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Privately Fine-Tuning Large Language Models with Differential Privacy
Differential privacy (DP) provides a rigorous framework that allows adding noise in the process of training or fine-tuning LLMs such that extracting the training data becomes infeasible (i. e., with a cryptographically small success probability).
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