arXiv:2605.26683v1 Announce Type: new Abstract: Cross-lingual transfer in language models is difficult to study in natural corpora because lexical overlap, morphology, data imbalance, and tokenization are entangled. We introduce an in-vitro framework with two procedurally generated languages that share the same ontology, typed grammar, and compositional structure, but differ in surface realization. This lets us independently vary lexical distance, minority-language proportion, tokenizer training regime, and vocabulary size, while evaluating transfer on a masked minority-language condition whos
This research is emerging now as models grow more sophisticated, necessitating deeper understanding of their cross-lingual capabilities, particularly for global deployment.
Understanding cross-lingual generalization is crucial for developing truly universal language models, impacting their accessibility and utility across diverse linguistic populations.
This research introduces a novel, controlled framework for studying a fundamental challenge in LLMs, which could lead to more robust and equitable AI systems.
- · AI researchers
- · Multilingual AI platforms
- · Developing economies (non-English speaking)
- · Academia
- · Monolingual AI approaches
- · AI models with poor generalization
Improved methods for training cross-lingual language models will emerge.
AI services will become more effective and accessible to a wider global audience, reducing language barriers.
This could lead to a 'flattening' of the digital linguistic landscape, increasing AI's pervasive influence across cultures and economies.
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