Comprehensive Taxonomy
Comprehensive taxonomies organize complex domains into hierarchical structures, aiming to clarify relationships between concepts and facilitate knowledge discovery and application. Current research focuses on developing and refining taxonomies across diverse fields, including natural language processing, computer vision, and machine learning, often leveraging large language models and advanced algorithms to automate the process and improve accuracy. These efforts are significant because well-structured taxonomies improve the efficiency of research, enhance the interpretability of complex models, and enable the development of more robust and reliable applications in various domains.
Papers
Taxonomy for Resident Space Objects in LEO: A Deep Learning Approach
Marta Guimarães, Cláudia Soares, Chiara Manfletti
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, Ting Liu
Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review
Catalina Gomez, Sue Min Cho, Shichang Ke, Chien-Ming Huang, Mathias Unberath
A Survey of Federated Unlearning: A Taxonomy, Challenges and Future Directions
Yang Zhao, Jiaxi Yang, Yiling Tao, Lixu Wang, Xiaoxiao Li, Dusit Niyato