Babel: Open Multilingual Large Language Models Serving Over 90% of
  Global Speakers
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 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: Babel-9B, designed for efficient inference and
fine-tuning, and 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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Yiran Zhao
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Yiran Zhao
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Yiran0924
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Chaoqun Liu
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2025-03-06T09:27:33.956Z
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CHAOQUN LIU
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lukecq
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Yue Deng
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Jiahao Ying
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jiahao ying
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jhying
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Mahani Aljunied
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Mahani Aljunied
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Zhaodonghui Li
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Lidong Bing
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Lidong Bing
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LidongBing
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Hou Pong Chan
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2025-03-06T09:35:50.272Z
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Hou Pong (Ken) Chan
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kenchan0226
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Yu Rong
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2025-06-10T09:30:55.214Z
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Yu Rong
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Swrooy
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Deli Zhao
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Wenxuan Zhang
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2025-03-06T09:27:36.769Z
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Wenxuan Zhang
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isakzhang
Babel: Open Multilingual Large Language Models Serving Over 90% of
  Global Speakers
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 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: Babel-9B, designed for efficient inference and
fine-tuning, and 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.
Babel is an open multilingual LLM that expands its parameter count through layer extension, covering numerous languages and achieving superior performance in multilingual tasks compared to other open LLMs.
LLMs natural language processing multilingual LLMs layer extension Babel-9B Babel-83B inference fine-tuning supervised fine-tuning multilingual tasks