Understanding Parameter-Efficient Finetuning of Large Language Models: From Prefix Tuning to LLaMA-Adapters

In the rapidly evolving field of artificial intelligence, utilizing large language models in an efficient and effective manner has become increasingly important. Parameter-efficient finetuning stands at the forefront of this pursuit, allowing researchers and practitioners to reuse pretrained models while minimizing their computational and resource footprints. This article explains the broad concept of finetuning and discusses popular parameter-efficient alternatives like prefix tuning and adapters. Finally, we will look at the recent LLaMA-Adapter method and see how we can use it in practice.