Wiki Contributions


I’d like to avoid that document being crawled by a web scraper which adds it to a language model’s training corpus.

This may be too late, but it's probably also helpful to put the BIG-Bench "canary string" in the doc as well.

Assuming we're working with near-frontier models (s.t., the cost of training them once is near the limit of what any institution can afford), we presumably can't actually retrain a model without the data. Are there ways to approximate this technique that preserve its appeal?

(Just to check my understanding, this would be a component of a sufficient-but-not-necessary solution, right?)

+1. The combination of the high dollar amount, the subjective criteria, and the panel drawn from the relatively small/insular 'core' AI safety research community mean that I expect this to look pretty fishy to established researchers. Even if the judgments are fair (I think they probably will be!) and the contest yields good work (it might!), I expect the benefit of that to be offset to a pretty significant degree by the red flags this raises about how the AI safety scene deals with money and its connection to mainstream ML research.

(To be fair, I think the Inverse Scaling Prize, which I'm helping with, raises some of these concerns, but the more precise/partially-quantifiable prize rubric, bigger/more diverse panel, and use of additional reviewers outside the panel mitigates them at least partially.)

Update: We did a quick follow-up study adding counterarguments, turning this from single-turn to two-turn debate, as a quick way of probing whether more extensive full-transcript debate experiments on this task would work. The follow-up results were negative. 

Tweet thread here:

Direct paper link: (To appear at the NeurIPS ML Safety workshop.)

We're still broadly optimistic about debate, but not on this task, and not in this time-limited, discussion-limited setting, and we're doing a broader more fail-fast style search of other settings. Stay tuned for more methods and datasets.

Fair. For better or worse, a lot of this variation came from piloting—we got a lot of nudges from pilot participants to move toward framings that were perceived as controversial or up for debate.

I agree that this points in the direction of video becoming increasingly important.

But why assume only 1% is useful? And more importantly, why use only the language data? Even if we don't have the scaling laws, but it seems pretty clear that there's a ton of information in the non-language parts of videos that'd be useful to a general-purpose agent—almost certainly more than in the language parts. (Of course, it'll take more computation to extract the same amount of useful information from video than from text.) 

Thanks! I'll admit that I meant to be asking especially about the toxicity case, though I didn't make that at all clear. As in Charlie's comment, I'm most interested in using this approach as a way to efficiently explore and pilot techniques that we can ultimately adapt back to humans, and text-based interactions seems like a good starting point for that kind of work. 

I don't see a clear picture either way on whether the noisy signal story presents a hard problem that's distinctively alignment oriented.

Thanks! I think I have some sense of what both directions look like, but not enough to know what a concrete starting experiment would look like. What would a minimum viable experiment look like for each?

Is anyone working on updating the Biological Anchors Report model based on the updated slopes/requirements here?

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