Contemporary translation engines built upon the encoder-decoder framework have reached a high level of development, while the emergence of Large Language Models (LLMs) has disrupted their position by offering the potential for achieving superior translation quality. Therefore, it is crucial to understand in which scenarios LLMs outperform traditional NMT systems and how to leverage their strengths. In this paper, we first conduct a comprehensive analysis to assess the strengths and limitations of various commercial NMT systems and MT-oriented LLMs. Our findings indicate that neither NMT nor MT-oriented LLMs alone can effectively address all the translation issues, but MT-oriented LLMs can serve as a promising complement to the NMT systems. Building upon these insights, we explore hybrid methods and propose Cooperative Decoding (CoDec), which treats NMT systems as a pretranslation model and MT-oriented LLMs as a supplemental solution to handle complex scenarios beyond the capability of NMT alone. The results on the WMT22 test sets and a newly collected test set WebCrawl demonstrate the effectiveness and efficiency of CoDec, highlighting its potential as a robust solution for combining NMT systems with MT-oriented LLMs in machine translation.

翻译：当代基于编码器-解码器框架的翻译引擎已达到高度发展水平，而大语言模型（LLMs）的出现通过提供实现卓越翻译质量的潜力，颠覆了其主导地位。因此，理解LLMs在何种场景下优于传统神经机器翻译（NMT）系统，以及如何发挥其优势至关重要。本文首先进行全面分析，评估各类商业NMT系统与面向机器翻译的LLMs的优势与局限性。研究发现：无论是NMT系统还是面向机器翻译的LLMs，均无法独立有效解决所有翻译问题，但面向机器翻译的LLMs可作为NMT系统的理想补充方案。基于此发现，我们探索混合方法并提出协同解码（CoDec）策略——将NMT系统视为预翻译模型，将面向机器翻译的LLMs作为处理NMT难以胜任复杂场景的补充方案。在WMT22测试集及新构建的WebCrawl测试集上的实验结果表明，CoDec兼具有效性与高效性，凸显其作为机器翻译领域融合NMT系统与面向机器翻译的LLMs的稳健方案潜力。