Large language models have shown surprising performances in understanding instructions and performing natural language tasks. In this paper, we propose iterative translation refinement to leverage the power of large language models for more natural translation and post-editing. We show that by simply involving a large language model in an iterative process, the output quality improves beyond mere translation. Extensive test scenarios with GPT-3.5 reveal that although iterations reduce string-based metric scores, neural metrics indicate comparable if not improved translation quality. Further, human evaluations demonstrate that our method effectively reduces translationese compared to initial GPT translations and even human references, especially for into-English directions. Ablation studies underscore the importance of anchoring the refinement process to the source input and a reasonable initial translation.

翻译：大语言模型在理解指令和执行自然语言任务方面展现出惊人性能。本文提出迭代式翻译优化方法，旨在利用大语言模型的强大能力实现更自然的翻译与译后编辑。研究表明，仅通过让大语言模型参与迭代过程，输出质量即可超越单纯的翻译效果。基于GPT-3.5的广泛测试显示，尽管迭代过程降低了基于字符串的指标评分，但神经指标表明翻译质量相当甚至更优。此外，人工评估表明，相较于GPT初始翻译甚至人工参考译文，我们的方法能有效降低翻译腔，尤其在英译方向表现突出。消融实验强调了将优化过程锚定于源语言输入及合理初始翻译的重要性。