Supervised Fine Tuning

Supervised fine-tuning (SFT) adapts pre-trained large language models (LLMs) to specific tasks by training them on labeled data, aiming to improve performance and alignment with human preferences. Current research focuses on optimizing SFT methods, including exploring alternative loss functions (e.g., beyond cross-entropy), developing techniques to mitigate training imbalances and overfitting, and investigating the interplay between SFT and reinforcement learning. These advancements are significant because they enhance the efficiency and effectiveness of adapting LLMs for diverse applications, ranging from question answering and code generation to specialized domains like biomedicine and legal text processing.