Data Labeling

Data labeling, the process of annotating data for machine learning, aims to create high-quality training datasets efficiently. Current research focuses on automating labeling through techniques like active learning (strategically selecting data for annotation), synthetic data generation, and leveraging large language models (LLMs) for both data augmentation and direct labeling. These advancements are crucial for mitigating the significant cost and time constraints associated with manual labeling, thereby accelerating progress in various fields including medical imaging, natural language processing, and computer vision.