Few-Shot Learning

Few-shot learning is designed to help models adapt when large amounts of labeled training data are unavailable. Instead of requiring thousands of examples, the model uses a limited set of demonstrations to understand the task or recognize new patterns. In large language models, few-shot prompting provides several examples within the prompt to guide the model toward the expected type of response.

Collecting and labeling large datasets can be expensive and time-consuming. Few-shot learning enables AI systems to adapt to new tasks with fewer examples, making model development more efficient and useful in areas where training data is limited.