PEFT Review (JUST FOR TEST)

Published: February 2025

Introduction

Parameter-Efficient Fine-Tuning (PEFT) is a technique used to adapt large pre-trained models efficiently with minimal additional parameters. This review explores current research, challenges, and potential directions.

Key Approaches

• LoRA (Low-Rank Adaptation): Injects trainable low-rank matrices into frozen layers, reducing memory footprint.
• Adapters: Small task-specific layers added between existing transformer layers.
• Prefix-Tuning: Learns continuous prompts that modify transformer activations without altering model weights.
• BitFit: Fine-tunes only bias parameters, significantly reducing computational cost.

Challenges

• Trade-off between efficiency and expressiveness.
• Retention of pre-trained knowledge vs. task-specific adaptation.
• Evaluation metrics—how to measure adaptation performance effectively?

Future Directions

Several promising research directions include:

• Exploring hybrid PEFT methods that combine LoRA and Prefix-Tuning.
• Developing better benchmarks for real-world efficiency gains.
• Investigating PEFT's role in continual learning and domain adaptation.