Finetuning LLMs Efficiently with Adapters
Large language models (LLMs) like BERT, GPT-3, GPT-4, LLaMA, and others are trained on a large corpus of data and have general knowledge. However, they may not perform as well on specific tasks without finetuning. For example, if you want to use a pretrained LLM for analyzing legal or medical documents, finetuning it on a corpus of legal documents can significantly improve the model's performance. (Interested readers can find an overview of different LLM finetuning methods in my previous article, Finetuning Large Language Models: An Introduction To The Core Ideas And Approaches.)
However, finetuning LLMs can be very expensive in terms of computational resources and time, which is why researchers started developing parameter-efficient finetuning methods.
As discussed in a previous article, many different types of parameter-efficient methods are out there. In an earlier post, I wrote about prompt and prefix tuning. (Although the techniques are somewhat related, you don't need to know or read about prefix tuning before reading this article about adapters.)
