Fine-tuning Happens in Tiny Subspaces: Exploring Intrinsic Task-specific Subspaces of Pre-trained Language Models
Zhong Zhang, Bang Liu, Junming Shao · 2023
Pre-trained language models (PLMs) are known to be overly parameterized and have significant redundancy, indicating a small degree of freedom of the PLMs.Motivated by the observation, in this paper, we study the problem of re-parameterizing and fine-tuning PLMs from a new perspective: Discovery of intrinsic task-specific subspace.Specifically, by exploiting the dynamics of the fine-tuning process for a given task, the parameter optimization trajectory is learned to uncover its intrinsic task-specific subspace.A key finding is that PLMs can be effectively fine-tuned in the subspace with a small number of free parameters.Beyond, we observe some outlier dimensions emerging during fine-tuning in the subspace.Disabling these dimensions degrades the model performance significantly.This suggests that these dimensions are crucial to induce task-specific knowledge to downstream tasks.