Large language models (LLMs) have demonstrated impressive capabilities in general scenarios, exhibiting a level of aptitude that approaches, in some aspects even surpasses, human-level intelligence. Among their numerous skills, the translation abilities of LLMs have received considerable attention. In contrast to traditional machine translation that focuses solely on source-target mapping, LLM-based translation can potentially mimic the human translation process that takes many preparatory steps to ensure high-quality translation. This work aims to explore this possibility by proposing the MAPS framework, which stands for Multi-Aspect Prompting and Selection. Specifically, we enable LLMs to first analyze the given source text and extract three aspects of translation-related knowledge: keywords, topics and relevant demonstrations to guide the translation process. To filter out the noisy and unhelpful knowledge, we employ a selection mechanism based on quality estimation. Experiments suggest that MAPS brings significant and consistent improvements over text-davinci-003 and Alpaca on eight translation directions from the latest WMT22 test sets. Our further analysis shows that the extracted knowledge is critical in resolving up to 59% of hallucination mistakes in translation. Code is available at https://github.com/zwhe99/MAPS-mt.

翻译：大型语言模型在通用场景中已展现出令人瞩目的能力，其表现水平在某些方面甚至接近或超越人类智能。在众多技能中，大型语言模型的翻译能力备受关注。与仅关注源语到目标语映射的传统机器翻译不同，基于大型语言模型的翻译有望模拟人类翻译过程中需要诸多预备步骤以确保高质量翻译的流程。本研究旨在通过提出MAPS框架（多维度提示与选择）探索这一可能性。具体而言，我们使大型语言模型首先分析给定源文本，提取三类翻译相关知识：关键词、主题及相关示例，以指导翻译过程。为过滤噪声和无效知识，我们采用基于质量估计的选择机制。实验表明，在最新WMT22测试集的八个翻译方向上，MAPS为text-davinci-003和Alpaca模型带来了显著且一致的改进。进一步分析显示，所提取的知识在解决翻译中高达59%的幻觉错误方面具有关键作用。代码已开源至https://github.com/zwhe99/MAPS-mt。