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Large-scale Pretrained Language Models (LLMs), such as ChatGPT and GPT4, haveshown strong abilities in multilingual translations, without being explicitlytrained on parallel corpora. It is interesting how the LLMs obtain theirability to carry out translation instructions for different languages. In thispaper, we present a detailed analysis by finetuning a multilingual pretrainedlanguage model, XGLM-7B, to perform multilingual translation following giveninstructions. Firstly, we show that multilingual LLMs have stronger translationabilities than previously demonstrated. For a certain language, the performancedepends on its similarity to English and the amount of data used in thepretraining phase. Secondly, we find that LLMs' ability to carry outtranslation instructions relies on the understanding of translationinstructions and the alignment among different languages. With multilingualfinetuning, LLMs could learn to perform the translation task well even forthose language pairs unseen during the instruction tuning phase.

Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions