  Large language models (LLMs) have demonstrated remarkable potential in
handling multilingual machine translation (MMT). In this paper, we
systematically investigate the advantages and challenges of LLMs for MMT by
answering two questions: 1) How well do LLMs perform in translating massive
languages? 2) Which factors affect LLMs' performance in translation? We
thoroughly evaluate eight popular LLMs, including ChatGPT and GPT-4. Our
empirical results show that translation capabilities of LLMs are continually
involving. GPT-4 has beat the strong supervised baseline NLLB in 40.91% of
translation directions but still faces a large gap towards the commercial
translation system like Google Translate, especially on low-resource languages.
Through further analysis, we discover that LLMs exhibit new working patterns
when used for MMT. First, LLM can acquire translation ability in a
resource-efficient way and generate moderate translation even on zero-resource
languages. Second, instruction semantics can surprisingly be ignored when given
in-context exemplars. Third, cross-lingual exemplars can provide better task
guidance for low-resource translation than exemplars in the same language
pairs. Code will be released at: https://github.com/NJUNLP/MMT-LLM.
