What is few-shot learning in NLP?

Few-shot learning in NLP is a technique where a machine learning model learns to perform a task after being exposed to a very small number of examples—often as few as 3 to 10 instances. Unlike traditional supervised learning, which requires large labeled datasets to train effectively, few-shot methods rely on the model’s ability to generalize from minimal input. This is possible because the model is typically pre-trained on vast amounts of general text data, allowing it to recognize patterns and apply them to new tasks with limited additional guidance. For example, a model might be given three labeled sentences demonstrating sentiment analysis (positive, negative, neutral) and then asked to classify new sentences based on those examples.

A common implementation involves prompt engineering, where developers design a textual prompt that includes the task description, a few examples, and the input to process. Suppose you want to classify emails into categories like “urgent,” “spam,” or “general.” A prompt might look like: `Classify the email: “Meeting rescheduled to 3 PM.” Examples:

1. “Server down!” → urgent
2. “Earn money fast!” → spam
3. “Project update attached.” → general Email: “Your invoice is ready.” →` The model uses the examples to infer the task and generate a label. Another approach is fine-tuning, where a pre-trained model (like BERT or GPT) is adjusted using a small dataset specific to the task. For instance, a developer might fine-tune a model on five examples per class for intent detection in a chatbot, allowing it to recognize user goals like “book a flight” or “cancel order.”

While few-shot learning reduces reliance on large datasets, it has limitations. The quality of examples is critical—poorly chosen or ambiguous samples can lead to incorrect generalizations. For instance, if all “urgent” examples in a prompt contain exclamation marks, the model might overindex on punctuation rather than context. Developers must also balance model size: larger models handle few-shot tasks better but require more computational resources. Techniques like data augmentation (e.g., paraphrasing examples) or using templates to standardize input formats can improve reliability. In practice, few-shot learning works best for tasks that align closely with a model’s pre-training (e.g., text classification) and less so for highly specialized domains without sufficient prior knowledge.