  Language modeling is a keystone task in natural language processing. When
training a language model on sensitive information, differential privacy (DP)
allows us to quantify the degree to which our private data is protected.
However, training algorithms which enforce differential privacy often lead to
degradation in model quality. We study the feasibility of learning a language
model which is simultaneously high-quality and privacy preserving by tuning a
public base model on a private corpus. We find that DP fine-tuning boosts the
performance of language models in the private domain, making the training of
such models possible.
