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Equation 5 · How Mechanistic Interpretability Research Is Actually Done

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θ\theta

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θ\theta

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with θ\theta fit by ordinary gradient descent to minimise cross-entropy against labelled examples. Alain and Bengio introduced this move under the name “probes” and made an observation that still organises how the technique is used: linear separability of a target property increases monotonically with depth in the networks they studied, which is itself informative about where in the computation a property becomes available for linear readout [ 1 ] .

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