Gold Standard Label

"Gold standard" labels, representing expert-verified annotations for training machine learning models, are crucial but often scarce and expensive to obtain. Current research focuses on mitigating this limitation through techniques like iterative label refinement from unlabeled data, leveraging language models to generate synthetic training examples, and utilizing "silver standard" (pseudo) labels derived from less reliable sources to augment limited gold standard data. These approaches aim to improve model performance and generalization, particularly in domains with high annotation costs, such as medical image analysis and information retrieval, ultimately enabling wider application of machine learning in data-scarce settings.