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Focusing on multilingual, multicultural, pre-trained large language models and their practical use through fine-tuning and prompt engineering, Wang and Smith demonstrate how to apply this new technology in areas such as information retrieval, semantic webs, and retrieval augmented generation, to improve both human productivity and machine intelligence. Finally, they discuss the human impact of language technologies in the cultural context, and provide an AI competence framework for users to design their own learning journey.
This innovative text is essential reading for all students, professionals, and researchers in language, linguistics, and related areas looking to understand how to integrate multilingual and multicultural artificial intelligence technology into their research and practice.
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Peng Wang is an IT analyst and the chair of the Multilingual AI Track. She is the co-author of Machine Learning in Translation.
Pete Smith is Professor of Modern Languages at the University of Texas Arlington, where he also serves as Chief Analytics and Data Officer.
