Transferring Natural Language Datasets Between Languages Using Large Language Models for Modern Decision Support and Sci-Tech Analytical Systems
arXiv:2410.14074v2 Announce Type: replace Abstract: The decision-making process to rule R&D relies on information related to current trends in particular research areas. In this work, we investigated how one can use large language models (LLMs) to transfer the dataset and its annotation from one language to another. This is crucial since sharing knowledge between different languages could boost certain underresourced directions in the target language, saving lots of effort in data annotation or quick prototyping. We experiment with English and Russian pairs, translating the DEFT (Definition Ex
The rapid advancement of LLMs has made sophisticated cross-lingual data transfer technically feasible and increasingly necessary for global AI development.
This research outlines a method to significantly reduce the cost and effort of creating AI datasets in under-resourced languages, democratizing AI development and application globally.
The ability to efficiently translate and annotate AI datasets across languages accelerates AI adoption and innovation beyond English-centric ecosystems by lowering data barriers.
- · AI developers in non-English speaking regions
- · Multilingual AI application providers
- · Governments investing in non-English AI capabilities
- · Data annotation services
- · Companies reliant on English-only AI data advantage
Reduced costs and increased speed for developing AI models in diverse languages.
Acceleration of localized AI applications and services, increasing market reach and cultural relevance.
Enhanced competition and innovation in global AI markets, potentially shifting centers of AI development.
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Read at arXiv cs.CL