ChatGPT is primarily trained using a combination of Supervised Learning and Reinforcement Learning, making it a hybrid model rather than purely supervised or unsupervised.
Supervised Learning (SL) Phase
- In the early training stages, human trainers provide labeled examples (input prompts and ideal responses).
- The model learns to generate responses based on these labeled examples.
- Example: A trainer provides a prompt like “What is machine learning?” and gives an ideal response for the model to learn from.
Reinforcement Learning (RL) Phase
- After supervised training, Reinforcement Learning from Human Feedback (RLHF) is used.
- Human reviewers rank different AI-generated responses, and the model learns to improve based on these rankings.
- The goal is to make responses more helpful, accurate, and human-like.
- Example: The model generates multiple answers to a question, and human raters rank them from best to worst.
Why Not Unsupervised Learning?
- Unsupervised learning finds patterns in unlabeled data without explicit guidance.
- While ChatGPT does learn from vast amounts of text data, its training involves human feedback and reinforcement learning, making it not purely unsupervised.
Conclusion
ChatGPT is trained using:
✅ Supervised Learning (for initial training with labeled examples)
✅ Reinforcement Learning (RLHF) (to refine responses based on human feedback)
❌ Not purely Unsupervised Learning (it doesn’t just cluster or find patterns without labels)