Abstract
Circuits are supposed to accurately describe how a neural network performs a specific task, but do they really? We evaluate three circuits found in the literature (IOI, greater-than, and docstring) in an adversarial manner, considering inputs where the circuit’s behavior maximally diverges from the full model. Concretely, we measure the KL divergence between the full model’s output and the circuit’s output, calculated through resample ablation, and we analyze the worst-performing inputs. Our results show that the circuits for the IOI and docstring tasks fail to behave similarly to the full model even on completely benign inputs from the original task, indicating that more robust circuits are needed for safety-critical applications.
Adrià Garriga-Alonso
Research Scientist
Adrià Garriga-Alonso is a scientist at FAR.AI, working on understanding what learned optimizers want. Previously he worked at Redwood Research on neural network interpretability, and holds a PhD from the University of Cambridge.