March 14, 2024, 4:48 a.m. | Ming Dong, Kang Xue, Bolong Zheng, Tingting He

cs.CL updates on

arXiv:2403.08484v1 Announce Type: new
Abstract: In view of the huge number of parameters of Large language models (LLMs) , tuning all parameters is very costly, and accordingly fine-tuning specific parameters is more sensible. Most of parameter efficient fine-tuning (PEFT) concentrate on parameter selection strategies, such as additive method, selective method and reparametrization-based method. However, there are few methods that consider the impact of data samples on parameter selecting, such as Fish Mask based method. Fish Mask randomly choose a part …

abstract arxiv data dynamic fine-tuning fish language language models large language large language models llms parameters peft strategies strategy type view

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