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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

Rachel Bawden , François Yvon This is my paper · ORCID
classification cs.CL
keywords languagebloommodelmultilingualperformancelargepairsresults
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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. OpenAlex reports about 12 citations worldwide. Full citation record

  1. Meta-Learning Preferences for Multilingual LLM Alignment

    cs.CL 2026-07 conditional novelty 6.0 of 10

    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. Information Loss in LLMs' Multilingual Translation: The Role of Training Data, Language Proximity, and Language Family

    cs.CL 2025-06 reject novelty 5.0 of 10

    Round-trip translation quality in GPT-4 and Llama 2 is jointly shaped by training data volume and language distance from English, with orthographic, phylogenetic, syntactic, and geographic distances as the strongest p...

  3. Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs

    cs.CL 2025-02 conditional novelty 5.0 of 10

    Selective pre-translation, translating only some prompt components into English, generally outperforms both full prompt translation and direct inference across tasks and languages, with the largest gains for low-resou...

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