Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM
By Rachel Bawden, François Yvon. 2023
100 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Large Language Models, Machine Translation
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In Artificial Intelligence
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In Large Language Models
In Machine Translation
Citations in the corpus
- Prompting PaLM for Translation: Assessing Strategies and Performance (2022, background): “, 2023), and frequently sub-par in low-resource settings (Zhu et al., 2023; Bawden and Yvon, 2023)”