DePass: Unified Feature Attributing by Simple Decomposed Forward Pass

Attributing the behavior of Transformer models to internal computations is a central challenge in mechanistic interpretability.

We introduce DePass, a unified framework for feature attribution based on a single decomposed forward pass. DePass decomposes hidden states into customized additive components, then propagates them with attention scores and MLP's activations fixed.

It achieves faithful, fine-grained attribution without requiring auxiliary training. We validate DePass across token-level, model component-level, and subspace-level attribution tasks, demonstrating its effectiveness and fidelity. Our experiments highlight its potential to attribute information flow between arbitrary components of a Transformer model.

We hope DePass serves as a foundational tool for broader applications in interpretability.