让大模型删除敏感记忆，同时保持说话能力
Operationalising the Right to be Forgotten in LLMs: A Lightweight Sequential Unlearning Framework for Privacy-Aligned Deployment in Politically Sensitive Environments
- 先加固正常能力，再针对性抑制敏感内容
- 在测试中抑制效果达90%以上，准确率损失低于5%
- 适合政治敏感场景下需合规部署的AI系统
大语言模型在政治敏感环境中部署时，可能记忆个人或机密数据，引发如GDPR等法规下的“被遗忘权”争议。本文提出一种轻量级序列化遗忘框架，明确分离保留与抑制目标：先通过正向微调稳定模型基础能力，再对特定层实施负向微调，以抑制指定敏感模式，同时保持通用语言能力。在SemEval-2025 LLM遗忘基准上的实验表明，该方法能有效抑制行为，对事实准确性和流畅性影响极小。GPT-2表现优于DistilGPT-2，凸显模型容量在隐私适配中的作用。本方法为政治部署场景下实现数据擦除要求提供了可操作、可复现的技术路径。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly deployed in politically sensitive environments, where memorisation of personal data or confidential content raises regulatory concerns under frameworks such as the GDPR and its Right to be Forgotten. Translating such legal principles into large-scale generative systems presents significant technical challenges. We introduce a lightweight sequential unlearning framework that explicitly separates retention and suppression objectives. The method first stabilises benign capabilities through positive fine-tuning, then applies layer-restricted negative fine-tuning to suppress designated sensitive patterns while preserving general language competence. Experiments on the SemEval-2025 LLM Unlearning benchmark demonstrate effective behavioural suppression with minimal impact on factual accuracy and fluency. GPT-2 exhibits greater robustness than DistilGPT-2, highlighting the role of model capacity in privacy-aligned adaptation. We position sequential unlearning as a practical and reproducible mechanism for operationalising data erasure requirements in politically deployed LLMs.
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