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        <title>Teaching Large Language Models an Unseen Language on the Fly</title>
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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
%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.
%R 10.18653/v1/2024.findings-acl.519
%U https://aclanthology.org/2024.findings-acl.519
%U https://doi.org/10.18653/v1/2024.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. Association for Computational Linguistics.