Human Annotator

Human annotators are crucial for creating high-quality training data for machine learning models, but their involvement is often costly and time-consuming. Current research focuses on improving annotator efficiency and accuracy through techniques like active learning, which strategically selects data for annotation, and the use of large language models (LLMs) to automate parts of the annotation process or even simulate annotator behavior. These advancements aim to reduce the reliance on human annotators while maintaining data quality, impacting various fields from image recognition and natural language processing to remote sensing and legal document analysis. The ultimate goal is to create more efficient and scalable methods for generating high-quality training data for machine learning.