rongyu Zhang, Huanrui Yang, Kurt Keutzer, Yuan Du, Li Du, and Shanghang Zhang. 2024 · 2024
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OpenAI and Josh Achiam et al. 2024 · 2024
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Towards modular llms by building and reusing a library of loras
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Ted Zadouri, Ahmet Üstün, Arash Ahmadian, Beyza Ermiş, Acyr Locatelli, and Sara Hooker. 2023 · 2023
Cited alongside, same era.
Sira: Sparse mixture of low rank adaptation
Yun Zhu, Nevan Wichers, Chu-Cheng Lin, Xinyi Wang, Tianlong Chen, Lei Shu, Han Lu, Canoee Liu, Liangchen Luo, Jindong Chen, et al. 2023 · 2023
Cited alongside, same era.
Mixture-of-loras: An efficient multitask tuning for large language models
Wenfeng Feng, Chuzhan Hao, Yuewei Zhang, Yu Han, and Hao Wang. 2024 · 2024
Cited alongside, same era.
Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2024 · 2024
Cited alongside, same era.
Adamix: Mixture-of-adaptations for parameter-efficient model tuning
Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, and Jianfeng Gao. 2022a
Cited in the paper.
Super-naturalinstructions:generalization via declarative instructions on 1600+ tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, et al. 2022b
Cited in the paper.
Oleksiy Ostapenko, Zhan Su, Edoardo Maria Ponti, Laurent Charlin, Nicolas Le Roux, Matheus Pereira, Lucas Caccia, and Alessandro Sordoni. 2024 · 2024
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Branch-train-mix: Mixing expert llms into a mixture-of-experts llm
Sainbayar Sukhbaatar, Olga Golovneva, Vasu Sharma, Hu Xu, Xi Victoria Lin, Baptiste Rozière, Jacob Kahn, Daniel Li, Wen tau Yih, Jason Weston, and Xian Li. 2024 · 2024
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Gemma: Open models based on gemini research and technology
Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al. 2024 · 2024
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Mixture of lora experts
Xun Wu, Shaohan Huang, and Furu Wei. 2024 · 2024
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