Sparse is Enough in Fine-tuning Pre-trained Large Language Model
Weixi Song Zuchao Li Lefei Zhang Hai Zhao Bo Du
With the prevalence of pre-training-fine-tuning paradigm, how to efficiently
adapt the pre-trained model to the downstream tasks has been an intriguing
issue. Parameter-Efficient Fine-Tuning (PEFT) methods have been proposed for
low-cost adaptation, including Adapters, Bia-only, and the recently widely used
Low-Rank Adaptation. Although these methods have demonstrated their
effectiveness to some extent and have been widely applied, the underlying
principles are still unclear. In this paper, we reveal the transition of loss
landscape in the downstream domain from random initialization to pre-trained
initialization, that is, from low-amplitude oscillation to high-amplitude
oscillation. The parameter gradients exhibit a property akin to sparsity, where
a small fraction of components dominate the total gradient norm, for instance,
1% of the components account for 99% of the gradient. This property ensures
that the pre-trained model can easily find a flat minimizer which guarantees
the model's ability to generalize even with a low number of trainable
parameters. Based on this, we propose a gradient-based sparse fine-tuning
algorithm, named Sparse Increment Fine-Tuning (SIFT), and validate its
effectiveness on a range of tasks including the GLUE Benchmark and
Instruction-tuning. The code is accessible at https://github.com/song-wx/SIFT/.