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. Based on this, we provide a framework for choosing the optimal fine-tuning techniques given the task type and data availability. Contrary to popular belief, we also empirically prove that PEFT techniques converge slower than full tuning in low data scenarios, and posit the amount of data required for PEFT methods to both perform well and converge efficiently. Lastly, we further optimize these PEFT techniques by selectively choosing which parts of the model to train, and find that these techniques can be applied with significantly fewer parameters while maintaining and even improving performance.

本文主要介绍了大型语言模型的fine-tuning方法——parameter-efficient fine-tuning（PEFT），并通过对FLAN-T5模型的综合测试和分析，提出了选择fine-tuning技术的最佳方法，重点考虑任务类型和数据可用性，同时指出了PEFT方法在数据过少的情况下的收敛速度慢的问题，提出了更好的model optimization方法。

基于实证分析的PEFT技术在LLM中的优势和劣势