{"id":785137,"date":"2021-10-14T18:24:28","date_gmt":"2021-10-15T01:24:28","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=785137"},"modified":"2022-04-06T19:24:36","modified_gmt":"2022-04-07T02:24:36","slug":"differentially-private-fine-tuning-of-language-models","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/differentially-private-fine-tuning-of-language-models\/","title":{"rendered":"Differentially private fine-tuning of language models"},"content":{"rendered":"

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\u00a087.8<\/span>%<\/span><\/span><\/span><\/span>\u00a0using RoBERTa-Large and\u00a083.5<\/span>%<\/span><\/span><\/span><\/span>\u00a0using RoBERTa-Base with a privacy budget of\u00a0\u03f5<\/span>=<\/span>6.7<\/span><\/span><\/span><\/span>. In comparison, absent privacy constraints, RoBERTa-Large achieves an accuracy of\u00a090.2<\/span>%<\/span><\/span><\/span><\/span>. 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\u00a0\u03f5<\/span>=<\/span>6.8<\/span>,<\/span>\u03b4<\/span>=<\/span><\/span><\/span><\/span>\u00a01e-5) whereas the non-private baseline is\u00a048.1<\/span><\/span><\/span><\/span>. 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.<\/p>\n","protected":false},"excerpt":{"rendered":"

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 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