BriefGPT.xyz
Jan, 2024
大型语言模型的私人微调与零阶优化
Private Fine-tuning of Large Language Models with Zeroth-order Optimization
HTML
PDF
Xinyu Tang, Ashwinee Panda, Milad Nasr, Saeed Mahloujifar, Prateek Mittal
TL;DR
DP-ZO是一种维护训练数据隐私的方法,通过对零阶优化中步长的隐私化来对大型语言模型进行微调,可在保守的隐私预算下提供强大的隐私-效用权衡,且在SQuAD的1000个训练样本上,对OPT-66B的微调仅导致1.86%的性能降低。
Abstract
fine-tuning
large pretrained models on private datasets may run the risk of violating
privacy
. Differential
privacy
is a framework for mit
→