Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet

I find Anthorpic's work on mech interp fascinating in general. Their initial towards monosemanticity paper was highly surprising, and so is this with the ability to scale to a real production-scale LLM.

My observation is, and this may be more philosophical than technical: this process of "decomposing" middle-layer activations with a sparse autoencoder -- is it capturing accurately underlying features in the latent space of the network, or are we drawing order from chaos, imposing monosemanticity where there aren't any? Or to put it another way, were the features always there, learnt by training, or are we doing post-hoc rationalisations -- where the features exist because that's how we defined the autoencoders' dictionaries, and we learn only what we wanted to learn? Are the alien minds of LLMs truly also operating on a similar semantic space as ours, or are we reading tea leaves and seeing what we want to see?

Maybe this distinction doesn't even make sense to begin with; concepts are made by man, if clamping one of these features modifies outputs in a way that is understandable to humans, it doesn't matter if it's capturing some kind of underlying cluster in the latent space of the model. But I do think it's an interesting idea to ponder.

I'm allergic to latent space because I've yet to find any meaning to it beyond poetics, I develop an acute allergy when it's explicitly related to visually dimensional ideas like clustering.

I'll make a probably bad analogy: does your mindmap place things near each other like my mindmap?

To which I'd say, probably not, mindmaps are very personal, and the more complex we put on ours, the more personal and arbitrary they would be, and the less import the visuals would have

ex. if we have 3 million things on both our mindmaps, it's peering too closely to wonder why you put mcdonalds closer to kids food than restaurants, and you have restaurants in the top left, whereas I put it closer to kids foods, in the top mid left.

Why would that matter? The absolute orientation of the mind map doesn't matter - maybe my map is actually very close to yours, subject to some rotation and mirroring?

More than that, I'd think a better 2D analogy for the latent space is a force-directed graph that you keep shaking as you add things to it. It doesn't seem unlikely for two such graphs, constructed in different order, to still end up identical in the end.

Thirdly:

> if we have 3 million things on both our mindmaps, it's peering too closely to wonder why you put mcdonalds closer to kids food than restaurants, and you have restaurants in the top left, whereas I put it closer to kids foods, in the top mid left.

In 2D analogy, maybe, but that's because of limited space. In 20 000 D analogy, there's no reason for our mind maps to meaningfully differ here; there's enough dimensions that terms can be close to other terms for any relationship you could think of.

> there's no reason for our mind maps to meaningfully differ here

Yes there is.

If you think all training runs converge to the same bits given the same output size, I would again stress that the visual dimensions analogy is poetics and extremely tortured.

If you're making the weaker claim that generally concepts sort themselves into a space and they're generally sorted the same way if we have the same training data. Or rotational symmetry means any differences don't matter. Or location doesn't matter at all...we're in poetics.

Something that really sold me when I was in a similar mindset was word2vec's king - man + woman = queen wasn't actually real or in the model. Just a way of explaining it simply.

Another thought from my physics days: try visualizing 4D. Some people do claim to, after much effort, but in my experience they're unserious, i.e. I didn't see PhDs or masters students in my program claiming this. No one tries claiming they can see in 5D.