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

• URL: http://arxiv.org/abs/2303.01911v2
• Date: Tue, 9 May 2023 12:21:58 GMT
• Title: Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM
• Authors: Rachel Bawden and Fran\c{c}ois Yvon
• Abstract summary: We focus on BLOOM's multilingual ability by evaluating its machine translation performance across several datasets. We study several aspects including prompt design, model sizes, cross-lingual transfer and the use of discursive context.
• Score: 8.858671209228536
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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