Label Model

Label models are a crucial component of weak supervision, aiming to synthesize accurate training labels from multiple noisy or incomplete sources. Current research focuses on improving label model accuracy and efficiency, exploring architectures like Bayesian models, Graph Neural Networks, and Hidden Markov Models to aggregate diverse labeling functions effectively, often incorporating instance features to enhance performance. This work is significant because it enables the creation of large, high-quality datasets for machine learning tasks where manual labeling is expensive or impractical, impacting fields like natural language processing, computer vision, and clinical data analysis. The development of robust and scalable label models is accelerating progress in various applications by reducing the reliance on extensive human annotation.