Error Classification

Error classification focuses on identifying and categorizing mistakes made by various systems, from large language models and machine translation systems to robots and deep learning models for image recognition. Current research emphasizes developing automated methods for error detection and classification, often leveraging techniques like large language models, one-class classifiers, and rule-based systems, with a strong focus on improving the interpretability and efficiency of these methods. This work is crucial for enhancing the reliability and performance of AI systems across diverse applications, as well as providing valuable insights into the learning processes of both humans and machines.