Comment by jsrozner | Hacker News Reader

Comment by jsrozner | Hacker News Reader

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This is actually not that surprising. Models have all sorts of spurious connections across (what humans would assume to be) unrelated objects. This is a nice result that shows how it can manifest.

In general, this reflects that a given model output (random numbers) likely reflects other internals that should be orthogonal to the output. Even theoretically "factual" outputs (i.e. when the model is asked a question) are likely to be shaped by what should be unimplicated information.

Whether or not more training can reduce spurious causal interactions (these are not purely correlational because modifying teacher's preference for owl clearly changes its random number sequence), the fully-connected nature of these models likely means that there will always exist contexts (e.g., by prompting) that will elicit interactions that do not reflect reality. See also https://arxiv.org/abs/2408.06518.

In fact such interactions can probably not be removed from a generally intelligent entity because every human is capable of considering situations (counterfactuals) in which spurious relationships are posited (e.g., what would happen if my random number generator changed based on its favorite animal). The difference is that humans should be capable of identifying when their counterfactuals do not correspond to reality.

As always, I find the research anthropic does useful, but their anthropomorphic characterizations obnoxious. This is not "subliminal". Models are not conscious and do not have self-awareness. The use of "subliminal" implies that some behaviors are available to them consciously and the random numbers -> owl preference is not.

Do humans exhibit these behaviors? Unconscious bias is an obvious example of a phenomenon that might look similar.

And it is surprising to me that the effect does not show up across models. I hypothesize that there may be some way to elicit it. Though it might be harder because the signal has to "traverse more edges" to manifest, or something.

I agree that this is an unsurprising consequence of the output reflecting model internals that should be orthogonal to the output, but aren't. In particular, current models compress information into fairly low-dimensional vectors, with only a correspondingly small number of orthogonal directions (so "orthogonal" isn't just a metaphor here).

Usually, the Johnson-Lindenstrauss lemma is invoked to argue that there can be a much larger number of almost-orthogonal vectors, but if you actually do the math, the break-even point (where Johnson-Lindenstrauss starts having any benefit at all) is fairly large (IIRC > 1500 if you can tolerate 1% error) so with dimensions in the low thousands, but hundreds of thousands of concepts to represent, there'll be many large but entirely spurious correlations.

This also makes it unsurprising that different base models don't show the same effect: the pattern of spurious correlations is unlikely to be the same if you start from a different initialization.

That math is for random projections? Note that JL lemma is a worst case guarantee and in practice, there's a lot more distortion tolerance than the given bounds would suggest. Concepts tend to live in a space of much lower intrinsic dimensionality than the data's and we often care more about neighbor and rank information than precise pair-wise distances.

Also, JL is only a part of the story for the transformers.

Johnson-Lindenstrauss is an example of a probabilistic existence argument: the probability of a random projection having low error is nonzero, therefore a low-error projection must exist. That doesn't mean any given random projection can be expected to have low error, although if you keep rerolling often enough, you'll eventually find one.

The existence argument does only provide a lower bound on the number of dimensions that can be represented with low error, but there's not necessarily much room for improvement left.

Interesting. I have been thinking that with these high dimensional representations that we have nearly infinite nearly orthogonal dimensions.

One thing that's interesting to me is where / how the model stores the info about a preference for a particular animal, and that this (presumably small) weights change leads to a difference in random numbers that then leaks into a student model.

The fact that this does not happen on models that are separately initialized/ trained could be seen to provide counter evidence to the recently published Platonic hypothesis paper.

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