Tower-Babel/Babel-9B

Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers

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Introduction

We introduce Babel, a multilingual LLM that covers the top 25 languages by number of speakers, including English, Chinese, Hindi, Spanish, Arabic, French, Bengali, Portuguese, Russian, Urdu, Indonesian, German, Japanese, Swahili, Filipino, Tamil, Vietnamese, Turkish, Italian, Javanese, Korean, Hausa, Persian, Thai, and Burmese. These 25 languages support over 90% of the global population, and include many languages neglected by other open multilingual LLMs. Unlike traditional continued pretraining approaches, Babel expands its parameter count through a layer extension technique that elevates Babel's performance ceiling.

We introduce two variants:

• Babel-9B, designed for efficient inference and fine-tuning
• Babel-83B, which sets a new standard for open multilingual LLMs

Extensive evaluations on multilingual tasks demonstrate its superior performance compared to open LLMs of comparable size. In addition, using existing supervised fine-tuning datasets, Babel achieves remarkable performance, with Babel-9B-Chat leading among 10B-sized LLMs and Babel-83B-Chat setting a new standard for open LLMs, performing comparably to GPT-4o on certain tasks.

This page introduces the Babel-9B-Base model

Evaluation

We employ multilingual tasks across several categories:

1. World Knowledge:
   MMMLU (OpenAI 2024), a human-translated version of MMLU (Hendrycks et al. 2021) available in 14 languages. For languages not covered, we use Google Translate (Google Translate API) to generate translations. Additionally, we include M3Exam (M3Exam), which consists of authentic human exam questions collected from various countries, covering multiple subjects and educational levels.

2. Reasoning:
   MGSM (Shi et al. 2022) and XCOPA (Ponti et al. 2020).

3. Understanding:
   XNLI (Conneau et al. 2018).

4. Translation:
   Flores-200 (NLLB Team 2022).

Performance of 10B-Size Base Models vs. Babel-9B

Dataset	Gemma2-9B	Mistral-12B	Llama3.1-8B	Qwen2.5-7B	GLM4-9B	Babel-9B
MMMLU	59.8	52.8	49.4	56.7	55.6	59.4
M3Exam	61.6	54.2	52.5	58.8	56.6	61.3
XCOPA	84.6	81.3	75.9	81.1	87.3	89.2
MGSM	34.3	26.0	18.0	41.1	39.0	43.4
XNLI	61.7	55.0	48.9	70.3	69.9	71.9
Flores-200	53.2	50.8	50.9	45.5	46.6	55.1
Average	59.5	53.4	49.3	58.9	59.2	63.4

Acknowledgement

We would like to thank Guanzheng Chen for assisting with the implementation of the training codebase. Our special thanks go to our professional and native linguists—Tantong Champaiboon, Nguyen Ngoc Yen Nhi, and Tara Devina Putri—who contributed to building, evaluating, and fact-checking our sampled pretraining dataset. We also appreciate Fan Wang, Jiasheng Tang, Xin Li, and Hao Zhang for their efforts in coordinating computing resources.

Citation

If you find our project useful, we hope you would kindly star our repo and cite our work as follows:

@misc{zhao2025babelopenmultilinguallarge,
      title={Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers}, 
      author={Yiran Zhao and Chaoqun Liu and Yue Deng and Jiahao Ying and Mahani Aljunied and Zhaodonghui Li and Lidong Bing and Hou Pong Chan and Yu Rong and Deli Zhao and Wenxuan Zhang},
      year={2025},
      eprint={2503.00865},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.00865}, 
}

Corresponding Author: [email protected]