用AI自动生成遗忘数据集，高效移除大模型敏感知识
LLM Unlearning Without an Expert Curated Dataset
- 用结构化提示词让LLM生成教科书式合成数据
- 在生物安全、网络安全等领域效果接近人工标注集
- 无需人工干预，适合快速适配新领域遗忘需求
现代大型语言模型常包含敏感、有害或受版权保护的知识，亟需后训练遗忘能力——在不重新训练的前提下移除特定领域的知识。当前遗忘流程的主要瓶颈在于构建有效的遗忘数据集（forget sets），即能近似目标领域并引导模型遗忘的数据集。本文提出一种可扩展的自动化方法，利用语言模型自身生成高质量遗忘数据集。该方法通过结构化提示管道合成教科书风格数据，仅需输入领域名称即可完成。在生物安全、网络安全及《哈利·波特》小说三个领域的实验表明，所生成的合成数据集在性能上持续优于基线合成方案，且与专家手工构建的数据集相当。消融实验显示，多步生成流程显著提升了数据多样性，从而增强遗忘效果。总体而言，研究结果表明合成数据集为实现广泛新兴领域的实用、可扩展遗忘提供了可行路径，且无需人工介入。代码与数据集已公开于 https://github.com/xyzhu123/Synthetic_Textbook。
原文摘要 · Abstract (English)
Modern large language models often encode sensitive, harmful, or copyrighted knowledge, raising the need for post-hoc unlearning-the ability to remove specific domains of knowledge from a model without full retraining. A major bottleneck in current unlearning pipelines is constructing effective forget sets-datasets that approximate the target domain and guide the model to forget it. In this work, we introduce a scalable, automated approach to generate high-quality forget sets using language models themselves. Our method synthesizes textbook-style data through a structured prompting pipeline, requiring only a domain name as input. Through experiments on unlearning biosecurity, cybersecurity, and Harry Potter novels, we show that our synthetic datasets consistently outperform the baseline synthetic alternatives and are comparable to the expert-curated ones. Additionally, ablation studies reveal that the multi-step generation pipeline significantly boosts data diversity, which in turn improves unlearning utility. Overall, our findings suggest that synthetic datasets offer a promising path toward practical, scalable unlearning for a wide range of emerging domains without the need for manual intervention. We release our code and dataset at https://github.com/xyzhu123/Synthetic_Textbook.
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