<ul><li>Large models have made significant progress in natural language generation tasks, but their parameter scale poses challenges in fine-tuning.</li><li>Parameter-Efficient Fine-Tuning (PEFT) offers a solution to efficiently adjust parameters of large pre-trained models for specific tasks.</li><li>PEFT minimizes the introduction of additional parameters and reduces the computational resources required.</li><li>This review provides an overview of PEFT, including its core principles, applications, and future research directions.</li></ul>

Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

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