All of scasper's Comments + Replies

Several people seem to be coming to similar conclusions recently (e.g., this recent post). 

I'll add that I have as well and wrote a sequence about it :)

I think this is very exciting, and I'll look forward to seeing how it goes!

Thanks, we will consider adding each of these. We appreciate that you took a look and took the time to help suggest these!

No, I don't think the core advantages of transparency are really unique to RLHF, but in the paper, we list certain things that are specific to RLHF which we think should be disclosed. Thanks.

Sounds right, but the problem seems to be semantic. If understanding is taken to mean a human's comprehension, then I think this is perfectly right. But since the method is mechanistic, it seems difficult nonetheless. 

1davidad (David A. Dalrymple)2mo
Less difficult than ambitious mechanistic interpretability, though, because that requires human comprehension of mechanisms, which is even more difficult.

Thanks -- I agree that this seems like an approach worth doing. I think that at CHAI and/or Redwood there is a little bit of work at least related to this, but don't quote me on that. In general, it seems like if you have a model and then a smaller distilled/otherwise-compressed version of it, there is a lot you can do with them from an alignment perspective. I am not sure how much work has been done in the anomaly detection literature that involves distillation/compression. 

We talked about this over DMs, but I'll post a quick reply for the rest of the world. Thanks for the comment. 

A lot of how this is interpreted depends on what the exact definition of superposition that one uses and whether it applies to entire networks or single layers. But a key thing I want to highlight is that if a layer represents a certain set amount of information about an example, then they layer must have more information per neuron if it's thin than if it's wide. And that is the point I think that the Huang paper helps to make. The fact that ... (read more)

Thanks, +1 to the clarification value of this comment. I appreciate it. I did not have the tied weights in mind when writing this. 

Thanks for the comment.

In general I think that having a deep understanding of small-scale mechanisms can pay off in many different and hard-to-predict ways.

This seems completely plausible to me. But I think that it's a little hand-wavy. In general, I perceive the interpretability agendas that don't involve applied work to be this way. Also, few people would argue that basic insights, to the extent that they are truly explanatory, can be valuable. But I think it is at least very non-obvious that it would be differentiably useful for safety. 

there are a

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In general, I think not. The agent could only make this actively happen to the extent that their internal activation were known to them and able to be actively manipulated by them. This is not impossible, but gradient hacking is a significant challenge. In most learning formalisms such as ERM or solving MDPs, the model's internals are not modeled as a part of the actual algorithm. They're just implementational substrate. 

One idea that comes to mind is to see if a chatbot who is vulnerable to DAN-type prompts could be made to be robust to them by self-distillation on non-DAN-type prompts. 

I'd also really like to see if self-distillation or similar could be used to more effectively scrub away undetectable trojans. https://arxiv.org/abs/2204.06974 

1research_prime_space6mo
1. I don't really think that 1. would be true -- following DAN-style prompts is the minimum complexity solution. You want to act in accordance with the prompt. 2. Backdoors don't emerge naturally. So if it's computationally infeasible to find an input where the original model and the backdoored model differ, then self-distillation on the backdoored model is going to be the same as self-distillation on the original model.  The only scenario where I think self-distillation is useful would be if 1) you train a LLM on a dataset, 2) fine-tune it to be deceptive/power-seeking, and 3) self-distill it on the original dataset, then self-distilled model would likely no longer be deceptive/power-seeking. 

I agree with this take. In general, I would like to see self-distillation, distillation in general, or other network compression techniques be studied more thoroughly for de-agentifying, de-backdooring, and robistifying networks. I think this would work pretty well and probably be pretty tractable to make ground on. 
 

2research_prime_space6mo
I think self-distillation is better than network compression, as it possesses some decently strong theoretical guarantees that you're reducing the complexity of the function. I haven't really seen the same with the latter. But what research do you think would be valuable, other than the obvious (self-distill a deceptive, power-hungry model to see if the negative qualities go away)? 

I buy this value -- FV can augment examplars. And I have never heard anyone ever say that FV is just better than examplars. Instead, I have heard the point that FV should be used alongside exemplars. I think these two things make a good case for their value. But I still believe that more rigorous task-based evaluation and less intuition would have made for a much stronger approach than what happened.

I do not worry a lot about this. It would be a problem. But some methods are model-agnostic and would transfer fine. Some other methods have close analogs for other architectures. For example, ROME is specific to transformers, but causal tracing and rank one editing are more general principles that are not. 

Thanks for the comment. I appreciate how thorough and clear it is. 

Knowing "what deception looks like" - the analogue of knowing the target class of a trojan in a classifier - is a problem.

Agreed. This totally might be the most important part of combatting deceptive alignment. I think of this as somewhat separate from what diagnostic approaches like MI are  equipped to do. Knowing what deception looks like seems more of an outer alignment problem. While knowing what will make the model even badly even if it seems to be aligned is more of an inner... (read more)

Thanks. See also EIS VIII.

Could you give an example of a case of deception that is quite unlike a trojan? Maybe we have different definitions. Maybe I'm not accounting for something. Either way, it seems useful to figure out the disagreement.  

2Charlie Steiner7mo
I'm slowly making my way through these, so I'll leave you a more complete comment after I read post 8.

Yes, it does show the ground truth.

The goal of the challenge is not to find the labels, but to find the program that explains them using MI tools. In the post, when I say labeling "function", I really mean labeling "program" in this case.

The MNIST CNN was trained only on the 50k training examples. 

I did not guarantee that the models had perfect train accuracy. I don't believe they did. 

I think that any interpretability tools are allowed. Saliency maps are fine. But to 'win,' a submission needs to come with a mechanistic explanation and sufficient evidence for it. It is possible to beat this challenge by using non mechanistic techniques to figure out the labeling function and then using that knowledge to find mechanisms by which the networks classify the data. 

At the end of the day, I (and possibly Neel) will have the final say in things. 

Thanks :)

This seems interesting. I do not know of steelmen for isolation, renaming, reinventing, etc. What is yours?

Thanks. I'll talk in some depth about causal scrubbing in two of the upcoming posts which narrow down discussion specifically to AI safety work. I think it's a highly valuable way of measuring how well a hypothesis seems to explain a network, but there are some pitfalls with it to be aware of. 

Correct. I intended the 3 paragraphs in that comment to be separate thoughts. Sorry.

There are not that many that I don't think are fungible with interpretability work :) 

But I would describe most outer alignment work to be sufficiently different...

Interesting to know that about the plan. I have assumed that remix was in large part about getting more people into this type of work. But I'm interested in the conclusions and current views on it. Is there a post reflecting on how it went and what lessons were learned from it?

3Lawrence Chan8mo
Don't think there have been public writeups, but here's two relevant manifold markets:    

I think that my personal thoughts on capabilities externalities are reflected well in this post

I'd also note that this concern isn't very unique to interpretability work but applies to alignment work in general. And in comparison to other alignment techniques, I think that the downside risks of interpretability tools are most likely lower than those of stuff like RLHF. Most theories of change for interpretability helping with AI safety involve engineering work at some point in time, so I would expect that most interpretability researchers have simil... (read more)

1Lawrence Chan8mo
Fwiw this does not seem to be in the Dan Hendrycks post you linked!

I think that (1) is interesting. This sounds plausible, but I do not know of any examples of this perspective being fleshed out. Do you know of any posts on this?

2Neel Nanda8mo
I don't know if they'd put it like this, but IMO solving/understanding superposition is an important part of being able to really grapple with circuits in language models, and this is why it's a focus of the Anthropic interp team
3Ryan Greenblatt8mo
I would argue that ARC's research is justified by (1) (roughly speaking). Sadly, I don't think that there are enough posts on their current plans for this to be clear or easy for me to point at. There might be some posts coming out soon.

Thanks! I discuss in the second post of the sequence why I lump ARC's work in with human-centered interpretability. 

This is an interesting point. But I'm not convinced, at least immediately, that this isn't likely to be largely a matter of AI governance. 

There is a long list of governance strategies that aren't specific to AI that can help us handle perpetual risk. But there is also a long list of strategies that are. I think that all of the things I mentioned under strategy 2 have AI specific examples:

establishing regulatory agencies, auditing companies, auditing models, creating painful bureaucracy around building risky AI systems, influencing hardware supply cha

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I see your point here about generally strengthening methods versus specifically refining them for an application. I think this is a useful distinction. But I'm all about putting a good amount of emphasis on connections between this work and different applications. I feel like at this point, we have probably cruxed. 

Thanks for the comment. I'm inclined to disagree though. The application was for detecting deceptiveness. But the method was a form of contrastive probing.  And one could use a similar approach for significantly different applications. 

1Robert Kirk9mo
This feels kind of like a semantic disagreement to me. To ground it, it's probably worth considering whether further research on the CCS-style work I posted would also be useful for self-driving cards (or other applications). I think that would depend on whether the work improves the robustness of the contrastive probing regardless of what is being probed for (which would be generically useful), or whether it improves the probing specifically for truthfulness in systems that have a conception of truthfulness , possibly by improving the constraints or adding additional constraints (less useful for other systems). I think both would be good, but I'm uncertain which would be more useful to pursue if one is motivated by reducing x-risk from misalignment.

Thanks!

We do hope that these are just special cases and that our methods will resolve a broader set of problems.

I hope so too. And I would expect this to be the case for good solutions. 

Whether they are based on mechanistic interpretability, probing, other interpretability tools, adversaries, relaxed adversaries, or red-teaming, I would expect methods that are good at detecting goal misgeneralization or deceptive alignment to also be useful for self-driving cars and other issues. At the end of the day, any misaligned model will have a bug -- some set ... (read more)

1Robert Kirk9mo
One example: A tool that is designed to detect whether a model is being truthful (correctly representing what it knows about the world to the user), perhaps based on specific properties of truthfulness (for example see https://www.alignmentforum.org/posts/L4anhrxjv8j2yRKKp/how-discovering-latent-knowledge-in-language-models-without) doesn't seem like it would be useful for improving the reliability of self-driving cars, as likely self-driving cars aren't misaligned in the sense that they could drive perfectly safely but choose not to, but rather are just unable to drive perfectly safely because some of their internal (learned) systems aren't sufficiently robust.

Nice post. I'll nitpick one thing. 

In the paper, the approach was based on training a linear probe to differentiate between true and untrue question answer pairs. I believe I mentioned to you at one point that "contrastive" seems more precise than "unsupervised" to describe this method. To carry out an approach like this, it's not enough to have or create a bunch of data. One needs the ability to reliably find subsets of the data that contrast. In general, this would be as hard as labeling. But when using boolean questions paired with "yes" and "no" a... (read more)

Hi Paul, thanks. Nice reading this reply. I like your points here.

Some of what I say here might reveal a lack of keeping up well with ARC. But as someone who works primarily on interpretability, the thought of mechanistic anomaly detection techniques that are not useful for use in today's vision or language models seems surprising to me. Is there anything you can point me to to help me understand why an interpretability/anomaly detection tool that's useful for ASI or something might not be useful for cars?

5Paul Christiano9mo
We are mostly thinking about interpretability and anomaly detection designed to resolve two problems (see here): * Maybe the AI thinks about the world in a wildly different way than humans and translates into human concepts by asking "what would a human say?" instead of "what is actually true?" This leads to bad generalization when we consider cases where the AI system plans to achieve a goal and has the option of permanently fooling humans. But that problem is very unlikely to be serious for self-driving cars, because we can acquire ground truth data for the relevant queries. On top of that it just doesn't seem they will think about physical reality in such an alien way. * Maybe an AI system explicitly understands that it is being evaluated, and then behaves differently if it later comes to believe that it is free to act arbitrarily in the world. We do hope that these are just special cases and that our methods will resolve a broader set of problems, but these two special cases loom really large. On the other hand, it's much less clear whether realistic failures for self-driving cars will involve the kind of mechanism distinctions we are trying to detect. Also: there are just way more pressing problems for self-driving cars. And on top of all that, we are taking a very theoretical approach precisely because we are worried it may be difficult to study these problems until a future time very close to when they become catastrophic. Overall I think that if someone looked at our research agenda and viewed it as an attempt to respond to reliability failures in existing models, the correct reaction should be more like "Why are they doing all of this instead of just empirically investigating which failures are most important for self-driving cars and then thinking about how to address those?" There's still a case for doing more fundamental theoretical research even if you are interested in more prosaic reliability failures, but (i) it's qua
1Robert Kirk9mo
Not Paul, but some possibilities why ARC's work wouldn't be relevant for self-driving cars: * The stuff Paul said about them aiming at understanding quite simple human values (don't kill us all, maintain our decision-making power) rather than subtle things. It's likely for self-driving cars we're more concerned with high reliability and hence would need to be quite specific. E.g., maybe ARC's approach could discern whether a car understands whether it's driving on the road or not (seems like a fairly simple concept), but not whether it's driving in a riskier way than humans in specific scenarios. * One of the problems that I think ARC is worried about is ontology identification, which seems like a meaningfully different problem for sub-human systems (whose ontologies are worse than ours, so in theory could be injected into ours) than for human-level or super-human systems (where that may not hold). Hence focusing on the super-human case would look weird and possibly not helpful for the subhuman case, although it would be great if they could solve all the cases in full generality. * Maybe once it works ARC's approach could inform empirical work which helps with self-driving cars, but if you were focused on actually doing the thing for cars you'd just aim directly at that, whereas ARC's approach would be a very roundabout and needlessly complex and theoretical way of solving the problem (this may or may not actually be the case, maybe solving this for self-driving cars is actually fundamentally difficult in the same way as for ASI, but it seems less likely).

No disagreements substance-wise. But I'd add that I think work to avoid scary autonomous weapons is likely at least as important as recommender systems. If this post's reason #1 were the only reason for working on nerartermist AI stuff, then it would probably be like a lot of other very worthy but likely not top-tier impactful issues. But I see it as emphasis-worthy icing on the cake given #2 and #3. 

1Neel Nanda9mo
Cool, agreed. Maybe my main objection is just that I'd have put it last not first, but this is a nit-pick

I think this seems really cool. I'm excited about this. The kind of thing that I would hope to see next is a demonstration that this method can be useful for modifying the transformer in a way that induces a predictable change in the network's behavior. For example, if you identify a certain type of behavior like toxicity or discussion of certain topics, can you use these interpretations to guide updates to the weights of the model that cause it to no longer say these types of things according to a classifier for them? 

My answer to this is actually tucked into one paragraph on the 10th page of the paper: "This type of approach is valuable...reverse engineering a system". We cite examples of papers that have used interpretability tools to generate novel adversaries, aid in manually-finetuning a network to induce a predictable change, or reverse engineer a network. Here they are.

Making adversaries:

https://distill.pub/2019/activation-atlas/

https://arxiv.org/abs/2110.03605

https://arxiv.org/abs/1811.12231

https://arxiv.org/abs/2201.11114

https://arxiv.org/abs/2206.14754

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