Randomized Masked Finetuning: An Efficient Way to Mitigate Memorization of PIIs in LLMs

This article likely discusses a novel finetuning technique to address the problem of Large Language Models (LLMs) memorizing and potentially leaking Personally Identifiable Information (PIIs). The method, "Randomized Masked Finetuning," suggests a strategy to prevent the model from directly memorizing sensitive data during training. The efficiency claim implies the method is computationally less expensive than other mitigation techniques.

• •Focuses on mitigating PII memorization in LLMs.
• •Proposes a new finetuning technique: Randomized Masked Finetuning.
• •Claims the method is efficient.