Foundation Models for Few-Shot Learning: Adapting to New Tasks Instantly

The ability to learn from few examples is a hallmark of human intelligence. Our research enables AI systems to do the same.

The Few-Shot Challenge

Traditional ML requires thousands of examples. In contrast, humans can:

• Learn new concepts from 1-2 examples
• Generalize systematically
• Compose known concepts
• Transfer across domains

Approach

We leverage foundation models through:

In-Context Learning

• Provide examples in the prompt
• No weight updates required
• Instant adaptation
• Task specification via demonstration

Meta-Learning

• Learn to learn from pretraining
• Fast adaptation mechanisms
• Task-agnostic representations
• Optimization-based methods

Retrieval Augmentation

• Find similar examples
• Contextual retrieval
• Knowledge base integration
• Dynamic few-shot selection

Evaluation

Real-World Impact

Enabling applications with:

• Limited training data
• Rapidly changing categories
• Long-tail distributions
• User personalization