High Quality Annotation

High-quality annotation in various fields, from natural language processing to computer vision, is crucial for training robust and accurate machine learning models, but obtaining it is often expensive and time-consuming. Current research focuses on improving annotation efficiency and accuracy through techniques like active learning, online correction methods, and the development of improved labeling instructions and guidelines, often incorporating probabilistic models and large language models. These advancements are vital for mitigating biases, reducing annotation costs, and ultimately improving the reliability and generalizability of machine learning systems across diverse applications.