Few-Shot Learning

Core Idea

Few-shot learning is a technique where a model is trained to perform a task with very few examples, making it adaptable with minimal data.

Explanation

Few-shot learning allows AI models to generalize tasks by learning from a limited number of examples, such as one or a few demonstrations. This method is especially useful for adapting large pre-trained models to specific tasks without extensive fine-tuning. Few-shot learning is common in language models, where it enables the model to complete tasks by showing it just a handful of examples.

Applications/Use Cases

• Text Classification – Categorizing text based on limited labeled examples, useful for new topics or domains.
• Customer Support – Adapts models to address new types of questions with only a few sample queries.
• Language Translation – Translating phrases in less common languages or dialects with minimal training data.

Related Resources

• “Language Models are Few-Shot Learners” (GPT-3 Paper) – Explores how few-shot learning works within large language models.

Related People

• TBD

Related Concepts

• Fine-Tuning – Few-shot learning is an alternative to fine-tuning for specific tasks.
• Prompt – Few-shot prompting often involves showing examples in the prompt to guide the model.