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What are you picturing a "lesion study on GPT" looking like? Naively I imagine something like "train an SAE on the activations at some layer, then determine how often features activate together and then turn that into a distance metric and do clustering/dimensionality reduction, then ablate clusters of features and see how behavior changes". But I don't know that I'd particularly expect that to show the GPT as being made of many more parts than I actually think said GPT is made of. But also I don't have a super clear mental model of how many "parts" a GPT ... (read more)