Hierarchical Taxonomy

Hierarchical taxonomy organizes data into nested categories, mirroring human cognitive structures and enabling efficient information processing. Current research focuses on leveraging hierarchical structures to improve machine learning model performance, particularly in continual learning, large language model error detection, and multi-label classification tasks, often employing transformer-based architectures and incorporating hierarchical information into loss functions or attention mechanisms. This work is significant for enhancing the accuracy and robustness of AI systems across diverse applications, from financial transaction processing to biomedical image analysis and patent classification, by effectively managing complex, multi-faceted data.