June 5, 2024, 4:51 a.m. | Kerim B\"uy\"ukaky\"uz

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.01775v1 Announce Type: new
Abstract: The advent of large language models (LLMs) has revolutionized natural language processing, enabling unprecedented capabilities in understanding and generating human-like text. However, the computational cost and convergence times associated with fine-tuning these models remain significant challenges. Low-Rank Adaptation (LoRA) has emerged as a promising method to mitigate these issues by introducing efficient fine-tuning techniques with a reduced number of trainable parameters. In this paper, we present OLoRA, an enhancement to the LoRA method that leverages …

abstract arxiv capabilities challenges computational convergence cost cs.cl enabling fine-tuning however human human-like language language models language processing large language large language models llms lora low low-rank adaptation natural natural language natural language processing processing text type understanding

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