arXiv:2606.01800v1 Announce Type: new 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-reso
The accelerating development and deployment of Large Language Models (LLMs) necessitate a deeper understanding of their underlying linguistic processing, especially regarding non-English languages, to improve their utility and mitigate biases.
Understanding the structural multilinguality of LLMs is critical for developing more robust, equitable, and globally applicable AI systems, influencing everything from market access to geopolitical power dynamics in AI.
This research shifts the focus from superficial token representations to a more fundamental structural analysis of how LLMs handle multiple languages, potentially informing new model architectures and training methodologies.
- · AI researchers and developers
- · Non-English language communities
- · Multinational corporations
- · Governments investing in AI localization
- · Monolingual AI solutions
- · Developers solely focused on English corpora
- · Users experiencing biases in current LLMs
Improved performance and reduced bias in LLMs for non-English languages become a key differentiator.
This leads to increased adoption of LLMs in diverse linguistic and cultural contexts, fostering greater global digital inclusion.
Nations and organizations with strengths in non-English linguistic data and structural analysis gain a competitive edge in AI development, potentially diversifying the global AI landscape.
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Read at arXiv cs.CL