SOURCE-LINKED INTELLIGENCE

Resolving the Paradoxes of Cross-lingual Transfer in Multilingual Language Models

CORDIS · observation · Publication date unknown

Resolving the Paradoxes of Cross-lingual Transfer in Multilingual Language Models The technical advances, and resulting societal opportunities, of Large Language Models (LLMs) have principally benefited communities whose primary languages are well-represented in the written data used for training LLMs (e.g., English). While these few high-resource languages are used by many around the world, they do not cover large segments of the global population of 8.2 billion, who collectively speak over 7000 languages. For intelligent natural language systems to be adopted and useful, they must enable interaction in the preferred languages of their users and be knowledgeable of the environments of those users. This expansion of LLM functionality requires re-thinking the cross-lingual transfer paradigm for enabling systems in low-resource languages. In an era where LLMs are knowledge bases, naive reasoners, and interactive agents, our intuitions that held for cross-lingual transfer to linguistic tasks will not extend to transferring regional an

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

1499597

unit

EUR