Annotator Simulation

Annotator simulation aims to create artificial annotators that mimic human behavior, offering a cost-effective alternative to expensive and time-consuming human annotation in various fields like medical image analysis and natural language processing. Current research focuses on developing more realistic simulation models, incorporating factors like inter-annotator disagreement and the influence of task difficulty, often employing techniques like mixup extensions and zero-shot density estimation to improve the accuracy and efficiency of simulated annotations. These advancements enable larger-scale and more robust evaluations of machine learning models, ultimately leading to improved model performance and more reliable results across diverse applications.