@inproceedings{she-etal-2024-mapo,
title = "{MAPO}: Advancing Multilingual Reasoning through Multilingual-Alignment-as-Preference Optimization",
author = "She, Shuaijie and
Zou, Wei and
Huang, Shujian and
Zhu, Wenhao and
Liu, Xiang and
Geng, Xiang and
Chen, Jiajun",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.539",
pages = "10015--10027",
abstract = "Intuitively, reasoning abilities are considered language-agnostic. However, existing LLMs exhibit inconsistent reasoning abilities across different languages, e.g., reasoning in the dominant language like English is superior to other languages due to the imbalance of multilingual training data. To enhance reasoning abilities in non-dominant languages, we propose a Multilingual-Alignment-as-Preference Optimization framework (MAPO) to align the reasoning processes in other languages with the dominant language. Specifically, we harness an off-the-shelf translation model for the consistency between answers in non-dominant and dominant languages, which we adopt as the preference for optimization, e.g., Direct Preference Optimization(DPO) or Proximal Policy Optimization (PPO). Experiments show that MAPO stably achieves significant improvements in the multilingual reasoning of various models on all three benchmarks (MSVAMP +16.2{\%}, MGSM +6.1{\%}, and MNumGLUESub +13.3{\%}), with improved reasoning consistency across languages. The project is available at https://github.com/NJUNLP/MAPO.",
}
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<abstract>Intuitively, reasoning abilities are considered language-agnostic. However, existing LLMs exhibit inconsistent reasoning abilities across different languages, e.g., reasoning in the dominant language like English is superior to other languages due to the imbalance of multilingual training data. To enhance reasoning abilities in non-dominant languages, we propose a Multilingual-Alignment-as-Preference Optimization framework (MAPO) to align the reasoning processes in other languages with the dominant language. Specifically, we harness an off-the-shelf translation model for the consistency between answers in non-dominant and dominant languages, which we adopt as the preference for optimization, e.g., Direct Preference Optimization(DPO) or Proximal Policy Optimization (PPO). Experiments show that MAPO stably achieves significant improvements in the multilingual reasoning of various models on all three benchmarks (MSVAMP +16.2%, MGSM +6.1%, and MNumGLUESub +13.3%), with improved reasoning consistency across languages. The project is available at https://github.com/NJUNLP/MAPO.</abstract>
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%0 Conference Proceedings
%T MAPO: Advancing Multilingual Reasoning through Multilingual-Alignment-as-Preference Optimization
%A She, Shuaijie
%A Zou, Wei
%A Huang, Shujian
%A Zhu, Wenhao
%A Liu, Xiang
%A Geng, Xiang
%A Chen, Jiajun
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F she-etal-2024-mapo
%X Intuitively, reasoning abilities are considered language-agnostic. However, existing LLMs exhibit inconsistent reasoning abilities across different languages, e.g., reasoning in the dominant language like English is superior to other languages due to the imbalance of multilingual training data. To enhance reasoning abilities in non-dominant languages, we propose a Multilingual-Alignment-as-Preference Optimization framework (MAPO) to align the reasoning processes in other languages with the dominant language. Specifically, we harness an off-the-shelf translation model for the consistency between answers in non-dominant and dominant languages, which we adopt as the preference for optimization, e.g., Direct Preference Optimization(DPO) or Proximal Policy Optimization (PPO). Experiments show that MAPO stably achieves significant improvements in the multilingual reasoning of various models on all three benchmarks (MSVAMP +16.2%, MGSM +6.1%, and MNumGLUESub +13.3%), with improved reasoning consistency across languages. The project is available at https://github.com/NJUNLP/MAPO.
%U https://aclanthology.org/2024.acl-long.539
%P 10015-10027
Markdown (Informal)
[MAPO: Advancing Multilingual Reasoning through Multilingual-Alignment-as-Preference Optimization](https://aclanthology.org/2024.acl-long.539) (She et al., ACL 2024)
ACL