Definition
Few-shot learning is a technique where a machine learning model is trained or prompted with a very small number of examples (shots) to perform a specific task. This allows the model to generalize to new, unseen data with limited prior exposure.
Why it matters (in Poovi’s context)
It’s a precursor and related concept to fine-tuning, demonstrating how providing a few examples in a prompt can significantly improve task performance. Fine-tuning can be seen as an extension of few-shot learning where examples are moved from the prompt to a training dataset.
Key properties or components
- Limited Examples
- Prompt-Based Learning
- Generalization Ability
Contradictions or debates
None.