SFT
IntermediateEnglish: SFT
Fine-tuning on (prompt, response) pairs to align a model with instruction-following behaviors.
Why It Matters
SFT is important because it helps AI models become more aligned with user expectations and instructions. By training models to follow specific prompts, industries can create AI systems that provide more relevant and accurate information, improving user experience and satisfaction.
Definition
Supervised Fine-Tuning (SFT) is a method employed in the training of machine learning models, particularly in natural language processing, where the model is fine-tuned on a dataset consisting of pairs of prompts and corresponding responses. This approach aims to align the model's behavior with specific instruction-following capabilities, enhancing its ability to generate relevant and contextually appropriate outputs. Mathematically, SFT involves optimizing the model parameters to minimize a loss function that measures the discrepancy between the predicted responses and the ground truth responses provided in the training data. SFT is a critical step in the development of models that require adherence to user instructions and preferences, serving as a bridge between general language understanding and task-specific performance.