  The NLP community recently saw the release of a new large open-access
multilingual language model, BLOOM (BigScience et al., 2022) covering 46
languages. We focus on BLOOM's multilingual ability by evaluating its machine
translation performance across several datasets (WMT, Flores-101 and DiaBLa)
and language pairs (high- and low-resourced). Our results show that 0-shot
performance suffers from overgeneration and generating in the wrong language,
but this is greatly improved in the few-shot setting, with very good results
for a number of language pairs. We study several aspects including prompt
design, model sizes, cross-lingual transfer and the use of discursive context.
