Context Generalization
Context generalization focuses on enabling machine learning models, particularly deep neural networks, to effectively apply knowledge learned from one context to novel, unseen contexts or tasks. Current research investigates how model architectures, such as transformers and vision transformers, can be trained to generalize through mechanisms like learning compositional representations, leveraging contextual information within prompts, and employing techniques like prototypical context-aware dynamics. This research is significant because improved context generalization is crucial for building more robust and adaptable AI systems capable of handling real-world scenarios with diverse and unpredictable inputs, impacting fields ranging from natural language processing to robotics.