Automatic Annotation

Automatic annotation aims to replace or augment the laborious manual labeling of data, a crucial step in many machine learning applications. Current research heavily utilizes large language models (LLMs), often coupled with techniques like prompt engineering and chain-of-thought prompting, to automatically generate labels for text, images, and even sensor data, with some work exploring semi-supervised and weakly-supervised approaches. This automated labeling process significantly accelerates data preparation for tasks like text classification, image tagging, and activity recognition, impacting fields ranging from social science research to medical image analysis and improving the efficiency and scalability of various scientific endeavors. The emphasis is on developing robust and reliable methods, often incorporating human validation steps to ensure accuracy and address the inherent limitations of current LLMs.