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This article presents a novel framework for training large language models in a more efficient and scalable manner, using a technique called 'LoRA' (Low-Rank Adaptation). The authors demonstrate significant performance improvements on a range of natural language processing tasks while requiring only a small fraction of the original model's parameters to be updated.