Probing the Geometry of Color Representations in Multilingual Language Models

Abstract:Do multilingual language models learn a universal internal geometry for color, and is it perceptually grounded or primarily linguistic? We decompose color representations into two geometric axes: (P1) alignment to perceptual color space, and (P2) cross-lingual symmetry. Analyzing layerwise embeddings across models and multilingual datasets, we find that perceptual grounding is weak: alignment to CIE-LAB peaks in intermediate layers, then collapses toward output layers. Cross-lingual symmetry degrades with depth, suggesting later layers encode language-specific structure. Functionally, we show that geometric properties predict task performance: models achieve 96.7% accuracy on color classification at layers where P1 alignment peaks, with P1 alignment (r=0.70) significantly correlating with downstream capabilities. Our results reveal a dissociation: text-only LMs learn universal color-language geometry, not universal color-perception geometry, providing diagnostics for where perceptual structure is preserved in language models.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2610.18331 [cs.CL]

(or arXiv:2610.18331v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.18331 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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