lora-low-rank-adaptation-of-large-language-models-2026-012

Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen

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lora-low-rank-adaptation-of-large-language-models-2026-012

Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu ChenUFTAN UNIVERSITYComputer Science2026

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This record links to the LoRA paper, which introduces low-rank adaptation for efficient fine-tuning of large language models. It is useful for testing AI thesis metadata, modern NLP keywords, and external PDF access.

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Author: Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen
Supervisor: Dr. Chiamaka R. Obi
Faculty: Science
Department: Computer Science
Document type: Masters Thesis
Year: 2026
Keywords: LoRA, parameter efficient tuning, large language models, low rank adaptation, NLP
Access: Metadata
License: all_rights_reserved

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Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen (2026). lora-low-rank-adaptation-of-large-language-models-2026-012 [Masters Thesis]. UFTAN UNIVERSITY. https://inrepscholar.com/uftan-university/projects/951

Source fields used: Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen

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