Learning-From-Mistakes Prompting for Indigenous Language Translation
<|reference_start|>Learning-From-Mistakes Prompting for Indigenous Language Translation: 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.<|reference_end|>
@article{liao2024learning-from-mistakes,
    title={Learning-From-Mistakes Prompting for Indigenous Language Translation},
    author={You-Cheng Liao, Chen-Jui Yu, Chi-Yi Lin, He-Feng Yun, Yen-Hsiang Wang,
  Hsiao-Min Li, Yao-Chung Fan},
    journal={arXiv preprint arXiv:2407.13343},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13343},
    primaryClass={cs.CL}
}