Leveraging large language models for machine translation has demonstrated promising results. However, it does require the large language models to possess the capability of handling both the source and target languages in machine translation. When it is challenging to find large models that support the desired languages, resorting to continuous learning methods becomes a costly endeavor. To mitigate these expenses, we propose an innovative approach called RD (Relay Decoding), which entails concatenating two distinct large models that individually support the source and target languages. By incorporating a simple mapping layer to facilitate the connection between these two models and utilizing a limited amount of parallel data for training, we successfully achieve superior results in the machine translation task. Experimental results conducted on the Multi30k and WikiMatrix datasets validate the effectiveness of our proposed method.

翻译：利用大型语言模型进行机器翻译已展现出令人瞩目的成果。然而，这要求大型语言模型具备同时处理机器翻译中源语言和目标语言的能力。当难以找到支持所需语言的大型模型时，采用持续学习的方法便成为一项成本高昂的举措。为降低这些开销，我们提出了一种创新方法——RD（接力解码），该方法通过串联两个分别支持源语言和目标语言的不同大型模型来实现。通过引入一个简单的映射层促进这两个模型之间的连接，并利用有限数量的平行数据进行训练，我们成功在机器翻译任务中取得了优异的结果。在Multi30k和WikiMatrix数据集上进行的实验结果验证了我们所提出方法的有效性。