Differentially Private Fine-tuning of Language Models. (arXiv:2110.06500v2 [cs.LG] UPDATED)

We give simpler, sparser, and faster algorithms for differentially private
fine-tuning of large-scale pre-trained language models, which achieve the
state-of-the-art privacy versus utility tradeoffs on many standard NLP tasks.
We propose a meta-framework for this problem, inspired by the recent success of
highly parameter-efficient methods for fine-tuning. Our experiments show that
differentially private adaptations of these approaches outperform previous
private algorithms in three important dimensions: utility, privacy, and the
computational and memory cost of private training. On many commonly studied …

language lg