Few-Shot Learning
The ability of AI models to learn new tasks with only a small number of training examples.
Detailed Definition
Few-shot learning is an important capability in machine learning where AI models can learn to perform new tasks or recognize new categories after being exposed to only a few (typically 1-10) training examples. This contrasts with traditional supervised learning that requires large amounts of labeled data. Large language models often demonstrate strong few-shot learning capabilities, enabling them to quickly adapt to new scenarios. This ability is achieved through pre-training on diverse datasets that help models learn general patterns and representations that can transfer to new tasks. Few-shot learning is particularly valuable in scenarios where labeled data is scarce or expensive to obtain, such as specialized domains or rare conditions.
Learning MethodsMore in this Category
Reinforcement Learning
A machine learning approach where agents learn optimal behaviors through trial-and-error interactions with an environment.
Transfer Learning
A machine learning method that applies knowledge and skills learned from one task to different but related tasks.
Zero-Shot Learning
The ability of AI models to perform tasks without having seen specific examples during training.