Large language models (LLMs) demonstrate promising translation performance among various natural languages. However, many LLMs especially the open-sourced ones, such as BLOOM and LLaMA, are English-dominant and support only dozens of natural languages, making the potential of LLMs on language translation less explored. In this work, we present BigTrans which adapts LLaMA that covers only 20 languages and enhances it with multilingual translation capability on more than 100 languages. BigTrans is built upon LLaMA-13B and it is optimized in three steps. First, we continue training LLaMA with massive Chinese monolingual data. Second, we continue training the model with a large-scale parallel dataset that covers 102 natural languages. Third, we instruct-tune the foundation model with multilingual translation instructions, leading to our BigTrans model. The preliminary experiments on multilingual translation show that BigTrans performs comparably with ChatGPT and Google Translate in many languages and even outperforms ChatGPT in 8 language pairs. We release the BigTrans model and hope it can advance the research progress.

翻译：大语言模型（LLMs）在多种自然语言之间的翻译任务中展现出卓越性能。然而，许多大语言模型（尤其是开源模型，如BLOOM和LLaMA）以英语为主导且仅支持数十种自然语言，这限制了LLMs在语言翻译领域的潜力。本研究提出BigTrans模型，对仅覆盖20种语言的LLaMA进行适配，使其具备覆盖100多种语言的多语言翻译能力。BigTrans基于LLaMA-13B构建，通过三步优化实现：首先，利用海量中文单语数据对LLaMA进行持续训练；其次，采用覆盖102种自然语言的大规模平行数据集对模型进行持续训练；最后，通过多语言翻译指令对基础模型进行指令微调，最终得到BigTrans模型。多语言翻译的初步实验表明，BigTrans在多种语言上的表现与ChatGPT和谷歌翻译相当，甚至在8个语言对中优于ChatGPT。我们已开源BigTrans模型，期望推动该领域的研究进展。