Which tasks are suitable for training with few-shot learning?

Few-shot learning is particularly suitable for natural language processing (NLP) tasks like text classification, named entity recognition (NER), sentiment analysis, and semantic parsing, especially when labeled data is scarce but the task itself has a well-defined, limited set of possible outputs or a clear structural pattern. It also excels in adapting pre-trained models to new, related domains or styles without extensive retraining.

Tasks best suited for few-shot learning typically have limited and distinct output classes or templates. They rely on models capable of leveraging rich pre-trained representations to identify patterns from minimal examples. This approach is effective when the new task shares significant underlying linguistic or semantic structures with the model's pre-training data. Few-shot learning is less effective for highly ambiguous, open-ended tasks or tasks requiring complex, fine-grained classification with many similar categories. Careful prompt design and demonstration selection are crucial for performance.

In practice, few-shot learning enables rapid deployment for specialized tasks like classifying customer feedback types, extracting domain-specific entities from technical documents, or translating between niche programming languages with only a handful of annotated examples per category. This brings significant business value by dramatically reducing data collection and labeling costs, accelerating model iteration for new use cases, and leveraging foundation models efficiently for specialized applications without full fine-tuning.