What is Few-Shot Learning?
An approach where AI models are trained to perform tasks with only a few labeled examples.
More about Few-Shot Learning:
Few-Shot Learning is a machine learning technique that enables AI models to generalize and perform tasks with minimal labeled data. By leveraging pre-trained models like PLMs, few-shot learning reduces the need for extensive task-specific datasets.
This approach is particularly useful in scenarios like context-aware generation and prompt engineering, where examples provided in the input prompt guide the model’s behavior effectively.
Frequently Asked Questions
How does few-shot learning improve efficiency?
It minimizes the need for large datasets, enabling models to adapt to new tasks quickly and cost-effectively.
What tasks benefit from few-shot learning?
Tasks like question answering and domain-specific retrieval are ideal for few-shot learning applications.