Annotation Process

Data annotation, the process of labeling data for machine learning, is crucial for training accurate models across diverse fields, from legal text analysis to gene function prediction and social media monitoring. Current research emphasizes improving annotation efficiency through automated methods, including leveraging large language models and developing specialized tools tailored to specific data types and annotation tasks. This focus on automation aims to reduce the significant time and cost associated with manual annotation, ultimately accelerating progress in various scientific domains and enabling the development of more robust and scalable AI applications. Furthermore, research highlights the importance of rigorous validation of automated annotation results and the need to address biases introduced by counting regimes in data annotation workflows.