Parameter-Efficient Fine-Tuning With Adapters

TL;DR

This paper presents a parameter-efficient fine-tuning method using adapters and a PromptTuning Layer, achieving competitive performance with fewer trainable parameters across multiple NLP benchmarks.

Contribution

Introduces a novel adapter-based fine-tuning approach with a PromptTuning Layer that reduces computational costs while maintaining high performance.

Findings

Achieves comparable results to full fine-tuning on benchmarks

Reduces the number of trainable parameters significantly

Speeds up the adaptation process for new tasks

Abstract

In the arena of language model fine-tuning, the traditional approaches, such as Domain-Adaptive Pretraining (DAPT) and Task-Adaptive Pretraining (TAPT), although effective, but computational intensive. This research introduces a novel adaptation method utilizing the UniPELT framework as a base and added a PromptTuning Layer, which significantly reduces the number of trainable parameters while maintaining competitive performance across various benchmarks. Our method employs adapters, which enable efficient transfer of pretrained models to new tasks with minimal retraining of the base model parameters. We evaluate our approach using three diverse datasets: the GLUE benchmark, a domain-specific dataset comprising four distinct areas, and the Stanford Question Answering Dataset 1.1 (SQuAD). Our results demonstrate that our customized adapter-based method achieves performance comparable to full model fine-tuning, DAPT+TAPT and UniPELT strategies while requiring fewer or equivalent amount of parameters. This parameter efficiency not only alleviates the computational burden but also expedites the adaptation process. The study underlines the potential of adapters in achieving high performance with significantly reduced resource consumption, suggesting a promising direction for future research in parameter-efficient fine-tuning.