Which approach aims to fine-tune only a small portion of a large language model's parameters to minimize computational demands? Parameter efficient Fine-tuning (PEFT)
Answer (1)
Parameter Efficient Fine-Tuning (PEFT) is an approach in the field of natural language processing (NLP) that aims to optimize the adaptation of pre-trained large language models to specific tasks, while minimizing computational resources and data requirements.
Who? This approach is mainly utilized by researchers and developers working in the field of artificial intelligence (AI) and NLP, who are looking to refine large language models such as BERT, GPT, or T5.
What? PEFT focuses on adjusting only a small fraction of the model's parameters instead of all of them. This method targets specific layers or modules within the neural network that are most relevant to the new task. This contrasts with traditional fine-tuning, which involves updating all of the model's parameters, often leading to increased computational costs and overfitting risks.
Why? The main reasons for using PEFT are to reduce computational demand, avoid large memory footprints, and increase efficiency during training. This is particularly valuable when computational resources are limited or when working with massive models where computational costs can be prohibitive.
How? Techniques commonly used in PEFT include:
Adapter Layers: Adding small trainable modules between existing layers of the pre-trained model, which can capture task-specific information without updating the entire model.
Low-Rank Adaptation (LoRA): Assuming that the change needed in the weights due to new tasks is of a low-rank nature, thus modifying models in a resource-efficient manner.
Gradient Adjustment: Selecting and adjusting gradients for only those parts of the model that contribute the most to performance improvements for the new task.
By implementing PEFT, researchers can achieve effective task-specific model adaptation with significantly reduced computational demand, maintaining the efficiency and scalability necessary for wide-scale applications.
