Singular-direction decomposition and pruning of transformer components

methods / Causal Validation / Singular-direction decomposition and pruning of transformer components

Singular-direction decomposition and pruning of transformer components

Techniqueadvanced

Takes the SVD of a transformer component's augmented weight matrix (e.g. an attention head's OV/QK circuit) to decompose it into orthogonal singular directions, then learns a diagonal mask that prunes most directions while measuring how much task accuracy/behavior survives — turning "one head, one function" into "one head, several functions living in distinct orthogonal directions of its own weight space."

Used in (1 observation)

GPT-2 heads multiplex subfunctions along orthogonal singular directionsmeasured

structure: Linear Direction · models: GPT-2-small · paper: Beyond Components: Singular Vector-Based Interpretability of Transformer Circuits