Recently, large language models (LLMs) enhanced by self-reflection have achieved promising performance on machine translation. The key idea is guiding LLMs to generate translation with human-like feedback. However, existing self-reflection methods lack effective feedback information, limiting the translation performance. To address this, we introduce a DUAL-REFLECT framework, leveraging the dual learning of translation tasks to provide effective feedback, thereby enhancing the models' self-reflective abilities and improving translation performance. The application of this method across various translation tasks has proven its effectiveness in improving translation accuracy and eliminating ambiguities, especially in translation tasks with low-resource language pairs.

翻译：近年来，通过自我反思增强的大语言模型在机器翻译任务中展现出良好性能。其核心思想是引导大语言模型生成具有类人反馈的翻译结果。然而，现有自我反思方法缺乏有效的反馈信息，限制了翻译性能的提升。为此，我们提出DUAL-REFLECT框架，利用翻译任务的双学习机制提供有效反馈，从而增强模型的自我反思能力并提升翻译性能。该方法在多种翻译任务中的应用证明，其能有效提高翻译准确性并消除歧义，尤其在低资源语言对的翻译任务中表现突出。