Circuit Stability Characterizes Language Model Generalization
This addresses the problem of benchmark saturation and labor-intensive dataset creation for AI researchers, offering a new interpretability-based evaluation method.
The paper tackles the difficulty of evaluating language model capabilities by introducing circuit stability, which measures a model's ability to apply consistent reasoning processes across inputs, and shows through case studies that it can characterize and predict generalization aspects.
Extensively evaluating the capabilities of (large) language models is difficult. Rapid development of state-of-the-art models induce benchmark saturation, while creating more challenging datasets is labor-intensive. Inspired by the recent developments in mechanistic interpretability, we introduce circuit stability as a new way to assess model performance. Circuit stability refers to a model's ability to apply a consistent reasoning process-its circuit-across various inputs. We mathematically formalize circuit stability and circuit equivalence. Then, through three case studies, we empirically show that circuit stability and the lack thereof can characterize and predict different aspects of generalization. Our proposed methods offer a step towards rigorously relating the generality of models to their interpretability.