Generative AI with LLMs: Reparameterization: A Parameter-Efficient Fine-Tuning Method for LLMs

Learn what reparameterization is and how it can help you fine-tune large language models (LLMs) with minimal resources and maximal performance.

Question

Which of the following are Parameter Efficient Fine-Tuning (PEFT) methods? Select all that apply.

A. Additive
B. Subtractive
C. Reparameterization
D. Selective

Answer

C. Reparameterization

Explanation

The correct answer is C. Reparameterization. Reparameterization is a general term for PEFT methods that modify the existing parameters of a pre-trained model in a way that preserves its original functionality while allowing for adaptation to a new task. Some examples of reparameterization methods are adapter, LoRA, and prefix tuning. These methods insert additional modules or layers into the pre-trained model and fine-tune only those parameters, leaving the rest of the model fixed. This reduces the computational and storage costs of fine-tuning, as well as the risk of overfitting or catastrophic forgetting.

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