Label Tuning

Label tuning is a machine learning technique focused on efficiently adapting pre-trained models to new tasks with limited labeled data. Current research emphasizes improving label efficiency through methods like experimental design for sample selection, adaptive label construction and iterative refinement, and targeted adjustments to specific model components such as batch normalization layers or label embeddings within Siamese networks and LLMs. This approach offers significant potential for reducing the high annotation costs associated with training large models, particularly in domains like natural language processing and image segmentation, leading to more accessible and scalable AI solutions.