  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:
\url{https://github.com/lmeraser/lmeraser}.
