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A Systematic Comparison of Multilingual Interpretability Methods Reveals Anisotropy-Driven Failures

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

Multilingual language models develop shared cross-lingual representations, and various interpretability methods claim to quantify this sharing. These methods have been developed largely in isolation, and when they disagree, it is unclear whether the disagreement reflects a property of the model or an artifact of the measurement. We compare four sharing metrics (CKA, ANC, GMM dominance per token, and ILO) across 21 base models from five families (125M-14B parameters) and correlate each with cross-lingual transfer on five downstream tasks. We find that the metrics differ in their quantification

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