Generalisation Ability

Generalization ability in machine learning focuses on a model's capacity to perform well on unseen data, a crucial aspect for real-world applications. Current research investigates how model architecture, training techniques (like incorporating noise or prompt engineering), and optimization strategies (such as targeting "flat minima") influence generalization. This research is vital because improved generalization leads to more robust and reliable AI systems across diverse domains, from natural language processing to computer vision, ultimately impacting the effectiveness and trustworthiness of AI applications.