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.

通过使用 RD（Relay Decoding）方法，将两个不同的大型语言模型连接起来，并利用少量的平行数据进行训练，我们成功地在机器翻译任务中取得了优越的结果。在 Multi30k 和 WikiMatrix 数据集上进行的实验结果验证了我们提出的方法的有效性。