Active Annotation

Active annotation is a machine learning technique aiming to reduce the cost and effort of creating labeled datasets by strategically selecting the most informative data points for human annotation. Current research focuses on integrating active learning with large language models and other advanced architectures to improve annotation efficiency and quality, often employing techniques like uncertainty sampling and pattern exploiting training. This approach is significantly impacting various fields, including natural language processing and medical image analysis, by enabling the development of high-performing models with substantially less human annotation effort. The resulting datasets and models are more cost-effective and can accelerate progress in numerous applications.