<?xml version="1.0" encoding="UTF-8"?><resource xmlns="http://datacite.org/schema/kernel-4"><identifier identifierType="URL">https://inrepscholar.com/projects/951</identifier><creators><creator><creatorName>Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen</creatorName></creator></creators><titles><title>lora-low-rank-adaptation-of-large-language-models-2026-012</title></titles><publisher>UFTAN UNIVERSITY</publisher><publicationYear>2026</publicationYear><resourceType resourceTypeGeneral="Text">Masters Thesis</resourceType><descriptions><description descriptionType="Abstract">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.</description></descriptions></resource>