arXiv:2606.14347v1 Announce Type: new Abstract: Large language models exhibit strong multilingual capabilities, however, their internal representations are difficult to interpret. Understanding these interactions is important for ensuring reliable behavior in multilingual systems. Recent work has shown that causal-geometric structure can explain how certain concepts are encoded as approximately linear and separable directions, but whether this framework extends to multilingual models, where language identity is correlated and hierarchical, is underexplored. We apply causal-geometric analysis t
The rapid advancement and widespread adoption of large language models necessitate deeper understanding of their internal workings, especially concerning multilingual capabilities and potential biases.
Understanding how LLMs process different languages will inform the development of more reliable, unbiased, and globally applicable AI systems, impacting their ethical deployment and market reach.
The ability to interpret and potentially manipulate cross-lingual representations within LLMs could lead to more robust AI localization and reduce unexpected behaviors in multilingual deployments.
- · Multilingual AI developers
- · AI ethics researchers
- · Global technology companies
- · NLP researchers
- · Companies relying on black-box multilingual AI
- · Developers neglecting interpretability
Improved interpretability of multilingual LLMs leads to more trustworthy and fair AI applications across different linguistic groups.
Enhanced control over language-specific behaviors in LLMs enables more tailored and effective AI tools for diverse markets.
A deeper understanding of language representation could contribute to more generalized AI, where core knowledge is separated from linguistic encoding.
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Read at arXiv cs.LG