The proliferation of Large Language Models (LLMs) has driven considerable
interest in fine-tuning them with domain-specific data to create specialized
language models. Nevertheless, such domain-specific fine-tuning data often
contains sensitive personally identifiable information (PII). Direct
fine-tuning LLMs on this data without privacy protection poses a risk of
leakage. To address this challenge, we introduce Privacy Protection Language
Models (PPLM), a novel paradigm for fine-tuning LLMs that effectively injects
domain-specific knowledge while safeguarding data privacy. Our work offers a
theoretical analysis for model design and delves into various techniques such
as corpus curation, penalty-based unlikelihood in training loss, and
instruction-based tuning, etc. Extensive experiments across diverse datasets
and scenarios demonstrate the effectiveness of our approaches. In particular,
instruction tuning with both positive and negative examples, stands out as a
promising method, effectively protecting private data while enhancing the
model's knowledge. Our work underscores the potential for Large Language Models
as robust privacy protection learners.

利用特定域数据对大型语言模型进行微调时，存在个人身份信息敏感度的问题。为了解决这一挑战，我们引入了隐私保护语言模型（PPLM），通过有效注入特定领域知识来保护数据隐私。我们的工作提供了模型设计理论分析，并详细介绍了诸如语料库整理、基于惩罚性失真的训练损失和基于指令的微调等技术。在各种数据集和场景下的广泛实验证实了我们方法的有效性。特别是，正负样本指令微调成为一种有潜力的方法，可以在增强模型知识的同时保护私人数据。我们的工作突显了大型语言模型作为强大隐私保护学习器的潜力。