Finetune Paradigm

The "finetune paradigm" in machine learning involves pretraining a large model on a massive dataset, then adapting it to specific downstream tasks with minimal additional training. Current research focuses on improving efficiency and effectiveness through techniques like parameter-efficient fine-tuning (PEFT), active finetuning (strategically selecting data for annotation), and innovative model architectures such as Low-Rank Adaptation (LoRA) and Mixture-of-Experts (MoE) approaches. This paradigm is crucial for addressing data scarcity in many domains, enabling rapid adaptation of powerful models to diverse applications while reducing computational costs.