What is Few-Shot Learning?

Few-Shot Learning

Definition The ability of a model to perform a new task correctly after seeing only a small number of examples in the prompt, without updating its weights. Few-shot learning is a key emergent capability of large pre-trained models.

In Depth

In practice, few-shot prompting means providing 2–10 input/output examples in the system or user prompt before asking the model to handle a new case. The model generalizes from these examples by leveraging patterns learned during pre-training. GPT-3's strong few-shot performance without fine-tuning demonstrated that scale could unlock in-context learning as a new AI paradigm.

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AI Agent AI Alignment Agentic AI Artificial General Intelligence Artificial Superintelligence Attention Mechanism AutoML Benchmark Bias (AI) Chain-of-Thought Computer Vision Deepfake Diffusion Model Edge AI Embedding Federated Learning Few-Shot Learning Fine-Tuning Function Calling GPT