Weak Supervision

Weak supervision in machine learning focuses on training models using imperfect or incomplete labels, thereby reducing the reliance on expensive, high-quality human annotations. Current research explores diverse approaches, including leveraging large language models to generate labeling functions, employing meta-learning to improve label efficiency, and developing novel algorithms for combining multiple weak supervision sources, such as probabilistic models and adversarial methods. This field is significant because it enables the development of accurate machine learning models for tasks where fully labeled data is scarce or costly, impacting various applications from medical image analysis and natural language processing to environmental monitoring and financial market analysis.