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        <title>Teaching Large Language Models an Unseen Language on the Fly</title>
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        <namePart type="given">Chen</namePart>
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    <abstract>Existing large language models struggle to support numerous low-resource languages, particularly the extremely low-resource ones, for which there is minimal training data available for effective parameter updating. We thus investigate whether LLMs can learn a new language on the fly solely through prompting. To study this question, we collect a research suite for Zhuang, a language supported by no LLMs currently. We introduce DiPMT++, a framework for adapting LLMs to unseen languages by in-context learning. Using a dictionary and 5K parallel sentences only, DiPMT++ significantly enhances the performance of GPT-4 from 0 to 16 BLEU for Chinese-to-Zhuang translation and achieves 32 BLEU for Zhuang-to-Chinese translation. We also validate the effectiveness of our framework on Kalamang, another unseen language. Furthermore, we demonstrate the practical utility of DiPMT++ in aiding humans in translating completely unseen languages, which could contribute to the preservation of linguistic diversity.</abstract>
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%0 Conference Proceedings
%T Teaching Large Language Models an Unseen Language on the Fly
%A Zhang, Chen
%A Liu, Xiao
%A Lin, Jiuheng
%A Feng, Yansong
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F zhang-etal-2024-teaching
%X Existing large language models struggle to support numerous low-resource languages, particularly the extremely low-resource ones, for which there is minimal training data available for effective parameter updating. We thus investigate whether LLMs can learn a new language on the fly solely through prompting. To study this question, we collect a research suite for Zhuang, a language supported by no LLMs currently. We introduce DiPMT++, a framework for adapting LLMs to unseen languages by in-context learning. Using a dictionary and 5K parallel sentences only, DiPMT++ significantly enhances the performance of GPT-4 from 0 to 16 BLEU for Chinese-to-Zhuang translation and achieves 32 BLEU for Zhuang-to-Chinese translation. We also validate the effectiveness of our framework on Kalamang, another unseen language. Furthermore, we demonstrate the practical utility of DiPMT++ in aiding humans in translating completely unseen languages, which could contribute to the preservation of linguistic diversity.
%U https://aclanthology.org/2024.findings-acl.519
%P 8783-8800

Markdown (Informal)

[Teaching Large Language Models an Unseen Language on the Fly](https://aclanthology.org/2024.findings-acl.519) (Zhang et al., Findings 2024)

• Teaching Large Language Models an Unseen Language on the Fly (Zhang et al., Findings 2024)

ACL

• Chen Zhang, Xiao Liu, Jiuheng Lin, and Yansong Feng. 2024. Teaching Large Language Models an Unseen Language on the Fly. In Findings of the Association for Computational Linguistics ACL 2024, pages 8783–8800, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.