Cross Task Generalization

Cross-task generalization in machine learning focuses on developing models capable of adapting to new tasks with minimal retraining, leveraging knowledge acquired from previously seen tasks. Current research emphasizes techniques like prompt engineering, parameter-efficient fine-tuning (e.g., using LoRA adapters or meta-learning for prompt initialization), and hybrid training approaches combining online and offline learning to improve generalization across diverse tasks and domains. This research area is crucial for building more robust and adaptable AI systems, reducing the need for extensive task-specific data and improving the efficiency of model development across various applications.