Zero-shot / Few-shot Learning
Techniques that allow AI models to handle new tasks with little or no example data
What is Zero-shot / Few-shot Learning?
Zero-shot and few-shot learning describe an AI model's ability to perform tasks it was never explicitly trained on, using either no examples or just a handful. Imagine meeting someone who speaks a language you have never studied, but you can still guess the meaning of their words from context clues. That is essentially what zero-shot learning does. Few-shot learning is like being shown two or three example sentences before you start guessing, giving you a small but helpful head start.
How Does It Work?
In zero-shot learning, you simply describe the task in natural language. For example, you might tell a model: "Classify the following review as positive or negative." The model uses its broad training knowledge to infer what you want without seeing any labeled examples.
In few-shot learning, you include a few input-output examples in your prompt before presenting the actual task. For instance, you might show three reviews with their correct labels, and then ask the model to classify a fourth. The model picks up on the pattern from those examples and applies it.
Both approaches rely on the model's pre-trained knowledge and are implemented entirely through prompt design, requiring no additional training or fine-tuning.
Why Does It Matter?
These techniques make AI remarkably flexible and accessible. Instead of collecting thousands of labeled examples and training a custom model, you can solve new problems simply by writing a good prompt. This drastically reduces the time, cost, and expertise needed to deploy AI solutions, putting powerful capabilities in the hands of anyone who can describe their task clearly.