Abstract
While Neural Machine Translation (NMT) represents the leading approach to Machine Translation (MT), the outputs of NMT models still require translation post-editing to rectify errors and enhance quality, particularly under critical settings. In this work, we formalize the task of translation post-editing with Large Language Models (LLMs) and explore the use of GPT-4 to automatically post-edit NMT outputs across several language pairs. Our results demonstrate that GPT-4 is adept at translation post-editing and produces meaningful edits even when the target language is not English. Notably, we achieve state-of-the-art performance on WMT-22 English-Chinese, English-German, Chinese-English and German-English language pairs using GPT-4 based post-editing, as evaluated by state-of-the-art MT quality metrics.
Abstract (translated)
虽然神经网络机器翻译(NMT)代表了机器翻译(MT)的主要方法，但NMT模型的输出仍然需要翻译后编辑来纠正错误和提高质量，特别是在关键环境中。在本研究中，我们使用大型语言模型(LLM) formal 翻译后编辑任务，并探索使用GPT-4自动编辑多个语言对的NMT输出。我们的结果表明，GPT-4擅长翻译后编辑，即使目标语言不是英语。值得注意的是，我们使用GPT-4基于后编辑的方法在 WMT-22 英语-中文、英语-德语、中文-英语和德语-英语语言对上取得了最先进的性能，并使用了最先进的MT质量度量进行评估。
URL
https://arxiv.org/abs/2305.14878
