smaller, task-speciﬁc datasets. Current PEFT techniques can be categorized into two
main streams: 1) Masking-based Fine-tuning and 2) Adapter-based Fine-tuning.
Masking-based ﬁne-tuning. In this approach, only a subset of the model’s parame-
ters is updated during the ﬁne-tuning process. The rest of the parameters are “masked”
or frozen, meaning they are not updated during backpropagation. This masking could
be applied to speciﬁc layers, certain types of parameters, or parameters identiﬁed
through various criteria like importance scores. Previous research endeavors [92, 93]
have focused on the optimization of ﬁne-tuning procedures for comparatively smaller
language models through the deployment of diverse regularization methodologies.
However, these approaches exhibit limitations when applied to the ﬁne-tuning of
LLMs due to the signiﬁcantly elevated computational requirements and voluminous
datasets essential for the eﬀective training of such models. In addressing these chal-
Beyond Efficiency