Multi Task Adaptation

Multi-task adaptation focuses on efficiently training large pre-trained models (like LLMs and Vision Transformers) to perform multiple tasks simultaneously, avoiding the resource-intensive process of individual fine-tuning for each task. Current research emphasizes parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA) and its variants (e.g., MultiLoRA), along with novel architectures like Mixture of Dyadic Experts (MoDE) and hierarchical adapters, to minimize the number of trainable parameters while maintaining performance. This area is significant because it enables the deployment of powerful models across diverse applications with reduced computational costs and improved resource utilization, particularly in resource-constrained environments like edge computing and medical image analysis.