Silver Standard

Silver standard data, pseudo-labeled data generated by machine learning models, is increasingly used to augment limited gold-standard datasets in various natural language processing and computer vision tasks. Current research focuses on improving the quality and utility of silver standard data through techniques like data cleaning, confidence-based weighting in loss functions, and leveraging multiple models or data sources to create more robust labels. This approach significantly reduces the reliance on expensive human annotation, enabling the development and training of more powerful models for tasks such as relation extraction, event summarization, and object detection, ultimately advancing the capabilities of these fields.