使用大型语言模型在相关语言之间进行机器翻译的分解提示
Decomposed Prompting for Machine Translation Between Related Languages using Large Language Models
AI总结：
本研究针对相关语言间的机器翻译任务，提出了一种名为DecoMT的新型少样本提示方法，通过利用单调对齐特征将翻译过程分解为词块序列，在多语系实验中显著优于BLOOM等基线模型，平均提升8个chrF++分数。
摘要：
本研究调查了相关语言之间的机器翻译，即同一语系内共享词序和词汇相似性等语言学特征的语言。通过少样本提示进行的机器翻译利用少量翻译对示例来为测试句子生成翻译。此过程要求模型学习如何生成翻译，同时确保维持词元顺序以产生流畅且准确的翻译。我们提出，对于相关语言，可以通过利用此类语言的单调对齐特征来简化机器翻译任务。我们引入了DecoMT，这是一种新颖的少样本提示方法，将翻译过程分解为一系列词块翻译序列。通过在跨越不同语系的多个相关语言对上进行的自动和人工评估，我们证明我们提出的分解提示方法超越了多个已建立的少样本基线方法。例如，DecoMT优于强大的少样本提示BLOOM模型，在所检查的语言中平均提高了8个chrF++分数。
英文摘要：
This study investigates machine translation between related languages i.e., languages within the same family that share linguistic characteristics such as word order and lexical similarity. Machine translation through few-shot prompting leverages a small set of translation pair examples to generate translations for test sentences. This procedure requires the model to learn how to generate translations while simultaneously ensuring that token ordering is maintained to produce a fluent and accurate translation. We propose that for related languages, the task of machine translation can be simplified by leveraging the monotonic alignment characteristic of such languages. We introduce DecoMT, a novel approach of few-shot prompting that decomposes the translation process into a sequence of word chunk translations. Through automatic and human evaluation conducted on multiple related language pairs across various language families, we demonstrate that our proposed approach of decomposed prompting surpasses multiple established few-shot baseline approaches. For example, DecoMT outperforms the strong few-shot prompting BLOOM model with an average improvement of 8 chrF++ scores across the examined languages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
