GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs

• URL: http://arxiv.org/abs/2408.15300v1
• Date: Tue, 27 Aug 2024 14:41:14 GMT
• Title: GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs
• Authors: Maxim Zhelnin, Viktor Moskvoretskii, Egor Shvetsov, Egor Venediktov, Mariya Krylova, Aleksandr Zuev, Evgeny Burnaev,
• Abstract summary: We introduce a novel PEFT method, called Gaussian noise Injected Fine Tuning of Salient Weights (GIFT-SW) Our method updates only salient columns, while injecting Gaussian noise into non-salient ones. Experiments with LLaMA models demonstrate that GIFT-SW outperforms full fine-tuning and modern PEFT methods under the same computational budget.
• Score: 51.02233412547456
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Parameter Efficient Fine-Tuning (PEFT) methods have gained popularity and democratized the usage of Large Language Models (LLMs). Recent studies have shown that a small subset of weights significantly impacts performance. Based on this observation, we introduce a novel PEFT method, called Gaussian noise Injected Fine Tuning of Salient Weights (GIFT-SW). Our method updates only salient columns, while injecting Gaussian noise into non-salient ones. To identify these columns, we developeda generalized sensitivity metric that extends and unifies metrics from previous studies. Experiments with LLaMA models demonstrate that GIFT-SW outperforms full fine-tuning and modern PEFT methods under the same computational budget. Moreover, GIFT-SW offers practical advantages to recover performance of models subjected to mixed-precision quantization with keeping salient weights in full precision.

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