Exemplar Partitioning for Mechanistic Interpretability

We introduce Exemplar Partitioning (EP), an unsupervised method for building interpretable feature dictionaries from large language model activations. An EP dictionary is a Voronoi partition of activation space, built by leader-clustering streamed activations within a distance threshold. Each region is defined by an observed exemplar and an average of its member activations, which define region membership and provide directions for intervention. Dictionary size is determined by the activation stream at the chosen threshold rather than pre-selected. Exemplars link regions to observed inputs, allowing dictionaries built from the same input stream to be compared across layers, training check...

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