Pre-trained Language Models (PLMs) may be poisonous with backdoors or bias injected by the suspicious attacker during the fine-tuning process. A core challenge of purifying potentially poisonous PLMs is precisely finding poisonous dimensions. To settle this issue, we propose the Fine-purifying approach, which utilizes the diffusion theory to study the dynamic process of fine-tuning for finding potentially poisonous dimensions. According to the relationship between parameter drifts and Hessians of different dimensions, we can detect poisonous dimensions with abnormal dynamics, purify them by resetting them to clean pre-trained weights, and then fine-tune the purified weights on a small clean dataset. To the best of our knowledge, we are the first to study the dynamics guided by the diffusion theory for safety or defense purposes. Experimental results validate the effectiveness of Fine-purifying even with a small clean dataset.

利用扩散理论研究微调动态过程，通过检测异常的动态，找到具有潜在问题的维度并恢复预训练权重，从而提出了Fine-purifying方法来解决预训练语言模型中可能存在的后门或偏差问题。实验证明了该方法的有效性。

扩散理论作为手术刀：检测和净化预先训练语言模型中由后门或偏见引起的有毒维度