Field of Study:

Parameter-Efficient Fine-Tuning (PEFT) is a method that aims to reduce the number of parameters that need to be fine-tuned in large pre-trained language models. This is achieved by freezing the majority of the parameters and only fine-tuning a small subset, which makes the process more efficient in terms of computational resources and time. This approach also helps to mitigate overfitting, especially when the available fine-tuning data is limited.

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