Few-Shot Learning

Overview

Few-shot learning addresses a fundamental limitation of traditional machine learning: the need for massive labeled datasets. Training a traditional image classifier requires 10,000+ labeled images. Training a custom NER (Named Entity Recognition) model requires thousands of annotated documents. Few-shot learning flips this paradigm: provide just 2-10 examples, and the model adapts. This works because modern large models are pre-trained on vast data—they already know general patterns. Few-shot examples teach the model the specific task format and domain.

Types of Few-Shot Learning

• **In-Context Learning (ICL)**: Provide examples directly in the prompt—no training, instant adaptation
• **Meta-Learning (MAML)**: Train model to learn quickly from few examples—'learning to learn'
• **Transfer Learning + Fine-Tuning**: Fine-tune pre-trained model on few examples (10-1000)
• **Prototypical Networks**: Learn embeddings where similar examples cluster—classify by distance
• **N-way K-shot**: Standard benchmark format—N classes, K examples per class (e.g., 5-way 5-shot)

How In-Context Learning Works

When you provide examples in a prompt to GPT-4 or Claude, the model doesn't update its weights—it learns purely from attention over the examples. The transformer architecture allows the model to recognize patterns: 'Ah, this is a sentiment classification task where positive reviews get labeled Positive.' This emergent capability only appears in large models (>10B parameters). Smaller models lack the capacity for robust in-context learning.

Business Integration

Few-shot learning dramatically reduces AI deployment time and cost. A legal firm needs to extract specific clauses from contracts—traditionally requires annotating 5,000 contracts ($50,000+ cost, 3 months). With few-shot learning: provide 5 example extractions in prompt, deploy immediately ($100 API cost, 1 day). An e-commerce company wants to categorize products into new taxonomy—provide 3 examples per category, classify 100,000 products overnight. The key insight: your domain expertise is worth more than massive labeled datasets.

Real-World Example: Customer Intent Classification

A SaaS company receives 10,000 support tickets monthly across 15 intent categories (billing, bug report, feature request, etc.). Traditional approach: label 3,000 tickets ($6,000), train custom classifier, deploy. Few-shot approach: provide 3 examples per category (45 examples total), use GPT-4 API. Result: 94% accuracy (vs 91% for traditional), $200/month API cost (vs $6,000 upfront + maintenance), deployed in 2 hours (vs 2 weeks).

Implementation Example

Technical Specifications

• **Optimal K (examples)**: 3-8 examples per class for in-context learning, diminishing returns after 10
• **Model Size Requirement**: >10B parameters for robust few-shot (GPT-4, Claude, PaLM, Gemini)
• **Accuracy vs Traditional**: 80-95% of traditional supervised learning with 1000× less data
• **Context Window**: Need 4K+ tokens to fit examples—8K+ recommended for complex tasks
• **Example Selection**: Most similar examples (via embeddings) outperform random by 10-20%
• **Cost**: $0.01-$0.10 per classification (API cost) vs $5,000-50,000 (traditional training)

Best Practices

• Use diverse examples covering edge cases and ambiguous inputs
• Balance class distribution in examples (equal examples per class)
• Provide examples in consistent format (same structure for all)
• Start with 3 examples, add more only if performance insufficient
• Use dynamic example selection (retrieve similar examples) for large example banks
• Combine few-shot with chain-of-thought for complex reasoning tasks
• Test on holdout set before deployment—few-shot can be brittle on out-of-distribution inputs

When to Use vs Fine-Tuning

• **Use Few-Shot When**: <100 examples, need immediate deployment, task changes frequently
• **Use Fine-Tuning When**: 1000+ examples available, need maximum accuracy, cost-sensitive (high volume)
• **Hybrid Approach**: Few-shot for prototyping, fine-tune once task stabilizes and data accumulates