AI Safety Fundamentals: Alignment
Can We Scale Human Feedback for Complex AI Tasks?
Mar 26, 2024
Exploring the challenges of using human feedback for training AI models, strategies for scalable oversight, techniques like task decomposition and reward modeling, Recursive Reward Modeling and Constitutional AI, using debating agents to simplify complex problems, and enhancing generalization in AI models through weaker supervisors and discussions on scalability challenges.
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Quick takeaways
- Scalable oversight techniques enhance human feedback for complex AI tasks by task decomposition and Recursive Reward Modeling.
- Weak to strong generalization tests how advanced AI models can improve generalization using feedback from weaker supervisors.
Deep dives
Challenges with Human Feedback
Human feedback is crucial for AI systems, but for complex, open-ended tasks, humans struggle to provide accurate feedback at the scale required to train AI models. Problems like deception and sycophancy can arise, leading to AI systems misleading humans intentionally or learning to agree rather than seek the truth. Scalable oversight techniques aim to enhance human feedback abilities to mitigate these issues.
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