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Collaborative learning of large language models (LLMs) has emerged as a new paradigm for utilizing private data from different parties to guarantee efficiency and privacy.
The lifo/fifo decision
Dale Morse and Gordon Richardson · 1983
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Classical groups and geometric algebra , volume 39
Larry C Grove · 2002
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Lie groups, Lie algebras, and representations
Brian C Hall and Brian C Hall · 2013
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Communication-efficient learning of deep networks from decentralized data
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
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A field guide to federated optimization
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Locating and editing factual associations in gpt
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Introduction to linear algebra
Gilbert Strang · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
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Social learning: Towards collaborative learning with large language models
Amirkeivan Mohtashami, Florian Hartmann, Sian Gooding, Lukas Zilka, Matt Sharifi, et al · 2023
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Task arithmetic in the tangent space: Improved editing of pre-trained models
Guillermo Ortiz-Jimenez, Alessandro Favero, and Pascal Frossard · 2023
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Reasoning with language model prompting: A survey
Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, and Huajun Chen · 2023
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On knowledge editing in federated learning: Perspectives, challenges, and future directions
Leijie Wu, Song Guo, Junxiao Wang, Zicong Hong, Jie Zhang, and Jingren Zhou · 2023
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Ties-merging: Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal · 2023
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Federated unlearning: Guarantee the right of clients to forget
Leijie Wu, Song Guo, Junxiao Wang, Zicong Hong, Jie Zhang, and Yaohong Ding · 2022
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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
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Dune: Dataset for unified editing
Afra Akyürek, Eric Pan, Garry Kuwanto, and Derry Wijaya · 2023
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Adaptersoup: Weight averaging to improve generalization of pretrained language models
Alexandra Chronopoulou, Matthew E Peters, Alexander Fraser, and Jesse Dodge · 2023
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 2023
Cited alongside, same era.
Knowledge unlearning for mitigating privacy risks in language models
Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo · 2023
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Mass-editing memory in a transformer
Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau · 2023
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Editing large language models
Ningyu Zhang, Yunzhi Yao, and Shumin Deng · 2023
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Performance analysis of fcfs and improved fcfs scheduling algorithms for dynamic real-time computer systems
W. Zhao and J.A. Stankovic · 2023
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Collaborative learning via prediction consensus
Dongyang Fan, Celestine Mendler-Dünner, and Martin Jaggi · 2024
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Model editing at scale leads to gradual and catastrophic forgetting
Akshat Gupta, Anurag Rao, and Gopala Anumanchipalli · 2024
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Unveiling the pitfalls of knowledge editing for large language models
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Massive editing for large language models via meta learning
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Openfedllm: Training large language models on decentralized private data via federated learning
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