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Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications

Charith Chandra Sai Balne, Sreyoshi Bhaduri, Tamoghna Roy, Vinija Jain, Aman Chadha

TL;DR

The paper addresses the computational and memory bottlenecks of full fine-tuning for large pre-trained models by surveying Parameter Efficient Fine-Tuning (PEFT) approaches across domains. It analyzes core strategies—including low-rank updates, adapters, and novel methods like LoReFT and DoRA—comparing their efficiency and performance. The review highlights that substantial parameter-efficiency can be achieved with minimal performance loss across NLP, video, medical imaging, protein modeling, code tasks, 3D models, and speech, showcasing broad practical impact. By mapping method trade-offs and future directions, the work guides practitioners toward scalable, privacy-preserving, and interpretable PEFT deployments across diverse applications.

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.

Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications

TL;DR

The paper addresses the computational and memory bottlenecks of full fine-tuning for large pre-trained models by surveying Parameter Efficient Fine-Tuning (PEFT) approaches across domains. It analyzes core strategies—including low-rank updates, adapters, and novel methods like LoReFT and DoRA—comparing their efficiency and performance. The review highlights that substantial parameter-efficiency can be achieved with minimal performance loss across NLP, video, medical imaging, protein modeling, code tasks, 3D models, and speech, showcasing broad practical impact. By mapping method trade-offs and future directions, the work guides practitioners toward scalable, privacy-preserving, and interpretable PEFT deployments across diverse applications.

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.
Paper Structure (12 sections, 2 figures, 3 tables)

This paper contains 12 sections, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Fine-tuning Methods
  3. Applications
  4. Commonsense and Arithmetic Reasoning
  5. Video Text Generation
  6. Medical Imaging
  7. Protein Models
  8. Code Review / Generation
  9. 3D Pretrained Models
  10. Speech Synthesis
  11. Considerations for Evaluation Across PEFT Methods
  12. Discussions

Figures (2)

  • Figure 1: Comparative study of PEFT across different applications.
  • Figure 2: Illustration of workflow for the PEFT paradigm starting with a pre-trained model ($\theta$), to which modifications such as additions, specifications, and reparameterizations are applied, effectively differentiating between frozen and tunable parameters to enhance model performance.