After a few months, my biggest regret about this research is that I thought I knew how to interpret the numbers you get out of causal scrubbing, when actually I'm pretty confused about this.
Causal scrubbing takes an explanation and basically says “how good would the model be if the model didn’t rely on any correlations in the input except those named in the explanation?”. When you run causal scrubbing experiments on the induction hypothesis and our paren balance classifier explanation, you get numbers like 20% and 50%.
The obvious next question is: what do these numbers mean? Are those good numbers or bad numbers? Does that mean that the explanations are basically wrong, or mostly right but missing various minor factors?
My current position is “I don’t really know what those numbers mean."
The main way I want to move forward here is to come up with ways of assessing the quality of interpretability explanations which are based on downstream objectives like "can you use your explanation to produce adversarial examples" or "can you use your explanation to distinguish between different mechanisms the model is using", and then use causal-scrubbing-measured explanation quality as the target which you use to find explanations, but then validate the success of the project based on whether the resulting explanations allow you to succeed at your downstream objective.
(I think this is a fairly standard way of doing ML research. E.g., the point of training large language models isn't that we actually wanted models which have low perplexity at predicting webtext, it's that we want models that understand language and can generate plausible completions and so on, and optimizing a model for the former goal is a good way of making a model which is good at the latter goal, but we evaluate our models substantially based on their ability to generate plausible completions rather than by looking at their perplexity.)
I think I was pretty wrong to think that I knew what “loss explained” means; IMO this was a substantial mistake on my part; thanks to various people (e.g. Tao Lin) for really harping on this point.
(We have some more ideas about how to think about "loss explained" than I articulated here, but I don't think they're very satisfying yet.)
In terms of what we can take away from these numbers right now: I think that these numbers seem bad enough that the interpretability explanations we’ve tested don’t seem obviously fine. If the tests had returned numbers like 95%, I’d be intuitively sympathetic to the position “this explanation is basically correct”. But because these numbers are so low, I think we need to instead think of these explanations as “partially correct” and then engage with the question of how good it is to produce explanations which are only partially correct, which requires thinking about questions like “what metrics should we use for partial correctness” and “how do we trade off between those metrics and other desirable features of explanations”.
I have wide error bars here. I think it’s plausible that the explanations we’ve seen so far are basically “good enough” for alignment applications. But I also think it’s plausible that the explanations are incomplete enough that they should roughly be thought of as “so wrong as to be basically useless”, because similarly incomplete explanations will be worthless for alignment applications. And so my current position is “I don’t think we can be confident that current explanations are anywhere near the completeness level where they’d add value when trying to align an AGI”, which is less pessimistic than “current explanations are definitely too incomplete to add value” but still seems like a pretty big problem.
We started realizing that all these explanations were substantially incomplete in around November last year, when we started doing causal scrubbing experiments. At the time, I thought that this meant that we should think of those explanations as “seriously, problematically incomplete”. I’m now much more agnostic.
(I also think that the evidence you're providing is mostly orthogonal to this argument.)
Upon further consideration, I think you're probably right that the causal scrubbing results I pointed at aren't actually about the question we were talking about, my mistake.
but in general, I'd rather advance this dialogue by just writing future papers
Seems like probably the optimal strategy. Thanks again for your thoughts here.
I’m sympathetic to many of your concerns here.
It seems to me like the induction head mechanism as described in A Mathematical Framework is an example of just looking at what a part of a model does on a particular distribution, given that those heads also do some unspecified amount of non-induction behaviors with non-induction mechanisms, as eg discussed here https://www.alignmentforum.org/posts/Si52fuEGSJJTXW9zs/behavioral-and-mechanistic-definitions-often-confuse-ai . (Though there’s a big quantitative difference—the distribution where induction happens is way bigger than eg the distribution where IOI happens.) Do you agree?
I agree with a lot of this post.
Relatedly: in my experience, junior people wildly overestimate the extent to which senior people form confident and sticky negative evaluations of them. I basically never form a confident negative impression of someone's competence from a single interaction with them, and I place pretty substantial probability on people changing substantially over the course of a year or two.
I think that many people perform very differently in different job situations. When someone performs poorly in a job, I usually only update mildly against them performing well in a different role.
But I also don't particularly feel optimistic about a review process either; for that to fix these problems the reviewers would have to be more epistemically competent than the post authors, and that currently doesn't seem likely to happen.
For what it's worth, this is also where I'm at on an Alignment Forum review.
Something like this might be a good idea :) . We've thought about various ideas along these lines. The basic problem is that in such cases, you might be taking the model importantly off distribution, such that it seems to me that your test might fail even if the hypothesis was a correct explanation of how the model worked on-distribution.
nope, but hopefully we'll release one in the next few weeks.
Extremal Goodhart is not differentially a problem for RL vs conditioning, right?
Firstly, a clarification: I don't want to claim that RL-with-KL-penalty policies are the same as the results of conditioning. I want to claim that you need further assumptions about the joint distribution of (overseer score, true utility) in order to know which produces worse Goodhart problems at a particular reward level (and so there's no particular reason to think of RL as worse).
It’s true that minimizing KL subject to a constraint of always exceeding a certain reward threshold would theoretically be equivalent to Bayesian conditioning and therefore equivalent to filtering. [...]
In practice (in the InstructGPT paper, at least), we have a linear mixture of the reward and the global KL penalty. [...]
I thought that using linear mixture of reward and global KL penalty is (because of a Lagrange multiplier argument) the same as having a constraint on reward while minimizing KL penalty?
Maybe the point you're making is that the KL between the policy and the original generative model is different on different inputs? I agree that this means that the RL policy is different than the best-of-n policy, but I don't see why either has predictably worse Goodhart problems.
My current guess is that people who want to use this algorithm should just implement it from scratch themselves--using our software is probably more of a pain than it's worth if you don't already have some reason to use it.