Seeing Through Circuits: Faithful Mechanistic Interpretability for Vision Transformers

TL;DR

This paper introduces a method called Vi-CD for discovering class-specific and behavior-underlying circuits in vision transformers, enhancing interpretability and transparency of these models.

Contribution

It proposes an effective approach for automatic circuit discovery in vision transformers, extending mechanistic interpretability from language models to vision models.

Findings

Edge-based circuits can be recovered from vision transformers.

Circuits identified are class-specific and relevant to model behavior.

The method aids in understanding and steering model decisions.

Abstract

Transparency of neural networks' internal reasoning is at the heart of interpretability research, adding to trust, safety, and understanding of these models. The field of mechanistic interpretability has recently focused on studying task-specific computational graphs, defined by connections (edges) between model components. Such edge-based circuits have been defined in the context of large language models, yet vision-based approaches so far only consider neuron-based circuits. These tell which information is encoded, but not how it is routed through the complex wiring of a neural network. In this work, we investigate whether useful mechanistic circuits can be identified through computational graphs in vision transformers. We propose an effective method for Automatic Visual Circuit Discovery (Vi-CD) that recovers class-specific circuits for classification, identifies circuits underlying typographic attacks in CLIP, and discovers circuits that lend themselves for steering to correct harmful model behavior. Overall, we find that insightful and actionable edge-based circuits can be recovered from vision transformers, adding transparency to the internal computations of these models.