Parameter-Efficient LLM Finetuning With Low-Rank Adaptation (LoRA)

Pretrained large language models are often referred to as foundation models for a good reason: they perform well on various tasks, and we can use them as a foundation for finetuning on a target task. As an alternative to updating all layers, which is very expensive, parameter-efficient methods such as prefix tuning and adapters have been developed. Let's talk about one of the most popular parameter-efficient finetuning techniques: Low-rank adaptation (LoRA). What is LoRA? How does it work? And how does it compare to the other popular finetuning approaches? Let's answer all these questions in this article!