Parameter Efficient Tuning

Parameter-efficient tuning (PET) focuses on adapting large pre-trained models to specific tasks by modifying only a small fraction of their parameters, thereby reducing computational costs and storage requirements. Current research emphasizes techniques like low-rank adaptation (LoRA), adapters, and prompt tuning, often applied within transformer architectures and increasingly explored for multimodal models and continual learning scenarios. This approach is significant because it enables the deployment of powerful models on resource-constrained devices and facilitates more efficient and scalable model personalization across diverse applications, including natural language processing, computer vision, and medical image analysis.