  Contemporary translation engines based on the encoder-decoder framework have
made significant strides in development. However, the emergence of Large
Language Models (LLMs) has disrupted their position by presenting the potential
for achieving superior translation quality. To uncover the circumstances in
which LLMs excel and explore how their strengths can be harnessed to enhance
translation quality, 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 show
promise as a complementary solution to NMT systems. Building upon these
insights, we 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. Experimental
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 the field of machine translation.
