Multilinguality of Large Language Models From a Structural Perspective

• URL: http://arxiv.org/abs/2606.01800v1
• Date: Mon, 01 Jun 2026 07:18:09 GMT
• Title: Multilinguality of Large Language Models From a Structural Perspective
• Abstract summary: Large language models (LLMs) have excelled in processing multiple languages through pre- and post-training on multilingual data.<n>Our findings reveal that low-resource languages are structurally more different from English than high- and mid-resource languages.
• Score: 55.097821802807566
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have excelled in processing multiple languages through pre- and post-training on multilingual data, even though English dominates the training data. Prior work focusing on token representations has revealed how those LLMs process non-English text. Although these analyses have provided insightful findings, they fail to capture a structural view, which is an inherent property of language. In this study, we explore the multilinguality of LLMs through representational structural analysis. Our findings reveal that low-resource languages are structurally more different from English than high- and mid-resource languages, and that language-specific post-training alters their structures while preserving inter-language relationships.

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