Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual
  Alignment
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.
6621d8e6a11ce46061cd827d
Zhaofeng Wu
admin_assigned
2024-04-19T08:48:43.225Z
6351712b40dffad651f128c7
/avatars/87708c86c1baef548ef556f5d32dca71.svg
Zhaofeng Wu
user
ZhaofengWu
6621d8e6a11ce46061cd827e
Ananth Balashankar
6621d8e6a11ce46061cd827f
Yoon Kim
6621d8e6a11ce46061cd8280
Jacob Eisenstein
6621d8e6a11ce46061cd8281
Ahmad Beirami
admin_assigned
2024-04-19T08:49:42.457Z
65482d45407bb19ff5adb8a4
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/_etKjaaubh3osxp3R2MP7.jpeg
Ahmad Beirami
user
beirami
Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual
  Alignment
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.
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.
language models human-annotated preference data zero-shot cross-lingual alignment reward model summarization open-ended dialog generation human evaluation