If what you’re saying is “any change to the distribution will change KL” — I think that’s just correct.
This also applies to changes during training where the model is learning to perform better on the objective task.
So we are expecting some amount of KL divergence already.
My claims are:
The cheapest place to hide information (due to KL) are places where the model already has high entropy (ie it is uncertain between many possible outputs)
optimization pressure will try to push this extra information into the cheapest places to hide
the increase in KL won’t be clearly distinguishable from the increase due to increased performance on the task
I think I understand what you're saying, but I want to double check and try laying it out explicitly.
I think I agree with both of these points, but here's my thinking for why I still expect to see this phenomena (and why the article above was simplified to just be "human")
I think the most important point in all of this is that I expect the model prior to be sufficiently close to the human prior -- and I'd be interested in learning more about evidence or research to the contrary.
I think this is worth considering even in cases of things like recursive prompting for a couple of reasons that I haven't organized:
Putting what I think is the most important part of my reply first: I think research into mitigations is premature and instead demonstrating/measuring the phenomena should take priority.
However given that, I think I agree that these are all possible mitigations to the phenomena, in particular (rephrasing your points):
Also I think an important point here is that we can expect to see it with fairly simple techniques -- merely including sampled/generated data in the training set is sufficient, as opposed to it requiring a complicated reinforcement learning algorithm like MuZero.
Did you publish your proposal? I'd be interested in reading it.
Agree that founders are a bit of an exception. Actually that's a bit in the longer version of this when I talk about it in person.
Basically: "The only people who at the very top of large tech companies are either founders or those who were able to climb to the tops of moral mazes".
So my strategic corollary to this is that it's probably weakly better for AI alignment for founders to be in charge of companies longer, and to get replaced less often.
In the case of facebook, even in the face of all of their history of actions, I think on the margin I'd prefer the founder to the median replacement to be leading the company.
(Edit: I don't think founders remaining at the head of a company isn't evidence that the company isn't a moral maze. Also I'm not certain I agree that facebook's pivot couldn't have been done by a moral maze.)
I think there should be a norm about adding the big-bench canary string to any document describing AI evaluations in detail, where you wouldn't want it to be inside that AI's training data.
Maybe in the future we'll have a better tag for "dont train on me", but for now the big bench canary string is the best we have.
This is in addition to things like "maybe don't post it to the public internet" or "maybe don't link to it from public posts" or other ways of ensuring it doesn't end up in training corpora.
I think this is a situation for defense-in-depth.
AGI will probably be deployed by a Moral Maze
Moral Mazes is my favorite management book ever, because instead of "how to be a good manager" it's about "empirical observations of large-scale organizational dynamics involving management".
I wish someone would write an updated version -- a lot has changed (though a lot has stayed the same) since the research for the book was done in the early 1980s.
My take (and the author's take) is that any company of nontrivial size begins to take on the characteristics of a moral maze. It seems to be a pretty good null hypothesis -- any company saying "we aren't/won't become a moral maze" has a pretty huge evidential burden to cross.
I keep this point in mind when thinking about strategy around when it comes time to make deployment decisions about AGI, and deploy AGI. These decisions are going to be made within the context of a moral maze.
To me, this means that some strategies ("everyone in the company has a thorough and complete understanding of AGI risks") will almost certainly fail. I think the only strategies that work well inside of moral mazes will work at all.
To sum up my takes here:
Thanks so much for making this!
I'm hopeful this sort of dataset will grow over time as new sources come about.
In particular, I'd nominate adding MLSN (https://www.alignmentforum.org/posts/R39tGLeETfCZJ4FoE/mlsn-4-many-new-interpretability-papers-virtual-logit) to the list of newsletters in the future.
This seems like an overly alarmist take on what is a pretty old trend of research. Six years ago there was a number of universities working on similar models for the VizDoom competition (IIRC they were won by Intel and Facebook). It seems good to track this kind of research, but IMO the conclusions here are not supported at all by the evidence presented.
I think at this point these feel like empirical questions, which I think would be much more clearly answered by demonstrations or experiments.
Trying to encode an additional penalty on changing non-semantic information is an interesting idea.
However I think you're missing that you don't have the ability to directly compare to a reference LM in cases where you're training to improve on some performance benchmark. During training the model will change its predictions on everything to some degree (both semantic and nonsemantic content).
So your proposed detection will always show some amount of disagreement between the prior and the trained model on weird grammatical patterns as well as conceptual tokens. The question is: "is the difference merely due to the changes to improve performance, or is it also transmitting hidden information"