Equation 5 · Running an Interpretability Investigation That Holds Up
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Once the question is scoped, the experiment that tests it is a causal one by convention in this field, and the convention exists for a good reason: a component correlated with a behaviour has told you nothing about whether the model uses it, while a component that changes the behaviour when intervened on has told you something. The standard instrument is activation patching — running the model on a clean input, running it again on a corrupted one, then substituting the corrupted run’s activations into the clean run at a chosen set of components and measuring the change in some behavioural metric m :
Sources cited in the article section
- [3] Towards Best Practices of Activation Patching in Language Models: Metrics and Methods ↗
- [4] Causal Scrubbing: A Method for Rigorously Testing Interpretability Hypotheses ↗
- [5] Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patching ↗
- [6] Towards Automated Circuit Discovery for Mechanistic Interpretability ↗
These citations give research context. Read each source to check which claims it supports.
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