arXiv:2605.23885v1 Announce Type: new Abstract: Cross-lingual knowledge transfer is critical for building high-performing multilingual language models for languages with insufficient training data. When target language data is scarce, the knowledge required for many downstream tasks involving scientific reasoning, commonsense inference, and world knowledge must be acquired primarily from the high-resource language, making effective knowledge transfer essential. Existing methods for improving such cross-lingual knowledge transfer require large amounts of parallel data, translation systems, auxi
The proliferation of AI systems across diverse linguistic contexts necessitates solutions for knowledge transfer to underserved languages, especially as global AI adoption expands.
Improving cross-lingual knowledge transfer under data constraints can democratize AI access and performance, reducing dependency on high-resource languages and supporting equitable AI development.
New methods for multilingual knowledge transfer, particularly those using lexical interventions, could make high-performing multilingual language models more accessible for languages with limited training data.
- · AI developers in non-English speaking regions
- · Multilingual AI users
- · Local language content creators
- · Emerging market economies
- · Monolingual AI solutions
- · AI models heavily reliant on parallel data
More robust and accurate AI applications become available in a wider array of languages and cultures.
This could accelerate the development of localized AI services and drive AI adoption in previously underserved markets.
Reduced linguistic fragmentation in AI capability could lessen the digital divide and foster greater global AI innovation outside existing dominant language ecosystems.
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Read at arXiv cs.CL