Fewer Example

Research on "fewer example" learning focuses on improving the efficiency and effectiveness of machine learning models by reducing their reliance on massive datasets. Current efforts concentrate on developing algorithms and model architectures that can achieve high performance with limited training data, including techniques like in-context learning, active learning for example selection, and methods leveraging large language models for data augmentation or improved generalization. This research is significant because it addresses the limitations of data-hungry models, potentially lowering the cost and time required for training, and enabling applications in data-scarce domains.