title: Aurora-M: The First Open Source Multilingual Language Model Red-teamed
    according to the U.S. Executive Order
summary: Pretrained language models underpin several AI applications, but their high
  computational cost for training limits accessibility. Initiatives such as BLOOM
  and StarCoder aim to democratize access to pretrained models for collaborative
  community development. However, such existing models face challenges: limited
  multilingual capabilities, continual pretraining causing catastrophic
  forgetting, whereas pretraining from scratch is computationally expensive, and
  compliance with AI safety and development laws. This paper presents Aurora-M, a
  15B parameter multilingual open-source model trained on English, Finnish,
  Hindi, Japanese, Vietnamese, and code. Continually pretrained from
  StarCoderPlus on 435 billion additional tokens, Aurora-M surpasses 2 trillion
  tokens in total training token count. It is the first open-source multilingual
  model fine-tuned on human-reviewed safety instructions, thus aligning its
  development not only with conventional red-teaming considerations, but also
  with the specific concerns articulated in the Biden-Harris Executive Order on
  the Safe, Secure, and Trustworthy Development and Use of Artificial
  Intelligence. Aurora-M is rigorously evaluated across various tasks and
  languages, demonstrating robustness against catastrophic forgetting and
  outperforming alternatives in multilingual settings, particularly in safety
  evaluations. To promote responsible open-source LLM development, Aurora-M and
  its variants are released at
  https://huggingface.co/collections/aurora-m/aurora-m-models-65fdfdff62471e09812f5407 .
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paper_title: Aurora-M: The First Open Source Multilingual Language Model Red-teamed
    according to the U.S. Executive Order
paper_summary: Pretrained language models underpin several AI applications, but their high
  computational cost for training limits accessibility. Initiatives such as BLOOM
  and StarCoder aim to democratize access to pretrained models for collaborative
  community development. However, such existing models face challenges: limited
  multilingual capabilities, continual pretraining causing catastrophic
  forgetting, whereas pretraining from scratch is computationally expensive, and
  compliance with AI safety and development laws. This paper presents Aurora-M, a
  15B parameter multilingual open-source model trained on English, Finnish,
  Hindi, Japanese, Vietnamese, and code. Continually pretrained from
  StarCoderPlus on 435 billion additional tokens, Aurora-M surpasses 2 trillion
  tokens in total training token count. It is the first open-source multilingual
  model fine-tuned on human-reviewed safety instructions, thus aligning its
  development not only with conventional red-teaming considerations, but also
  with the specific concerns articulated in the Biden-Harris Executive Order on
  the Safe, Secure, and Trustworthy Development and Use of Artificial
  Intelligence. Aurora-M is rigorously evaluated across various tasks and
  languages, demonstrating robustness against catastrophic forgetting and
  outperforming alternatives in multilingual settings, particularly in safety
  evaluations. To promote responsible open-source LLM development, Aurora-M and
  its variants are released at
  https://huggingface.co/collections/aurora-m/aurora-m-models-65fdfdff62471e09812f5407 .
paper_ai_summary: Aurora-M, a 15B parameter multilingual open-source model trained on multiple languages and code, addresses challenges of catastrophic forgetting and AI safety through continual pretraining and human-reviewed safety instructions.
paper_ai_keywords: pretrained language models multilingual capabilities continual pretraining catastrophic forgetting open-source model StarCoderPlus human-reviewed safety instructions Biden-Harris Executive Order robustness safety evaluations