Abstract:Recent papers have shown that large pre-trained language models (LLMs) such as BERT, GPT-2 can be fine-tuned on private data to achieve performance comparable to non-private models for many downstream Natural Language Processing (NLP) tasks while simultaneously guaranteeing differential privacy. The inference cost of these models -which consist of hundreds of millions of parameters -however, can be prohibitively large. Hence, often in practice, LLMs are compressed before they are deployed in specific applicati… Show more
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