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.

基于编码器 - 解码器框架的当代翻译引擎发展迅猛，而大型语言模型的出现破坏了它们的地位，提供了实现更优翻译质量的潜力。因此，了解 LLMs 是如何在何种场景下胜过传统 NMT 系统，以及如何发挥其优势，变得至关重要。本文首先进行了全面分析，评估了各种商业 NMT 系统和面向机器翻译的 LLMs 的优势和局限性。发现 NMT 系统和面向机器翻译的 LLMs 都不能有效解决所有翻译问题，但面向机器翻译的 LLMs 可以作为 NMT 系统的有希望的补充。基于这些发现，我们探索了混合方法，并提出了合作解码（CoDec）将 NMT 系统视为预翻译模型，将面向机器翻译的 LLMs 视为处理 NMT 系统无法覆盖的复杂场景的补充解决方案。在 WMT22 测试集和新收集的 WebCrawl 测试集上的结果表明，CoDec 的有效性和效率，突出了将 NMT 系统与面向机器翻译的 LLMs 相结合作为强大解决方案的潜力。