Supervised fine-tuning (SFT) is a technique used to adapt pre-trained models to specific tasks using labelled data. It involves taking a model that was pre-trained on large datasets and then further training it on a specialised labeled dataset to improve its performance for particular applications.
In the context of LLMs, SFT is typically used as the first step in the alignment process, teaching them to follow instructions and respond appropriately to user queries.