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Zero-Shot Learning

AI performing tasks without any specific examples, using only general knowledge.

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Definition

Zero-shot learning refers to a model's ability to perform tasks it wasn't explicitly trained for, without being given examples.

How It Works: - Model uses general knowledge from pre-training - Task is described in natural language - No task-specific examples provided

Example Prompt: "Classify this review as positive or negative: 'Great product, highly recommend!'" (No examples of classifications given)

  • **Contrast with:**
  • One-shot: One example provided
  • Few-shot: A few examples provided
  • Fine-tuning: Many examples + training

Why It Matters: - Faster deployment (no training needed) - More flexible (any task) - Lower cost (no labeled data)

Examples

Asking GPT-4 to translate to a language it wasn't specifically trained on.

Related Terms

Prompt Engineering

The practice of designing effective prompts to get better outputs from AI models.

Few-Shot Learning

AI ability to learn new tasks from just a handful of examples.

Transfer Learning

Using knowledge learned on one task to improve performance on a different task.

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