How do LLMs perform few-shot and zero-shot learning?

Few-shot and zero-shot learning allow LLMs to perform tasks without explicit retraining by learning patterns directly from prompts.

Traditional machine learning systems usually require task-specific training. LLMs introduced a fundamentally different paradigm where models can generalize from prompts alone.

The model performs tasks using only instructions.

Example: 'Summarize this article.'

The prompt includes a few demonstrations showing expected behavior.

Example: Input → Output examples embedded inside the prompt.

This works because large transformer models develop generalized representations during pretraining.…