alt Hacker NewsOk, I'll bite. Let's assume a modern cutting edge model but even with fairly standard GQA attention, and something obviously bigger than just monosemantic features per neuron.
Based on any reasonable mechanistic interpretability understanding of this model, what's preventing a circuit/feature with polysemanticity from representing a specific error in your code?
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Do you actually understand ML? Or are you just parroting things you don't quite understand?
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Polysemantic features in modern transformer architectures (e.g., with grouped-query attention) are not discretely addressable, semantically stable units but superposed, context-dependent activation patterns distributed across layers and attention heads, so there is no principled mechanism by which a single circuit or feature can reliably and specifically encode “a particular code error” in a way that is isolable, causally attributable, and consistently retrievable across inputs.
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Way to go in showing you want a discussion, good job.
Ok, let's chew on that. "reasonable mechanistic interpretability understanding" and "semantic" are carrying a lot of weight. I think nobody understands what's happening in these models -irrespective of narrative building from the pieces. On the macro level, everyone can see simple logical flaws.