Task Adaptation

Task adaptation focuses on efficiently modifying pre-trained models to perform new tasks without extensive retraining, aiming to improve both accuracy and resource efficiency. Current research emphasizes parameter-efficient fine-tuning methods, such as low-rank adaptation and adapter modules, often combined with techniques like in-context learning, prompting strategies, and active learning to optimize data usage. This field is crucial for deploying large models on resource-constrained devices and for enabling rapid adaptation to evolving real-world scenarios across diverse applications, including computer vision, natural language processing, and robotics.