Large language models (LLMs) exhibit remarkable capabilities in understanding and generating natural language. However, these models can inadvertently memorize private information, posing significant privacy risks. This study addresses the challenge of enabling LLMs to protect specific individuals' private data without the need for complete retraining. We propose \return, a Real-world pErsonal daTa UnleaRNing dataset, comprising 2,492 individuals from Wikipedia with associated QA pairs, to evaluate machine unlearning (MU) methods for protecting personal data in a realistic scenario. Additionally, we introduce the Name-Aware Unlearning Framework (NAUF) for Privacy Protection, which enables the model to learn which individuals' information should be protected without affecting its ability to answer questions related to other unrelated individuals. Our extensive experiments demonstrate that NAUF achieves a state-of-the-art average unlearning score, surpassing the best baseline method by 5.65 points, effectively protecting target individuals' personal data while maintaining the model's general capabilities.

该研究提出了一种 Name-Aware Unlearning Framework (NAUF) 隐私保护方法，旨在通过实现机器遗忘学习 (MU) 技术来保护特定个体的个人数据，同时兼顾模型对其他非关联个体的问题回答能力，并通过大量实验证明，NAUF 方法在平均遗忘率方面表现出色，超越最佳基准方法 5.65 分，有效地保护目标个体的个人数据同时维持模型的普适性能。

学会拒绝：减少LLM中的隐私风险