Label Error

Label error, the presence of inaccurate annotations in training datasets, significantly impacts the performance and reliability of machine learning models across various domains. Current research focuses on developing robust methods for detecting and correcting these errors, employing techniques such as uncertainty quantification, loss inspection, and contrastive learning within diverse model architectures including neural networks and pre-trained language models. Addressing label error is crucial for improving model accuracy, generalizability, and fairness, ultimately leading to more reliable and trustworthy AI systems in diverse applications.