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.
