Sam Bowman
Survey of NLP Researchers: NLP is contributing to AGI progress; major catastrophe plausible
AI Safety and Neighboring Communities: A Quick-Start Guide, as of Summer 2022
Jobs: Help scale up LM alignment research at NYU
NLP Position Paper: When Combatting Hype, Proceed with Caution
Artificial Sandwiching: When can we test scalable alignment protocols without humans?
A Small Negative Result on Debate
When I converse with junior folks about what qualities they’re missing, they often focus on things like “not being smart enough” or “not being a genius” or “not having a PhD.” It’s interesting to notice differences between what junior folks think they’re missing & what mentors think they’re missing.
This issue is real, it’s the thing that frustrates me most about alignment pipeline-building work in general right now. There are very likely some important formal/theoretical areas of alignment research that really do need to recruit mostly for something like ‘genius’. But a lot more of the active work that’s getting done (and a way more of the hard-to-fill open jobs) depend much, much more on skills 1–5 here much more than on intelligence in that sense.(This is on the margin. Here I’m focused on the actual population of people who tend to be interested in ML alignment research, so I’m baking in the assumption that all of the candidates could, say, get above-average grades in a STEM undergrad degree at a top-100 university if they tried.)
As someone who’s supervised/trained ML researchers for ~8 years now, I’d pretty much always hire someone who’s 90th-percentile on two or three of these skills than someone who’s no better than 70th percentile but has world-class IMO (or IOI) performance or a verified IQ of 160 or some other classic raw intelligence signal.
Concretely, the scaling experiments in the first paper here show that, as models get larger, truncating or deleting the CoT string makes less and less difference to the model’s final output on any given task.
So, stories about CoT faithfulness that depend on the CoT string being load-bearing are no longer very compelling at large scales, and the strings are pretty clearly post hoc in at least some sense.
This doesn’t provide evidence, though, that the string is misleading about the reasoning process that the model is doing, e.g., in the sense that the string implies false counterfactuals about the model’s reasoning. Larger models are also just better at this kind of task, and the tasks all have only one correct answer, so any metric that requires the model to make mistakes in order to demonstrate faithfulness is going to struggle. I think at least for intuitive readings of a term like ‘faithfulness’, this all adds up to the claim in the comment above.
Counterfactual-based metrics, like the ones in the Turpin paper, are less vulnerable to this, and that’s probably where I’d focus if I wanted to push much further on measurement given what we know now. Though we already know from that paper that standard CoT in near-frontier models isn’t reliably faithful by that measure.
We may be able to follow up with a few more results to clarify the takeaways about scaling, and in particular, I think just running a scaling sweep for the perturbed reasoning adding-mistakes metric from the Lanham paper here would clarify things a bit. But the teams behind all three papers have been shifting away from CoT-related work (for good reason I think), so I can’t promise much. I’ll try to fit in a text clarification if the other authors don’t point out a mistake in my reasoning here first...
More organizations like CAIS that aim to recruit established ML talent into alignment research
This is somewhat risky, and should get a lot of oversight. One of the biggest obstacles to discussing safety in academic settings is that academics are increasingly turned off by clumsy, arrogant presentations of the basic arguments for concern.
The BIG-Bench paper that those ‘human’ numbers are coming from (unpublished, quasi-public as TeX here) cautions against taking those average very seriously, without giving complete details about who the humans are or how they were asked/incentivized to behave on tasks that required specialized skills:
Possible confound: Is it plausible that the sycophancy vector is actually just adjusting how much the model conditions its responses on earlier parts of the conversation, beyond the final 10–20 tokens? IIUC, the question is always at the end, and ignoring the earlier context about the person who’s nominally asking the question should generally get you a better answer.
Is anyone working on updating the Biological Anchors Report model based on the updated slopes/requirements here?
+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.)
I agree, though I’ll also add:
- I don’t think our results clearly show that faithfulness goes down with model size, just that there’s less affirmative evidence for faithfulness at larger model sizes, at least in part for predictable reasons related to the metric design. There’s probably more lowish-hanging fruit involving additional experiments focused on scaling. (I realize this disagrees with a point in the post!)
- Between the good-but-not-perfect results here and the alarming results in the Turpin ‘Say What They Think’ paper, I think this paints a pretty discouraging picture of standard CoT as a mechanism for oversight. This isn’t shocking! If we wanted to pursue an approach that relied on something like CoT, and we want to get around this potentially extremely cumbersome sweet-spot issue around scale, I think the next step would be to look for alternate training methods that give you something like CoT/FD/etc. but have better guarantees of faithfulness.
I suspect that these developments look a bit less surprising if you’ve been trying to forecast progress here, and so might be at least partially priced in. Anyhow, the forecast you linked to shows >10% likelihood before spring 2025, three years from now. That’s extraordinarily aggressive compared to (implied) conventional wisdom, and probably a little more aggressive than I’d be as an EA AI prof with an interest in language models and scaling laws.
Just flagging that another cross-post has been collecting some comments: https://www.lesswrong.com/posts/xhKr5KtvdJRssMeJ3/anthropic-s-core-views-on-ai-safety
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: https://twitter.com/sleepinyourhat/status/1585759654478422016
Direct paper link: https://arxiv.org/abs/2210.10860 (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.
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?)
I mostly agree, but it’s messy. I don’t think it’s obvious that a PhD is anywhere near the ideal way to pick up some of these skills, or that earning a PhD definitely means that you’ve picked them up, but PhD programs do include lots of nudges in these directions, and PhD-holders are going to be much stronger than average at most of this.
In particular, like Johannes said, doing a PhD is notoriously hard on mental health for a number of reasons, even at a more-supportive-than-average lab. So to the extent that they teach ‘taking care of your mental health’ and ‘staying motivated when you’re lost’, it’s often by throwing you into stressful, confusing work situations without great resources and giving you the degree if you figure out how to navigate them.
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.)