Few-shot learning

Definition

Few-shot learning enables large language models to perform tasks effectively using only a small number of labeled examples (typically 1-10) provided in the prompt, relying on in-context learning without parameter updates. It bridges the gap between zero-shot and fine-tuning by demonstrating patterns through examples.

Models Using Few-shot learning(7)