Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications

TL;DR

This paper provides a comprehensive analysis of Parameter Efficient Fine-Tuning (PEFT) techniques, comparing various strategies across multiple domains to highlight their benefits in reducing computational costs while maintaining performance.

Contribution

It offers a detailed comparison of PEFT methods, showcasing their effectiveness and applications across diverse fields, and guides future research in model optimization.

Findings

PEFT reduces computational load and memory usage.

PEFT accelerates training times.

PEFT maintains competitive performance levels.

Abstract

The rise of deep learning has marked significant progress in fields such as computer vision, natural language processing, and medical imaging, primarily through the adaptation of pre-trained models for specific tasks. Traditional fine-tuning methods, involving adjustments to all parameters, face challenges due to high computational and memory demands. This has led to the development of Parameter Efficient Fine-Tuning (PEFT) techniques, which selectively update parameters to balance computational efficiency with performance. This review examines PEFT approaches, offering a detailed comparison of various strategies highlighting applications across different domains, including text generation, medical imaging, protein modeling, and speech synthesis. By assessing the effectiveness of PEFT methods in reducing computational load, speeding up training, and lowering memory usage, this paper contributes to making deep learning more accessible and adaptable, facilitating its wider application and encouraging innovation in model optimization. Ultimately, the paper aims to contribute towards insights into PEFT's evolving landscape, guiding researchers and practitioners in overcoming the limitations of conventional fine-tuning approaches.