title: SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models
    for Southeast Asian Languages
summary: Large Language Models (LLMs) have shown remarkable abilities across various
  tasks, yet their development has predominantly centered on high-resource
  languages like English and Chinese, leaving low-resource languages underserved.
  To address this disparity, we present SeaLLMs 3, the latest iteration of the
  SeaLLMs model family, tailored for Southeast Asian languages. This region,
  characterized by its rich linguistic diversity, has lacked adequate language
  technology support. SeaLLMs 3 aims to bridge this gap by covering a
  comprehensive range of languages spoken in this region, including English,
  Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao,
  Tamil, and Javanese. Leveraging efficient language enhancement techniques and a
  specially constructed instruction tuning dataset, SeaLLMs 3 significantly
  reduces training costs while maintaining high performance and versatility. Our
  model excels in tasks such as world knowledge, mathematical reasoning,
  translation, and instruction following, achieving state-of-the-art performance
  among similarly sized models. Additionally, we prioritized safety and
  reliability by addressing both general and culture-specific considerations and
  incorporated mechanisms to reduce hallucinations. This work underscores the
  importance of inclusive AI, showing that advanced LLM capabilities can benefit
  underserved linguistic and cultural communities.
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paper_authors.name: Wenxuan Zhang
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paper_authors.fullname: Hou Pong (Ken) Chan
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paper_authors.fullname: Yiran Zhao
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paper_authors.name: Mahani Aljunied
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paper_authors.fullname: Mahani Aljunied
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paper_authors.name: Jianyu Wang
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paper_authors.fullname: Jianyu Wang
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paper_authors.user: Jianyu
paper_authors._id: 66a86a304ac110262089dba7
paper_authors.name: Chaoqun Liu
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paper_authors.fullname: CHAOQUN LIU
paper_authors.type: user
paper_authors.user: lukecq
paper_authors._id: 66a86a304ac110262089dba8
paper_authors.name: Yue Deng
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paper_authors.fullname: yue deng
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paper_authors.name: Zhiqiang Hu
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paper_authors.fullname: Zhiqiang Hu
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paper_authors.name: Weiwen Xu
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paper_authors.fullname: Xu Weiwen
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paper_authors.name: Yew Ken Chia
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paper_authors.name: Xin Li
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paper_authors.fullname: Xin Li
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paper_authors.name: Lidong Bing
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paper_title: SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models
    for Southeast Asian Languages
paper_summary: Large Language Models (LLMs) have shown remarkable abilities across various
  tasks, yet their development has predominantly centered on high-resource
  languages like English and Chinese, leaving low-resource languages underserved.
  To address this disparity, we present SeaLLMs 3, the latest iteration of the
  SeaLLMs model family, tailored for Southeast Asian languages. This region,
  characterized by its rich linguistic diversity, has lacked adequate language
  technology support. SeaLLMs 3 aims to bridge this gap by covering a
  comprehensive range of languages spoken in this region, including English,
  Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao,
  Tamil, and Javanese. Leveraging efficient language enhancement techniques and a
  specially constructed instruction tuning dataset, SeaLLMs 3 significantly
  reduces training costs while maintaining high performance and versatility. Our
  model excels in tasks such as world knowledge, mathematical reasoning,
  translation, and instruction following, achieving state-of-the-art performance
  among similarly sized models. Additionally, we prioritized safety and
  reliability by addressing both general and culture-specific considerations and
  incorporated mechanisms to reduce hallucinations. This work underscores the
  importance of inclusive AI, showing that advanced LLM capabilities can benefit
  underserved linguistic and cultural communities.
paper_ai_summary: SeaLLMs 3 is a large language model optimized for Southeast Asian languages, offering state-of-the-art performance in various tasks while addressing safety and cultural considerations.
paper_ai_keywords: large language models SeaLLMs linguistic diversity language enhancement instruction tuning world knowledge mathematical reasoning translation instruction following hallucinations inclusive AI