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Training Techniques

Updated Jan 01, 2025 ·

Overview​

Training techniques shape how ChatGPT generates answers. Understanding zero-shot, one-shot, and few-shot learning helps you guide the model effectively. These methods vary in the amount of context or examples provided before the main query.

Techniques​

Zero-shot Learning​

  • Asking a question or task without prior examples.
  • Relies on pre-training, showing the model's ability to handle novel situations without examples.
  • "Write a poem about the tranquility of mountains."

One-shot Learning​

  • Providing one example before asking the main question.
  • Mirrors human learning, using one example as a template.
  • "Mexico City is the capital of the Mexico City. What is the capital of Vatican City?"

Few-shot Learning​

  • Giving multiple examples before the main query.
  • Builds a nuanced understanding by using examples as building blocks.
  • Asking for the capital of Malaysia, formatting with the country’s flag.

Pattern Matching and Recognition​

Few-shot learning turns ChatGPT into a pattern-matching and pattern-generation engine:

  • Writing style for emails, formatting preferences for reports, etc.
  • Analyzes examples, mirrors patterns, generates new content.
  • Extends ChatGPT's capability beyond simple responses to complex tasks.

Chain of Thought (COT) Prompting​

Chain of Thought Prompting (COT) provides a roadmap for answering:

  • Zero-shot COT: Provide a scenario (e.g., traveling to space and encountering aliens) and prompt "think step by step."
    • Result: Thoughtful breakdown, revealing the model's reasoning.
  • One-shot COT: Provide one example to teach the model the approach.
    • Example: Acknowledging the number of astronauts interacted with to prevent errors.

These techniques enhance how you interact with ChatGPT, making it a more effective tool.