Manual Annotation

Manual annotation, the process of labeling data for machine learning, remains crucial despite advancements in automated methods. Current research focuses on improving annotation efficiency and quality through techniques like active learning, leveraging large language models (LLMs) for automated annotation or pre-annotation, and developing novel frameworks for efficient human-in-the-loop systems. This work is driven by the high cost and time constraints of manual annotation, particularly for large-scale datasets, and aims to improve the accuracy and scalability of machine learning models across diverse applications. The resulting improvements in data quality and annotation efficiency have significant implications for various fields, including natural language processing, computer vision, and biomedical research.