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arXiv · 2402.19167

Teaching Large Language Models an Unseen Language on the Fly

Chen Zhang·Xiao Liu·Jiuheng Lin·Yansong Feng
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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.

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Chen Zhang, Xiao Liu, Jiuheng Lin, Yansong Feng. 2024-02-29. Teaching Large Language Models an Unseen Language on the Fly. https://arxiv.org/abs/2402.19167

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