Sam Bowman
That’s part of Step 6!
Or, we are repeatedly failing in consistent ways, change plans and try to articulate as best we can why alignment doesn’t seem tractable.
I think we probably do have different priors here on how much we’d be able to trust a pretty broad suite of measures, but I agree with the high-level take. Also relevant:
However, we expect it to also be valuable, to a lesser extent, in many plausible harder worlds where this work could provide the evidence we need about the dangers that lie ahead.
Is there anything you’d be especially excited to use them for? This should be possible, but cumbersome enough that we’d default to waiting until this grows into a full paper (date TBD). My NYU group’s recent paper on a similar debate setup includes a data release, FWIW.
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.
That makes sense, though what’s at stake with that question? In almost every safety-relevant context I can think of, ‘scale’ is just used as a proxy for ‘the best loss I can realistically achieve in a training run’, rather than as something we care about directly.
Yep, that sounds right! The measure we’re using gets noisier with better performance, so even faithfulness-vs-performance breaks down at some point. I think this is mostly an argument to use different metrics and/or tasks if you’re focused on scaling trends.
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...
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’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?)
Just flagging that another cross-post has been collecting some comments: https://www.lesswrong.com/posts/xhKr5KtvdJRssMeJ3/anthropic-s-core-views-on-ai-safety
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.
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.
A New York-based alignment hub that aims to provide talent search and logistical support for NYU Professor Sam Bowman’s planned AI safety research group.
:D
I think my lab is bottlenecked on things other than talent and outside support for now, but there probably is more that could be done to help build/coordinate an alignment research scene in NYC more broadly.
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.
+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: 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.
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.
Thanks! I’ll keep my opinionated/specific overview of the alignment community, but I know governance less well, so I’m happy to defer there.
Thanks! Fixed link.
Thanks! Report coauthor here with a few quick reactions.
On the general vibe that this isn’t a safety case and that we could reasonably do something much more rigorous here, I agree. (And we do describe it as an incomplete pilot in the report, though didn’t spell out much about our plans.) This started because we were more and more worried about us and others missing easy to catch warning signs, and wanted to at least avoid that.
Some additional reactions:
(I realize that many of these won’t be reassuring to a skeptical reader without a lot more detail on the exact evals than I can pull together for this comment, but hopefully they at least clarify how we understood what we were and weren’t accomplishing. More on this meta-point below.)
We also worry about this, and I don’t think we’re fully robust to it, but I think we got a pretty substantial positive from the evaluation, in part because we evaluated similar past models and early snapshots of these models: If Opus were deceptive in this way, it seems very likely (though not certain) that we’d see more slip ups at _some_ point in its developmental trajectory. I’m picturing observations like the model defecting on only the most realistic test cases.
We are at least trying to track sandbagging and situational awareness, and the actual algorithm is closer to ‘keep doing this until we’re reasonably confident that there are no remaining issues severe enough to be worth delaying the launch over.’ This means that our decision includes considerations like ‘we used a lot of information from eval X in training, so we should focus on eval Y.’ We didn’t have a completely strict train-test separation, in that there were no evals that we intentionally ignored until the end, but we did enough along these lines that I’m not concerned.
We did some ad-hoc experiments beyond this, though these were both (i) not rigorous enough to be worth sharing as substantial evidence of safety but (ii) encouraging/unconcerning in their results.
That said, more explicitly selecting test cases for realism is one of our top priorities, and realism is one of my biggest concerns about most of the existing public evals.
The ‘systematic deception’ section pretty clearly addresses this, IMO: Alignment faking entails this kind of deception and goal-hiding. In particular, a decent fraction of the manual and automated red-teaming involved putting models in situations that we thought would lead them to reveal hidden goals or to hide goals in a detectable way. All of this is modulo the genuine concerns about whether moderate-scale behavioral assessments would catch this kind of thing if it were present.
I agree we could have drawn this connection more clearly. We have some moderately confident non-concerning hypotheses about why it’s worse, but no strong assurances.
I think that would be >95%th-percentile obvious relative to the evals we report on here. I think we could have done better on the older eval you mention, though. We’ve been focusing on many-turn interactions, without this kind of tell, that map onto real or plausible use cases, and I expect we’ll keep moving further in that direction.
Yeah, I think this’ll be an ongoing challenge. We’ve found it really informative to spend a lot of time on ad-hoc prompts/honeypots/red-teaming prompts/mini-evals than on a handful of big standardized evals, in part to help address concerns about realism and sandbagging. But big standardized evals are much easier to document. I think a non-pilot version of something like this that relied on much less ‘trust us’ would need to be a few times longer, and this was already a pretty huge effort to prepare.
It seems plausible (not a plan—just an off the cuff thought here) that an alignment safety case ultimately includes something like ‘we let some neutral third-party experts sit down and skim through the tens of thousands of pages of examples and mini-evals that we dumped into our internal docs and slack threads, and they endorse our qualitative conclusions, which fit into our overall argument in XYZ ways.’
I very much want to avoid this, but FWIW, I think it’s still far from than the worst-case scenario, assuming that this role play needs to be actively evoked in some way, like the catgirl etc. personas do. In these cases, you don’t generally get malign reasoning when you’re doing things like running evals or having the model help with monitoring or having the model do safety R&D for you. This leaves you a lot of affordances.
Strongly agree. (If anyone reading this thinks they’re exceptionally good at this kind of very careful long-form prompting work, and has at least _a bit_ of experience that looks like industry RE/SWE work, I’d be interested to talk about evals jobs!)