Parameter Efficient Fine-Tuning: A Survey

on Nlp, Survey, Fine-tuning, Peft, Lora, Adapters, Efficiency

Introduction

This post reviews the survey on Parameter Efficient Fine-Tuning (PEFT) methods, which enable adaptation of large pre-trained models with minimal computational resources and parameter updates.

Key Topics Covered

  • Introduction to Parameter Efficient Fine-Tuning
  • LoRA (Low-Rank Adaptation)
  • Adapter Methods
  • Prefix Tuning and P-Tuning
  • BitFit and Other Sparse Methods
  • Comparison of PEFT Techniques
  • Performance vs Efficiency Trade-offs
  • Applications and Use Cases
  • Future Research Directions

Summary

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References

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