  Large, general purpose language models have demonstrated impressive
performance across many different conversational domains. While multi-domain
language models achieve low overall perplexity, their outputs are not
guaranteed to stay within the domain of a given input prompt. This paper
proposes domain privacy as a novel way to quantify how likely a conditional
language model will leak across domains. We also develop policy functions based
on token-level domain classification, and propose an efficient fine-tuning
method to improve the trained model's domain privacy. Experiments on membership
inference attacks show that our proposed method has comparable resiliency to
methods adapted from recent literature on differentially private language
models.
