What is Few-Shot Learning?
Few-Shot Learning is a machine learning paradigm where models learn to perform tasks from only a small number of examples (typically 1-10), leveraging prior knowledge to generalize from minimal training data.
Quick Facts
| Created | Concept from 2000s, LLM context from 2020 |
|---|---|
| Specification | Official Specification |
How It Works
Few-shot learning addresses one of the key limitations of traditional machine learning: the need for large labeled datasets. In the context of large language models, few-shot learning refers to providing a handful of examples in the prompt to guide the model's behavior. This technique, popularized by GPT-3, allows users to teach models new tasks without fine-tuning by simply showing examples of desired input-output pairs. It's a cornerstone of prompt engineering and in-context learning.
Key Characteristics
- Learns from 1-10 examples (shots) provided in context
- No model weight updates required
- Leverages pre-trained knowledge for generalization
- Examples provided directly in the prompt
- Performance improves with example quality and relevance
- Bridges zero-shot and full fine-tuning approaches
Common Use Cases
- Teaching models new output formats
- Classification with limited labeled data
- Customizing model behavior without fine-tuning
- Rapid prototyping of AI applications
- Domain-specific task adaptation
Example
Loading code...Frequently Asked Questions
How many examples should I provide for few-shot learning to be effective?
Typically, 2-5 examples work well for most tasks, though the optimal number depends on task complexity and model capability. More examples generally improve performance but consume more tokens from the context window. Start with 3 examples and adjust based on results—sometimes even 1-2 high-quality examples outperform many mediocre ones.
What is the difference between few-shot learning and fine-tuning?
Few-shot learning provides examples in the prompt without modifying model weights, making it fast and flexible but limited by context length. Fine-tuning actually updates the model's parameters using training data, creating a permanently customized model. Few-shot is best for quick experimentation, while fine-tuning is better for production systems requiring consistent behavior.
Why does the order and quality of examples matter in few-shot learning?
Models are sensitive to example ordering and quality because they learn patterns from the provided context. High-quality, diverse examples that clearly demonstrate the desired behavior lead to better results. Placing the most representative examples first or last (recency bias) can also impact performance, so experimenting with order is recommended.
Can few-shot learning work for any task?
Few-shot learning works best for tasks where the pattern can be clearly demonstrated through examples, such as classification, formatting, translation, and simple reasoning. It may struggle with complex multi-step reasoning, tasks requiring domain knowledge not in the model, or highly creative tasks where examples might constrain output diversity.
What is the relationship between few-shot learning and prompt engineering?
Few-shot learning is a key technique within prompt engineering. While prompt engineering encompasses all methods of crafting effective prompts (including instructions, context, and formatting), few-shot learning specifically focuses on teaching through examples. Effective prompts often combine few-shot examples with clear instructions and structured output formats.