Annotation Strategy

Annotation strategy in machine learning focuses on efficiently and effectively creating high-quality labeled datasets for training models, addressing the significant cost and time constraints of manual annotation. Current research explores diverse approaches, including leveraging large language models (LLMs) for automated annotation or pseudo-labeling, employing active learning techniques to prioritize informative samples, and developing novel quality assurance methods for human annotation processes. These advancements are crucial for improving the accuracy and scalability of machine learning applications across various domains, from medical image analysis and natural language processing to robotics and financial technology.