Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs

Understand

As foundation models continue to exponentially scale in size, efficient methods of adaptation become increasingly critical.

• Parameter-efficient fine-tuning (PEFT), a recent class of techniques that require only modifying a small percentage of the model parameters, is currently the most popular method for adapting large language models (LLMs).
• Several PEFT techniques have recently been proposed with varying tradeoffs.
• We provide a comprehensive and uniform benchmark of various PEFT techniques across a representative LLM, the FLAN-T5 model, and evaluate model performance across different data scales of classification and generation datasets.