Annotation Data

Annotation data, the labeled examples used to train machine learning models, is crucial for advancing various fields like natural language processing and computer vision. Current research focuses on improving annotation efficiency through techniques like leveraging large language models to generate synthetic labeled data, employing contrastive learning for cross-domain knowledge transfer, and utilizing annotation byproducts to enhance model robustness. These advancements aim to reduce the high cost and effort associated with manual annotation, ultimately leading to more accurate and generalizable models with broader applications across diverse domains.