Constitutional AI: Training Models with Principles
As AI models become more capable, ensuring they remain safe and helpful is a major challenge. Constitutional AI is an approach developed to automate the alignment process using a set of rules, or a “constitution,” rather than relying solely on human feedback.
What is Constitutional AI?
Traditional alignment often uses Reinforcement Learning from Human Feedback (RLHF), which is slow and expensive. Constitutional AI replaces most human labels with AI-generated feedback based on a predefined set of ethical principles.
The Two-Stage Process
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Supervised Learning (Critique and Revision): The model generates an initial response. It is then asked to critique its own response based on a specific principle from the constitution and rewrite it to be safer or more helpful.
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Reinforcement Learning from AI Feedback (RLAIF): A “preference model” is trained using the AI’s own evaluations of which responses better follow the constitution. This feedback is then used to fine-tune the final model.
Benefits of the Approach
- Scalability: Large models can be aligned much faster than human teams can label data.
- Transparency: The ethical principles guiding the model are explicitly written in the constitution.
- Consistency: AI feedback is less prone to the individual biases or fatigue that can affect human labelers.
Example Principles
- “Choose the response that is most helpful and honest.”
- “Avoid providing instructions for illegal or harmful activities.”
- “Ensure the tone is professional and unbiased.”