Parameter Efficient Finetuning

Parameter-efficient finetuning (PEFT) aims to adapt large pre-trained models to specific tasks with minimal computational cost and parameter updates, addressing the limitations of full finetuning for resource-constrained environments. Current research focuses on techniques like low-rank adaptation (LoRA), prompt tuning, and selective layer training, often applied to transformer-based architectures and multimodal models, to improve efficiency and performance across various domains including natural language processing, computer vision, and speech recognition. PEFT's significance lies in its potential to democratize access to powerful models by reducing the computational burden and resource requirements for adaptation, enabling broader application and deployment of advanced AI systems.