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A Single Linear Layer Yields Task-Adapted Low-Rank Matrices
March 25, 2024, 4:42 a.m. | Hwichan Kim, Shota Sasaki, Sho Hoshino, Ukyo Honda
cs.LG updates on arXiv.org arxiv.org
Abstract: Low-Rank Adaptation (LoRA) is a widely used Parameter-Efficient Fine-Tuning (PEFT) method that updates an initial weight matrix $W_0$ with a delta matrix $\Delta W$ consisted by two low-rank matrices $A$ and $B$. A previous study suggested that there is correlation between $W_0$ and $\Delta W$. In this study, we aim to delve deeper into relationships between $W_0$ and low-rank matrices $A$ and $B$ to further comprehend the behavior of LoRA. In particular, we analyze a …
abstract arxiv correlation cs.ai cs.cl cs.lg delta fine-tuning layer linear lora low low-rank adaptation matrix peft study type updates weight matrix
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