Parameter-Efficient Fine-Tuning in Large Models: A Survey of
Methodologies
Parameter-Efficient Fine-Tuning in Large Models: A Survey of
Methodologies
The large models, as predicted by scaling raw forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks, where they have approached or even surpassed human levels. However, the unprecedented scale of their parameters brings significant computational and storage costs. These large models require substantial computational …