Few-shot learning is a technique in machine learning and artificial intelligence where a model learns to perform a new task or recognize new classes using only a small number of labeled examples—often just a handful. This approach is inspired by the human ability to generalize from very limited information. For instance, a child doesn’t need to see thousands of pictures of a giraffe to recognize one later; a few examples are usually enough.
In traditional supervised learning, models are trained on large datasets with many labeled examples for each class. However, collecting and labeling massive datasets can be expensive, time-consuming, or even impossible for rare events and specialized domains. Few-shot learning aims to address this limitation by enabling models to generalize from very limited data.
Most few-shot learning methods rely on meta-learning, which is sometimes described as “learning to learn.” The idea is to train a model across a wide variety of tasks so it becomes skilled at rapidly adapting to new ones with minimal data. During this process, the model learns a good initialization or set of parameters that can be quickly fine-tuned on a new task using only a few examples (the “shots”).
A common way to set up few-shot learning is through “n-way, k-shot” tasks. For example, in a 5-way, 1-shot task, a model must distinguish between 5 classes after seeing just 1 labeled example of each. This setup is frequently used in image classification, natural language processing, and other domains. Popular approaches include prototypical networks, matching networks, and model-agnostic meta-learning (MAML). These techniques help models form representations that are transferable and can be adapted efficiently.
Few-shot learning has become especially important with the rise of large language models and AI systems deployed in real-world settings where labeled data may be scarce or privacy-sensitive. For instance, medical image analysis or niche language translation often require models to perform well with few available examples. In conversational AI, few-shot prompting means describing a new behavior or task to a model using a handful of examples within a prompt, allowing it to adapt without further training.
The main challenges in few-shot learning include preventing overfitting to the small set of examples, ensuring generalization to new tasks, and designing training regimes that expose the model to enough task diversity. Advances in this field are helping AI systems become more flexible, sample-efficient, and accessible for applications where big data isn’t available.
