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

Maxim Zhelnin, Viktor Moskvoretskii, Egor Shvetsov, Venediktov Egor, Maria Krylova, Zuev Aleksandr, Evgeny Burnaev

Keywords: large language models, parameter efficient fine-tuning, Gaussian noise, quantization

TL;DR: Fine-tuning weights in salient columns produces results comparable to those obtained using low-rank adapters

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 developed a 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.

Primary Area: optimization

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Submission Number: 4309