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

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arxiv 2303.01911 v2 pith:GV6QXU5M submitted 2023-03-03 cs.CL

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

Rachel Bawden , Fran\c{c}ois Yvon This is my paper
classification cs.CL
keywords languagebloommodelmultilingualperformancelargepairsresults
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Meta-Learning Preferences for Multilingual LLM Alignment

    cs.CL 2026-07 conditional novelty 6.0

    Meta-learning a shared initialization on multilingual preference data lets LLMs align to a new language from ~100 preference samples, with up to 28% win-rate gains over baselines.

  2. Lessons from the Trenches on Reproducible Evaluation of Language Models

    cs.CL 2024-05 accept novelty 6.0

    The paper compiles practical lessons on reproducible LM evaluation and introduces the lm-eval library to mitigate common methodological problems in NLP.

  3. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

    cs.CL 2022-11 unverdicted novelty 6.0

    BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.