During full fine-tuning, the PLM is initialized with pre-
trained weights and subsequently trained on task-specific data
using techniques like backpropagation and gradient descent
[72], [73]. All model parameters, including pretrained weights,
are updated to minimize a task-specific loss that quantifies
the disparity between predicted outputs and ground truth. In
this way, full fine-tuning enables the model to learn task-
specific patterns and nuances from the labeled data, facili-
tating predictions or outputs tailored to the target tasks [74].
Notably, full fine-tuning necessitates substantial computational
resources and labeled data, as the model is trained from scratch
for the specific target task. Moreover, as PLMs grow in size
and with the advent of LLMs containing billions of parameters,
full fine-tuning places even greater demands on computational
resources. In contrast, PEFT methods aim to alleviate these re-
quirements by selectively updating or modifying specific parts
Parameter-EfficientFine-TuningMethods