Few-Shot Learning - Page 2

Few-shot learning enables an AI system to perform a task after seeing only a small number of examples. In a language-model prompt, a user might provide two or three sample inputs with the desired outputs, allowing the model to infer the pattern without changing its underlying parameters. Other few-shot methods train models to adapt quickly to new classes or domains where labeled data is scarce. The approach is valuable for specialized documents, rare events, emerging products, and languages with limited datasets. Results depend heavily on whether the examples are accurate, representative, consistently formatted, and close to the real task. A model may copy irrelevant details or fail on cases outside the examples, so few-shot performance still requires testing and clear instructions.