Wiki Contributions


the existence of predicates on the world that are easier to evaluate than generate examples of (in the same way that verifying the answer to a problem in NP can be easier than generating it) guarantees that the model should be better at distinguishing between evaluation and deployment than any evaluator can be at tricking it into thinking it's in deployment


Where does the guarantee come from? Why do we know that for this specific problem (generating vs. evaluating whether the model is deployed) it's easier to evaluate? For many problems it's equally difficult, right?

Given that the judge that selects the best argument for BoN is the same as the one that chooses the winner, what is your main takeaway from the fact that ELO increases as you increase N? I see this as mainly a sanity check, but want to check if I'm missing something. 

Another author here! Regarding specifically the 74% vs. 84% numbers - a key takeaway that our error analysis is intended to communicate is that we think a large fraction of the errors judges made in debates were pretty easily solvable with more careful judges, whereas this didn't feel like it was the case with consultancy. 

For example, Julian and I both had 100% accuracy as judges on human debates for the 36 human debates we judged, which was ~20% of all correct human debate judgments. So I'd guess that more careful judges overall could increase debate accuracy to at least 90%, maybe higher, although at that point we start hitting measurement limits from the questions themselves being noisy. 

The issue with early finetuning is that there’s not much that humans can actually select on, because the models aren’t capable enough - it’s really hard for me to say that one string of gibberish is better/worse.

I think the issue with the more general “neocortex prosthesis” is that if AI safety/alignment researchers make this and start using it, every other AI capabilities person will also start using it.

  • unreasonable ^5



I think there's a typographical error - this doesn't link to any footnote for me, and there doesn't appear to be a fifth footnote at the end of the post

Geoffrey and others raised this general problem several years ago (e.g. here)

This link no longer works - I get a permission denied message.

This post is short, but important. The fact that we regularly receive enormously improbable evidence is relevant for a wide variety of areas. It's an integral part of having accurate beliefs, and despite this being such a key idea, it's underappreciated generally (I've only seen this post referenced once, and it's never come up in conversation with other rationalists). 

Has anyone thought about the best ways of intentionally inducing the most likely/worst kinds of misalignment in models, so we can test out alignment strategies on them? I think red teaming kinda fits this, but that’s more focused on eliciting bad behavior, instead of causing a more general misalignment. I’m thinking about something along the lines of “train with RLHF so the model reliably/robustly does bad things, and then we can try to fix that and make the model good/non-harmful”, especially in the sandwiching context where the model is more capable than the overseer.


This is especially relevant for Debate, where we currently do self-play with a helpful assistant-style RLHF'd model, where one of the models is prompted to argue for an incorrect answer. But prompting the model to argue for an incorrect answer is a very simple/rough way of inducing misalignment, which is (at least partially) what we're trying to design Debate to be robust against.

Is there any other reason to think that scalable oversight is possible at all in principle, other the standard complexity theory analogy? I feel like this is forming the basis of a lot of our (and other’s) work in safety, but I haven’t seen work that tries to understand/conceptualize this analogy concretely.

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