mlsanityData to 14 Oct 2026

Multilingual Language Models Encode Script Over Linguistic Structure

arXiv 2604.05090 ↗First posted 6 Apr 2026cs.CL cs.LGcited by 3

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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

  1. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,998
  2. 2023Sparse Autoencoders Find Highly Interpretable Features in Language Modelscited by 1,420
  3. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 876
  4. 2017Network Dissection: Quantifying Interpretability of Deep Visual Representationscited by 708
  5. 2019How multilingual is Multilingual BERT?cited by 645
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  7. 2019On the Cross-lingual Transferability of Monolingual Representationscited by 578
  8. 2016Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translationcited by 469
  9. 2024Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Modelscited by 435
  10. 2024Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2cited by 394
  11. 2023Low-Resource Languages Jailbreak GPT-4cited by 309
  12. 2024Do Llamas Work in English? On the Latent Language of Multilingual Transformerscited by 281
17 more
  1. 2020Are All Languages Created Equal in Multilingual BERT?cited by 207
  2. 2020When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Modelscited by 131
  3. 2020On the Language Neutrality of Pre-trained Multilingual Representationscited by 85
  4. 2024On the Multilingual Ability of Decoder-based Pre-trained Language Models: Finding and Controlling Language-Specific Neuronscited by 83
  5. 2025Do Multilingual LLMs Think In English?cited by 78
  6. 2019Investigating Multilingual NMT Representations at Scalecited by 68
  7. 2025Large Language Models Share Representations of Latent Grammatical Concepts Across Typologically Diverse Languagescited by 59
  8. 2019How Language-Neutral is Multilingual BERT?cited by 51
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  10. 2025Route Sparse Autoencoder to Interpret Large Language Modelscited by 26
  11. 2025Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipelinecited by 21
  12. 2025Language Arithmetics: Towards Systematic Language Neuron Identification and Manipulationcited by 17
  13. 2025RomanLens: The Role Of Latent Romanization In Multilinguality In LLMscited by 12
  14. 2025What Language(s) Does Aya-23 Think In? How Multilinguality Affects Internal Language Representationscited by 5
  15. 2019Language and Dialect Identification of Cuneiform Textscited by 4
  16. 2025Sparse Autoencoders Can Capture Language-Specific Concepts Across Diverse Languagescited by 4
  17. 2025Unveiling the Influence of Amplifying Language-Specific Neuronscited by 3

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Citing papers per year: 2026: 3.

3 papers.

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  1. 2024The Same But Different: Structural Similarities and Differences in Multilingual Language Modeling
  2. 2025How Language Directions Align with Token Geometry in Multilingual LLMs
  3. 2025Tracing Multilingual Representations in LLMs with Cross-Layer Transcoders
  4. 2025How a Bilingual LM Becomes Bilingual: Tracing Internal Representations with Sparse Autoencoders
  5. 2024Understanding the role of FFNs in driving multilingual behaviour in LLMs
  6. 2026Causal Interventions Reveal Typologically Organized Syntactic Mechanisms in Multilingual Language Models
  7. 2025Cross-Lingual Generalization and Compression: From Language-Specific to Shared Neurons
  8. 2026Cross-Lingual Alignment Without Joint Training: Do Monolingual Language Models Converge on Universal Representations?