Annotation Task

Annotation tasks, the process of labeling data for machine learning, are crucial for training accurate models but often involve significant human effort and inherent subjectivity. Current research focuses on automating annotation using large language models (LLMs) like GPT and LLaMA, exploring techniques such as few-shot prompting, collaborative learning, and prompt optimization to improve efficiency and consistency across diverse tasks, including image segmentation, text classification, and complex structured data. These advancements aim to reduce costs, improve annotation quality, and enable the creation of larger, higher-quality datasets for various scientific and practical applications, particularly in fields like social sciences, finance, and industrial quality control.