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

• URL: http://arxiv.org/abs/2503.00865v1
• Date: Sun, 02 Mar 2025 11:53:55 GMT
• Title: Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers
• Authors: Yiran Zhao, Chaoqun Liu, Yue Deng, Jiahao Ying, Mahani Aljunied, Zhaodonghui Li, Lidong Bing, Hou Pong Chan, Yu Rong, Deli Zhao, Wenxuan Zhang,
• Abstract summary: $texttBabel$ is an open multilingual LLM that covers the top 25 languages by number of speakers.<n>It supports over 90% of the global population, and includes many languages neglected by other open multilingual LLMs.
• Score: 80.69714909319842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have revolutionized natural language processing (NLP), yet open-source multilingual LLMs remain scarce, with existing models often limited in language coverage. Such models typically prioritize well-resourced languages, while widely spoken but under-resourced languages are often overlooked. To address this disparity, we introduce $\texttt{Babel}$, an open multilingual LLM that covers the top 25 languages by number of speakers, supports over 90% of the global population, and includes many languages neglected by other open multilingual LLMs. Unlike traditional continue pretraining approaches, Babel expands its parameter count through a layer extension technique that elevates Babel's performance ceiling. We introduce two variants: $\texttt{Babel-9B}$, designed for efficient inference and fine-tuning, and $\texttt{Babel-83B}$, 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 open-source 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 multilingual tasks, reaching the same level of commercial models.

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