Annotation Rather

Annotation, the process of labeling data for machine learning, is a crucial but often laborious task driving advancements across diverse fields. Current research focuses on improving annotation efficiency through tools like interactive visualization platforms and leveraging large language models (LLMs) for automated or semi-automated annotation, particularly for complex tasks like sentiment analysis, focalization detection, and factual error identification. These efforts aim to reduce the cost and time associated with data labeling, enabling the development of more accurate and robust machine learning models with applications ranging from medical image analysis to improved natural language processing.