The Sequence Opinion #677: Glass-Box Transformers: How Circuits Illuminate Deep Learning’s Inner Workings
Are circuits the final path for AI interpretability or a simple step in the right direction?
Circuits are quickly becoming a favorite of the AI research community to tackle the monumental challenge of interpretability. Today, we are going to explore both the case in favor and against circuits. Specifically, this essay explores how the circuits paradigm has evolved, its application to modern transformer architectures, its promising potential, and its limitations as a complete framework for interpretability.
As transformer-based models push the boundaries of what AI can do, understanding how they work becomes increasingly urgent. Mechanistic interpretability is one of the most rigorous approaches to this challenge, aiming to dissect the internal components of neural networks to reveal the algorithms they implement. At the core of this approach lies the concept of circuits: interconnected sets of neurons or attention heads that jointly compute a specific function. Circuits aren't just about identifying individual neurons with particular behaviors. They map out the interactions between components, allowing us to reconstruct the flow of information through the model for a given task.
Let’s dive in.