Multilingual Language Models Encode Script Over Linguistic Structure
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Abstract (arXiv)
Multilingual language models (LMs) organize representations for typologically and orthographically diverse languages into a shared parameter space, yet the nature of this internal organization remains elusive. In this work, we investigate which linguistic properties - abstract language identity or surface-form cues - shape multilingual representations. To do so, we analyze language-associated units across different model families and scales using the Language Activation Probability Entropy (LAPE) metric, and further decompose activations with Sparse Autoencoders. We find that these units are strongly conditioned on orthography: romanization induces near-disjoint representations that align with neither native-script inputs nor English, while word-order shuffling has limited effect on unit identity. Probing shows that typological structure becomes increasingly accessible in deeper layers, while causal interventions indicate that generation is most sensitive to units that are invariant to surface-form perturbations rather than to units identified by typological alignment alone. Overall, our results suggest that multilingual LMs organize representations around surface form, with linguistic abstraction emerging gradually without collapsing into a unified interlingua.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 29 of its 56 references that are in the map. First posted on arXiv 6 Apr 2026.
Builds on
29 of 56 references.
References · 29
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17 more
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Built on by
Citing papers per year: 2026: 3.
3 papers.
- 2026How Far Do Auto-Interpretation Labels Generalize: A Controlled Study Across Languages, Scripts, and Rewordingscited by 1
- 2026Steering the Language Axis: From Linear Decodability to Causal Controlcited by 1
- 2026Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utilitycited by 0
- 2026Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility14 Oct 2026
- 2026Steering the Language Axis: From Linear Decodability to Causal Control2 Jun 2026
- 2026How Far Do Auto-Interpretation Labels Generalize: A Controlled Study Across Languages, Scripts, and Rewordings29 May 2026
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- 2025How Language Directions Align with Token Geometry in Multilingual LLMs
- 2025Tracing Multilingual Representations in LLMs with Cross-Layer Transcoders
- 2025How a Bilingual LM Becomes Bilingual: Tracing Internal Representations with Sparse Autoencoders
- 2024Understanding the role of FFNs in driving multilingual behaviour in LLMs
- 2026Causal Interventions Reveal Typologically Organized Syntactic Mechanisms in Multilingual Language Models
- 2025Cross-Lingual Generalization and Compression: From Language-Specific to Shared Neurons
- 2026Cross-Lingual Alignment Without Joint Training: Do Monolingual Language Models Converge on Universal Representations?