To address the growing demand for privacy protection in machine learning, we
propose a novel and efficient machine unlearning approach for \textbf{L}arge
\textbf{M}odels, called \textbf{LM}Eraser. Existing unlearning research suffers
from entangled training data and complex model architectures, incurring
extremely high computational costs for large models. LMEraser takes a
divide-and-conquer strategy with a prompt tuning architecture to isolate data
influence. The training dataset is partitioned into public and private
datasets. Public data are used to train the backbone of the model. Private data
are adaptively clustered based on their diversity, and each cluster is used to
optimize a prompt separately. This adaptive prompt tuning mechanism reduces
unlearning costs and maintains model performance. Experiments demonstrate that
LMEraser achieves a $100$-fold reduction in unlearning costs without
compromising accuracy compared to prior work. Our code is available at:
https://github.com/lmeraser/lmeraser.

为了满足对机器学习隐私保护的不断增长需求，我们提出了一种名为 LMEraser 的适用于大型模型的新颖高效的机器去学习方法。LMEraser 采用分治策略和提示调整架构来隔离数据影响，并实现训练数据集的适应性分区，从而显著降低去学习成本且保持模型性能。与以往研究相比，LMEraser 实现了 100 倍的去学习成本减少而不影响准确性。