Improving Machine Translation with Large Language Models: A Preliminary
  Study with Cooperative Decoding
Jiali Zeng Fandong Meng Yongjing Yin Jie Zhou
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.