---
title: "Multilingual Large Language Model — paper by Qin et al. (2024)"
description: "They then propose a two-branch taxonomy. 421 references, cited by 62 papers here. Among them: 13."
authors: ["Libo Qin", "Qiguang Chen", "Yuhang Zhou", "Zhi Chen", "Yinghui Li", "Lizi Liao", "Min Li", "Wanxiang Che", "Philip S. Yu"]
year: 2024
venue: "arXiv:2404.04925"
source: "https://arxiv.org/abs/2404.04925"
doi: "10.48550/arxiv.2404.04925"
arxiv: "2404.04925"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/qin-2024-multilingual-large-language-model-survey/"
---
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# Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers

**Authors:** [Libo Qin](https://md.research.elamur.ai/free/en/authors/libo-qin/), [Qiguang Chen](https://md.research.elamur.ai/free/en/authors/qiguang-chen/), [Yuhang Zhou](https://md.research.elamur.ai/free/en/authors/yuhang-zhou/), [Zhi Chen](https://md.research.elamur.ai/free/en/authors/zhi-chen/), [Yinghui Li](https://md.research.elamur.ai/free/en/authors/yinghui-li/), [Lizi Liao](https://md.research.elamur.ai/free/en/authors/lizi-liao/), [Min Li](https://md.research.elamur.ai/free/en/authors/min-li/), [Wanxiang Che](https://md.research.elamur.ai/free/en/authors/wanxiang-che/) and [Philip S. Yu](https://md.research.elamur.ai/free/en/authors/philip-s-yu/) · **Year:** 2024 · **Venue:** arXiv:2404.04925 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2404.04925) · [PDF](https://arxiv.org/pdf/2404.04925) · [DOI 10.48550/arxiv.2404.04925](https://doi.org/10.48550/arxiv.2404.04925) · [arXiv 2404.04925](https://arxiv.org/abs/2404.04925) · [OpenAlex](https://openalex.org/W4394662652)

**Citations:** 13 citations (OpenAlex, 2026-10-14) · [62](https://md.research.elamur.ai/free/en/papers/qin-2024-multilingual-large-language-model-survey/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Most large language models are trained mainly on English and perform noticeably worse in other languages, especially low-resource ones. Before this paper, no overview tied together the many recent approaches to multilingual LLMs (MLLMs). Qin et al. fill this gap with a survey organised around one question: how a model aligns representations across languages. They first catalogue the data used at each training stage (pre-training, supervised fine-tuning (SFT) and RLHF) and group the corpora by how they were produced. They then propose a two-branch taxonomy. In parameter-tuning alignment, the model's weights are updated during pre-training, SFT, RLHF or downstream fine-tuning. In parameter-frozen alignment, cross-lingual behaviour comes purely from prompting. The survey closes with six open research directions and a companion website listing papers, corpora and leaderboards. For teams building Ukrainian-language tools, it maps the available multilingual datasets and the trade-offs between fine-tuning a model and relying on prompting alone.

## Key points
- **Taxonomy examples:**
  - Pre-training alignment covers both from-scratch models (mT5, BLOOM, XGLM, mGPT, LLaMA) and continual pre-training (Chinese-LLaMA, Sabia, Cabrita, FinGPT).
  - SFT alignment includes BLOOMz, mT0, PolyLM and Bayling.
  - RLHF alignment includes ChatGLM, Qwen, Okapi and LLaMA2-Chat.
- **Pre-training corpora fall into three groups:**
  - Hand-built: Bible Corpus, MultiUN.
  - Web-crawled: OSCAR, CC-100, mC4, RedPajama-v2, plus Wikipedia-derived sets such as WikiMatrix.
  - Re-cleaned benchmarks: OPUS-100, CulturaX, ROOTS, WMT.
- **SFT data falls into four groups:**
  - Manual: Sup-NatInst, OpenAssist.
  - Machine-translated: xP3-MT, Bactrian-X, CrossAlpaca.
  - Converted benchmarks: xP3, BUFFET.
  - Model-generated: ShareGPT, BELLE, MultiAlpaca.
- **Six frontiers are identified:** hallucination, knowledge editing, safety, fairness, adding new languages, and multimodality.
  - On fairness, tokenizers split some languages into more tokens than others, which raises their compute cost.
  - On language extension, adapters were reported to beat continued pre-training (Yong et al., 2022), but adding languages risks forgetting the original ones.
- **Motivation and resources:** the world has over 7,000 languages. Curated resources are hosted at multilingual-llm.net.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) — University, Beijing, China · 415 works · 94,028 citations
- [Harbin Institute of Technology](https://md.research.elamur.ai/free/en/universities/harbin-institute-of-technology/) — University, Harbin, China · 185 works · 29,140 citations
- [University of Illinois Chicago](https://md.research.elamur.ai/free/en/universities/university-of-illinois-chicago/) — University, Chicago, United States · 70 works · 20,363 citations
- [Singapore Management University](https://md.research.elamur.ai/free/en/universities/singapore-management-university/) — University, Singapore, Singapore · 35 works · 2,457 citations
- [Central South University](https://md.research.elamur.ai/free/en/universities/central-south-university/) — University, Changsha, China · 23 works · 657 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works

🧭 Topics (2): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,894 works · [🌍 Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · 882 works

## Cite

Cite as: Libo Qin, Qiguang Chen, Yuhang Zhou, Zhi Chen, Yinghui Li, Lizi Liao, Min Li, Wanxiang Che and Philip S. Yu. 2024. Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers. arXiv:2404.04925.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{qin2024multilingual,
  title = {{Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers}},
  author = {Libo Qin, Qiguang Chen, Yuhang Zhou, Zhi Chen, Yinghui Li, Lizi Liao, Min Li, Wanxiang Che and Philip S. Yu},
  year = {2024},
  doi = {10.48550/arxiv.2404.04925},
  eprint = {2404.04925},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2404.04925}
}
```

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- [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/6/)

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*Updated 2 November 2026*