SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning
Zhihao Wen Jie Zhang Yuan Fang
Fine-tuning all parameters of large language models (LLMs) necessitates
substantial computational power and extended time. Latest advancements in
parameter-efficient fine-tuning (PEFT) techniques, such as Adapter tuning and
LoRA, allow for adjustments to only a minor fraction of the parameters of these
LLMs. Concurrently, it has been noted that the issue of over-smoothing
diminishes the effectiveness of these Transformer-based LLMs, resulting in
suboptimal performances in downstream tasks. In this paper, we present SIBO,
which is a SImple BOoster to enhance PEFT, by injecting an initial residual.
SIBO is straight-forward and readily extensible to a range of state-of-the-art
PEFT techniques to alleviate over-smoothing and enhance performance. Extensive
experiments on 22 benchmark datasets demonstrate that SIBO significantly
enhances the performance of various strong baselines, achieving up to 15.7% and
23.5% improvement over existing PEFT methods on the arithmetic and commonsense
reasoning tasks, respectively.