Few-shot learning in NLP refers to the ability of a model to perform a task with very limited labeled training data—often just a few examples. It contrasts with traditional supervised learning, which requires large amounts of annotated data. Few-shot learning relies heavily on pre-trained models like GPT or T5, which have been trained on diverse and extensive corpora, allowing them to generalize well across tasks.
A common approach to few-shot learning involves providing the model with a task description and a small number of examples in the input prompt. For instance, "Classify the sentiment of the following reviews. Example: 'I love this movie' → Positive. Example: 'The food was terrible' → Negative. Now classify: 'The service was excellent'." The model leverages its pre-trained knowledge to infer patterns and complete the task.
Few-shot learning is particularly valuable in scenarios where labeled data is scarce or expensive to obtain, such as low-resource languages or niche domains. It is widely applied in text classification, translation, and question-answering tasks. By reducing the dependence on extensive labeled datasets, few-shot learning has made NLP more accessible and cost-effective.