Lightweight Fine Tuning

Lightweight fine-tuning focuses on adapting large pre-trained models to specific tasks using minimal parameter updates, aiming to improve efficiency and reduce computational costs while maintaining performance. Current research explores various techniques, including adapter modules added to existing architectures (like diffusion models for audio processing) and methods that selectively fine-tune specific layers or embeddings, often in conjunction with retrieval augmentation for improved knowledge access. This approach is significant because it addresses the limitations of full fine-tuning, particularly in resource-constrained environments and long-tail learning scenarios, enabling faster adaptation and deployment of powerful models across diverse applications.