Optimal ablation for interpretability

Maximilian Li, Lucas Janson

Keywords: mechanistic intepretability, model internals, ablation, activation patching, automatic circuit discovery, causal tracing, tuned lens

Abstract:

Interpretability studies often involve tracing the flow of information through machine learning models to identify specific model components that perform relevant computations for tasks of interest. Prior work quantifies the importance of a model component on a particular task by measuring the impact of performing ablation on that component, or simulating model inference with the component disabled.
We propose a new method, optimal ablation (OA), and show that OA-based component importance has theoretical and empirical advantages over measuring importance via other ablation methods. We also show that OA-based component importance can benefit several downstream interpretability tasks, including circuit discovery, localization of factual recall, and latent prediction.

Primary Area: Interpretability and explainability

Submission Number: 14963