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Equation 4 · Part 5 · How Mechanistic Interpretability Research Is Actually Done

Symbol w^top

p^θ(y∣aℓ)=σ ⁣(w⊤aℓ+b),θ={w,b},\hat p_\theta(y \mid a_\ell) = \sigma\!\left(w^{\top} a_\ell + b\right), \qquad \theta = \{w, b\},
w⊤w^{\top}

What this part means

wtw^top is one of the signed contributions combined to compute the quantity on the left.

Its job in the formula

wtw^top is one of the signed contributions combined to compute the quantity on the left.

The passage around this formula

The first concrete operation in almost any interpretability project is the least glamorous: run the model forward on a batch of inputs, and at some chosen point in the computation — a residual-stream position, an attention head’s output, a particular MLP layer — copy the activation tensor out before it is overwritten by the next step of the forward pass. This is extraction, and it produces nothing on its own beyond a large table of vectors. What turns it into evidence is probing: fitting a small, separately trained classifier to predict some property of interest directly from those vectors, while the model’s own weights stay frozen. Formally, for an activation aℓa_\ell read out at layer ℓ\ell…

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Learn the underlying idea

An exponent tells how a base is used in multiplication. In x³, x is the base and 3 is the exponent: x³ = x × x × x.

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Sources cited in the surrounding passage

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