不训练也不反向传播，直接削弱模型中敏感知识的激活
Knowledge Vector Weakening: Efficient Training-free Unlearning for Large Vision-Language Models
- 通过识别遗忘数据激活的知识向量并逐步削弱其影响
- 在MLLMU和CLEAR上实现稳定遗忘-保留平衡，效率远超传统方法
- 适合需要快速清除隐私或有害内容的大型多模态模型应用
大型视觉语言模型（LVLM）凭借强大的多模态能力被广泛应用，但存在隐私泄露和生成有害内容等风险。机器遗忘作为一种解决方案，旨在移除特定数据对已训练模型的影响。然而，现有方法大多依赖基于梯度的优化，对大规模LVLM带来巨大计算开销。为此，我们提出知识向量削弱（KVW），一种无需训练的遗忘方法，直接干预完整模型而无需计算梯度。KVW识别模型在遗忘数据输出时激活的知识向量，并逐步减弱其贡献，从而阻止模型利用不良知识。在MLLMU和CLEAR基准上的实验表明，KVW在保持稳定遗忘-保留权衡的同时，显著提升了计算效率，优于基于梯度和LoRA的遗忘方法。
原文摘要 · Abstract (English)
Large Vision-Language Models (LVLMs) are widely adopted for their strong multimodal capabilities, yet they raise serious concerns such as privacy leakage and harmful content generation. Machine unlearning has emerged as a promising solution for removing the influence of specific data from trained models. However, existing approaches largely rely on gradient-based optimization, incurring substantial computational costs for large-scale LVLMs. To address this limitation, we propose Knowledge Vector Weakening (KVW), a training-free unlearning method that directly intervenes in the full model without gradient computation. KVW identifies knowledge vectors that are activated during the model's output generation on the forget set and progressively weakens their contributions, thereby preventing the model from exploiting undesirable knowledge. Experiments on the MLLMU and CLEAR benchmarks demonstrate that KVW achieves a stable forget-retain trade-off while significantly improving computational efficiency over gradient-based and LoRA-based unlearning methods.
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