Equation 1 · Running an Interpretability Investigation That Holds Up
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The reason this ordering matters is not procedural fussiness. Zhang and Nanda’s systematic study of activation patching found that the choice of corruption distribution and evaluation metric — decisions usually made informally, late, and sometimes after a first look at the data — can by itself change which components a patching sweep identifies as important [ 3 ] . If the metric is chosen after the sweep, on the grounds that it produced the most legible result, the investigation has stopped testing a hypothesis and started constructing one to fit the data. The same failure has a clean statistical description. Sweep enough components at a nominal per-test false-positive rate and report…
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The reason this ordering matters is not procedural fussiness. Zhang and Nanda’s systematic study of activation patching found that the choice of corruption distribution and evaluation metric — decisions usually made informally, late, and sometimes after a first look at the data — can by itself change which components a patching sweep identifies as important [ 3 ] . If the metric is chosen after the sweep, on the grounds that it produced the most legible result, the investigation has stopped testing a hypothesis and started constructing one to fit the data. The same failure has a clean statistical description. Sweep enough components at a nominal per-test false-positive rate and report only the one that clears threshold, and the probability that at least one clears by chance alone across k independently tested components is
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