arXiv:2510.15551v2 Announce Type: replace-cross Abstract: Any piece of knowledge is usually expressed in one or a handful of natural languages on the web or in any large corpus. Large Language Models (LLMs) act as a bridge by acquiring knowledge from a source language and making it accessible when queried using target languages. A cross-lingual gap is a drop in accuracy incurred when querying knowledge in a target language rather than the source language. Existing research focused on modeling or training failures leading to cross-lingual gaps. In this work, we take an alternative view to chara
This paper re-evaluates fundamental assumptions about cross-lingual transfer in LLMs, suggesting a new research direction at a time of rapid LLM development and deployment.
Understanding the statistical basis of cross-lingual gaps can lead to more robust and accurate multilingual LLMs, crucial for global AI applications and data utilization.
The focus of research shifts from solely modeling or training failures to considering the intrinsic statistical properties of how knowledge is distributed across languages.
- · Multilingual LLM developers
- · Users of diverse language data
- · AI research community
- · Monolingual LLM approaches
Improved performance and fairness in LLMs operating across multiple languages.
Reduced investment in language-specific LLM training if cross-lingual transfer becomes more efficient.
Acceleration of global knowledge access and integration through more effective AI translation and summarization capabilities.
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Read at arXiv cs.AI