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
datasets, the utility of private models approaches that of non-private models.
For example, on the MNLI dataset we achieve an accuracy of $87.8%$ using
RoBERTa-Large and $83.5%$ using RoBERTa-Base with a privacy budget of
$epsilon = 6.7$. In comparison, absent privacy constraints, RoBERTa-Large
achieves an accuracy of $90.2%$. Our findings are similar for natural language
generation tasks. Privately fine-tuning with DART, GPT-2-Small, GPT-2-Medium,
GPT-2-Large, and GPT-2-XL achieve BLEU scores of 38.5, 42.0, 43.1, and 43.8
respectively (privacy budget of $epsilon = 6.8,delta=$ 1e-5) whereas the
non-private baseline is $48.1$. All our experiments suggest that larger models
are better suited for private fine-tuning: while they are well known to achieve
superior accuracy non-privately, we find that they also better maintain their
accuracy when privacy is introduced.

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