Structural Generalization

Structural generalization in machine learning focuses on developing models capable of extrapolating learned patterns to novel combinations of previously seen components, rather than simply memorizing training examples. Current research emphasizes evaluating and improving this ability in various tasks, particularly natural language processing (NLP), using architectures like transformers and graph neural networks, often incorporating techniques like pre-training and structural constraints. The ability to achieve robust structural generalization is crucial for building more reliable and adaptable AI systems across diverse applications, including autonomous systems, semantic parsing, and machine translation.