Debate Helps Weak-to-Strong Generalization
Hao Lang, Fei Huang, Yongbin Li
2025-01-24
Summary
This paper talks about a new way to make AI systems smarter and safer by using a method called 'debate' to help weaker AI models learn from stronger ones, and then using these improved weaker models to guide the stronger models.
What's the problem?
As AI gets smarter, it's becoming harder for humans to supervise and guide them effectively. We're worried that future AI might become so advanced that humans won't be able to properly check if they're doing the right things. This could make AI systems unsafe or unreliable.
What's the solution?
The researchers came up with a clever idea: use a strong AI model to help a weaker AI model get better at understanding things. They do this through a 'debate' process where the strong AI provides information, and the weak AI learns to figure out what's trustworthy. Then, they use this improved weaker AI to guide and improve the stronger AI. It's like having a really smart tutor help a student get better, and then having that improved student give feedback to the tutor.
Why it matters?
This matters because it could be a way to keep AI safe and aligned with human values even as it becomes super intelligent. By using this 'debate' method, we might be able to create AI systems that can improve themselves while still staying under human control. This could lead to smarter, safer AI that we can trust to handle important tasks without worrying about them going off track or doing something we don't want them to do.
Abstract
Common methods for aligning already-capable models with desired behavior rely on the ability of humans to provide supervision. However, future superhuman models will surpass the capability of humans. Therefore, humans will only be able to weakly supervise superhuman models. This expected deficiency of human evaluation would weaken the safety of future AI systems. Scalable oversight and weak-to-strong generalization are two complementary approaches to tackle this issue. In this paper, we attempt to combine the strengths of these two approaches to further improve alignment. Specifically, we investigate ways of improving human supervision with a strong pretrained model and then supervise the strong model with enhanced weak human supervision. To make iterative empirical progress, we consider an analogy: can we use a strong model to improve weak model supervision and then use it to supervise the strong model? We empirically test it by finetuning a small weak model on ground truth labels with the additional help from a large strong model, and then finetuning the strong model on labels generated by the weak model. We find that debate can assist a weak model in extracting trustworthy information from an untrustworthy strong model, which provides leverage as context on samples when training a weak model. We also show that an ensemble of weak models helps exploit long arguments generated by strong model debaters and obtain a more robust supervision estimate. Extensive experiments on the OpenAI weak-to-strong NLP benchmarks show that the combination approach leads to better alignment, which indicates that debate has the potential to help weak-to-strong generalization.