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

Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM