Learning-From-Mistakes Prompting for Indigenous Language Translation

• URL: http://arxiv.org/abs/2407.13343v1
• Date: Thu, 18 Jul 2024 09:41:20 GMT
• Title: Learning-From-Mistakes Prompting for Indigenous Language Translation
• Authors: You-Cheng Liao, Chen-Jui Yu, Chi-Yi Lin, He-Feng Yun, Yen-Hsiang Wang, Hsiao-Min Li, Yao-Chung Fan,
• Abstract summary: This paper presents techniques to improve extremely low-resourced indigenous language translations. Our approaches are grounded in the use of a datastore consisting of a limited number of parallel translation examples. We harness the potential of LLMs and in-context learning techniques in such a setting for using LLMs as universal translators.
• Score: 3.7790255156708397
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Using large language models, this paper presents techniques to improve extremely low-resourced indigenous language translations. Our approaches are grounded in the use of (1) the presence of a datastore consisting of a limited number of parallel translation examples, (2) the inherent capabilities of LLMs like GPT-3.5, and (3) a word-level translation dictionary. We harness the potential of LLMs and in-context learning techniques in such a setting for using LLMs as universal translators for extremely low-resourced languages. Our methodology hinges on utilizing LLMs as language compilers for selected language pairs, hypothesizing that they could internalize syntactic structures to facilitate accurate translation. We introduce three techniques: KNNPrompting with Retrieved Prompting Context, Chain-of-Thought Prompting and Learningfrom-Mistakes Prompting, with the last method addressing past errors. The evaluation results suggest that, even with limited corpora, LLMs can effectively translate extremely low-resource languages when paired with proper prompting.

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