A new paradigm for machine translation has recently emerged: fine-tuning
large language models (LLM) on parallel text has been shown to outperform
dedicated translation systems trained in a supervised fashion on much larger
amounts of parallel data (Xu et al., 2024a; Alves et al., 2024). However, it
remains unclear whether this paradigm can enable massively multilingual machine
translation or whether it requires fine-tuning dedicated models for a small
number of language pairs. How does translation fine-tuning impact the MT
capabilities of LLMs for zero-shot languages, zero-shot language pairs, and
translation tasks that do not involve English? To address these questions, we
conduct an extensive empirical evaluation of the translation quality of the
TOWER family of language models (Alves et al., 2024) on 132 translation tasks
from the multi-parallel FLORES-200 data. We find that translation fine-tuning
improves translation quality even for zero-shot languages on average, but that
the impact is uneven depending on the language pairs involved. These results
call for further research to effectively enable massively multilingual
translation with LLMs.

通过在平行文本上对大型语言模型 (LLM) 进行微调，新的机器翻译范式已被证明能够胜过使用大量平行数据以监督方式训练的专用翻译系统，然而，对于大规模多语言机器翻译，是否需要对少数语言对进行专门的模型微调仍然不清楚。本研究通过对 TOWER 系列语言模型在 132 个来自多语言平行数据 FLORES-200 的翻译任务上进行实证评估，发现翻译微调即使对于零样本语言平均而言也会提高翻译质量，但其影响因所涉及的语言对而异。这些结果呼吁进一步研究以有效实现大规模多语言翻译。