Label Constraint

Label constraints in machine learning address the challenge of limited labeled data, impacting model training and performance. Current research focuses on developing methods to effectively utilize limited labels, including techniques like constrained decoding, semi-supervised learning with pseudo-labeling, and selective labeling strategies combined with prompt tuning. These advancements aim to improve model accuracy and efficiency in scenarios with scarce labeled data, impacting various applications from medical image analysis to information extraction. The overarching goal is to achieve near full-supervision performance with significantly reduced labeling effort.