title: Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual
    Alignment
summary: Aligning language models (LMs) based on human-annotated preference data is a
  crucial step in obtaining practical and performant LM-based systems. However,
  multilingual human preference data are difficult to obtain at scale, making it
  challenging to extend this framework to diverse languages. In this work, we
  evaluate a simple approach for zero-shot cross-lingual alignment, where a
  reward model is trained on preference data in one source language and directly
  applied to other target languages. On summarization and open-ended dialog
  generation, we show that this method is consistently successful under
  comprehensive evaluation settings, including human evaluation: cross-lingually
  aligned models are preferred by humans over unaligned models on up to >70% of
  evaluation instances. We moreover find that a different-language reward model
  sometimes yields better aligned models than a same-language reward model. We
  also identify best practices when there is no language-specific data for even
  supervised finetuning, another component in alignment.
paper_authors._id: 6621d8e6a11ce46061cd827d
paper_authors.name: Zhaofeng Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-19T08:48:43.225Z
paper_authors._id: 6351712b40dffad651f128c7
paper_authors.avatarUrl: /avatars/87708c86c1baef548ef556f5d32dca71.svg
paper_authors.fullname: Zhaofeng Wu
paper_authors.type: user
paper_authors.user: ZhaofengWu
paper_authors._id: 6621d8e6a11ce46061cd827e
paper_authors.name: Ananth Balashankar
paper_authors._id: 6621d8e6a11ce46061cd827f
paper_authors.name: Yoon Kim
paper_authors._id: 6621d8e6a11ce46061cd8280
paper_authors.name: Jacob Eisenstein
paper_authors._id: 6621d8e6a11ce46061cd8281
paper_authors.name: Ahmad Beirami
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-19T08:49:42.457Z
paper_authors._id: 65482d45407bb19ff5adb8a4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/_etKjaaubh3osxp3R2MP7.jpeg
paper_authors.fullname: Ahmad Beirami
paper_authors.type: user
paper_authors.user: beirami
paper_title: Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual
    Alignment
paper_summary: Aligning language models (LMs) based on human-annotated preference data is a
  crucial step in obtaining practical and performant LM-based systems. However,
  multilingual human preference data are difficult to obtain at scale, making it
  challenging to extend this framework to diverse languages. In this work, we
  evaluate a simple approach for zero-shot cross-lingual alignment, where a
  reward model is trained on preference data in one source language and directly
  applied to other target languages. On summarization and open-ended dialog
  generation, we show that this method is consistently successful under
  comprehensive evaluation settings, including human evaluation: cross-lingually
  aligned models are preferred by humans over unaligned models on up to >70% of
  evaluation instances. We moreover find that a different-language reward model
  sometimes yields better aligned models than a same-language reward model. We
  also identify best practices when there is no language-specific data for even
  supervised finetuning, another component in alignment.
paper_ai_summary: A zero-shot cross-lingual alignment method for language models using a reward model trained in one language outperforms unaligned models across multiple languages and tasks.
paper_ai_keywords: language models human-annotated preference data zero-shot cross-lingual alignment reward model summarization open-ended dialog generation human evaluation