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Multilingual Large Language Models and Curse of Multilinguality

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arxiv 2406.10602 v2 pith:IHY7PBNL submitted 2024-06-15 cs.CL cs.CY

Daniil Gurgurov , Tanja Bäumel , Tatiana Anikina This is my paper
classification cs.CLcs.CY
keywords modelsmultilinguallanguagelargellmscursemultilingualityacross
verification ladder T0 review T1 audit T2 compute T3 formal
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Multilingual Large Language Models (LLMs) have gained large popularity among Natural Language Processing (NLP) researchers and practitioners. These models, trained on huge datasets, show proficiency across various languages and demonstrate effectiveness in numerous downstream tasks. This paper navigates the landscape of multilingual LLMs, providing an introductory overview of their technical aspects. It explains underlying architectures, objective functions, pre-training data sources, and tokenization methods. This work explores the unique features of different model types: encoder-only (mBERT, XLM-R), decoder-only (XGLM, PALM, BLOOM, GPT-3), and encoder-decoder models (mT5, mBART). Additionally, it addresses one of the significant limitations of multilingual LLMs - the curse of multilinguality - and discusses current attempts to overcome it.

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Cited by 3 Pith papers

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

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    cs.CL 2026-07 conditional novelty 5.0 of 10

    Grouping 495 languages into roughly 16 clusters and routing speech to group-specific LoRA experts improves multilingual ASR error rates over dense and random baselines.

  3. SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use

    cs.CL 2025-05 conditional novelty 4.0 of 10

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