Large language models (LLMs) like BERT and GPT-3 require fine-tuning for specific tasks, but this can be computationally expensive.
Adapters offer a more parameter-efficient method by adding tunable layers to the transformer blocks of LLMs, allowing for better performance on target tasks and datasets.