Comment by bluejay2387
4 days ago
The claims seem to be an overreach of what is actually proven. We've known about progressive feature complexity and the impacts of early training structures on later training for a really long time (since CNN's, The Lottery Ticket Hypothesis, etc...) and neither of those phenomenon are mutually exclusive with parameter overloading. The Multimodal Gap work (https://arxiv.org/abs/2203.02053) seems to imply networks have a tendency towards condensed representations that are clearly not semantic and a bunch of other considerations are already on the table for discussion. I don't think these have all been considered sufficiently to start claiming we are around the corner from mechanistic interpretation.
Nick isn't trying to re-claim progressive feature complexity. He's saying that many polysemantic examples were misread because we assumed neurons packed unrelated things together. But also I think what he's claiming if you read the full blog post isn't inconsistent with that paper, where that paper actually isn't itself claiming semantic condensing but geometric condensing. That paper doesn't analyze neuronal firing patterns whereas Nick's trying to analyze what fires together and what doesn't fire.
>This is interesting work, but I don't think it's a significant update to the big picture of interpretability.
>I'm on board with the broad idea of doing exploratory interpretability with neurons, e.g. neuron-based attribution graphs, for pragmatic problems like why did a model do a sketchy thing? I think this work provides some reasonable evidence, in addition to good earlier work from Transluce, that this might actually work for practical purposes, and has some advantages over the standard alternatives like SAEs/transcoders. I do not agree with some people's grander inferences like that this is substantial progress towards ambitious reverse engineering, solving interpretability etc.
https://xxcancel.com/NeelNanda5/status/2110875974296654062#m