Human Annotated

Human annotation of data remains crucial for training advanced machine learning models, particularly in natural language processing and computer vision, despite efforts to reduce reliance on it. Current research focuses on improving the efficiency and quality of annotation through techniques like knowledge distillation from large language models (LLMs), developing automated annotation tools and frameworks, and creating more robust evaluation metrics that better reflect human judgment. This work is vital for advancing the accuracy and reliability of AI systems across diverse applications, from text summarization and emotion recognition to object detection and information extraction from complex documents.