利用机器遗忘净化大型语言模型
Leverage Unlearning to Sanitize LLMs
AI总结：
针对LLM微调后记忆敏感数据的问题，提出基于擦除与修复两阶段的机器遗忘方法SANI，通过重置神经元并微调以净化模型，实验显示少量训练即可大幅减少敏感信息复述。
摘要：
预训练的大型语言模型（LLMs）正逐渐在各种任务中发挥作用。为了提升其在特定任务上的性能，有必要在特定数据语料库（如医疗报告、商业数据）上对其进行微调。这些专门的数据语料库可能包含敏感数据（如个人或机密数据），这些数据会被模型记忆，并很可能在其后续使用过程中被复述出来。模型对敏感信息的这种记忆构成了重大的隐私或保密问题。为了消除这种记忆并在无需对安全数据语料库进行昂贵额外微调的情况下净化模型，我们提出了SANI。SANI是一种用于净化语言模型的机器遗忘方法。它依赖于擦除和修复两个阶段：1）重置模型最后几层中的某些神经元，以破坏对细粒度信息的记忆；2）随后对模型进行微调，同时避免记忆敏感信息。我们全面评估了SANI在净化模型方面的效果：一方面，针对使用医疗数据微调并专业化的模型，通过从模型记忆中移除直接和间接标识符进行净化；另一方面，针对标准预训练模型，通过移除被定义为机密信息的特定术语进行净化。结果表明，仅需少量额外的遗忘训练轮次，模型即被净化，且复述次数大幅减少。该方法对于医院或其他已投入大量资源在大型数据集上训练模型并希望在共享前进行净化的行业尤为有用。
英文摘要：
Pre-trained large language models (LLMs) are becoming useful for various tasks. To improve their performance on certain tasks, it is necessary to fine-tune them on specific data corpora (e.g., medical reports, business data). These specialized data corpora may contain sensitive data (e.g., personal or confidential data) that will be memorized by the model and likely to be regurgitated during its subsequent use. This memorization of sensitive information by the model poses a significant privacy or confidentiality issue. To remove this memorization and sanitize the model without requiring costly additional fine-tuning on a secured data corpus, we propose SANI. SANI is an unlearning approach to sanitize language models. It relies on both an erasure and repair phases that 1) reset certain neurons in the last layers of the model to disrupt the memorization of fine-grained information, and then 2) fine-tune the model while avoiding memorizing sensitive information. We comprehensively evaluate SANI to sanitize both a model fine-tuned and specialized with medical data by removing directly and indirectly identifiers from the memorization of the model, and a standard pre-trained model by removing specific terms defined as confidential information from the model. Results show that with only few additional epochs of unlearning, the model is sanitized and the number of regurgitations is drastically reduced. This approach can be particularly useful for hospitals or other industries that have already spent significant resources training models on large datasets and wish to sanitize them before sharing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
