@inproceedings{yoon-etal-2024-langbridge,
title = "{L}ang{B}ridge: Multilingual Reasoning Without Multilingual Supervision",
author = "Yoon, Dongkeun and
Jang, Joel and
Kim, Sungdong and
Kim, Seungone and
Shafayat, Sheikh and
Seo, Minjoon",
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.405",
doi = "10.18653/v1/2024.acl-long.405",
pages = "7502--7522",
abstract = "We introduce LangBridge, a $\textit{zero-shot}$ approach to adapt language models for multilingual reasoning tasks without multilingual supervision. LangBridge operates by bridging two models, each specialized in different aspects: (1) one specialized in understanding multiple languages (e.g., mT5 encoder) and (2) one specialized in reasoning (e.g., MetaMath). LangBridge connects the two models by introducing minimal trainable parameters between them. Despite utilizing only English data for training, LangBridge considerably enhances the performance of language models on low-resource languages across mathematical reasoning, code completion, logical reasoning, and commonsense reasoning. Our analysis suggests that the efficacy of LangBridge stems from the language-agnostic characteristics of multilingual representations. We publicly release our code and models.",
}
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<abstract>We introduce LangBridge, a zero-shot approach to adapt language models for multilingual reasoning tasks without multilingual supervision. LangBridge operates by bridging two models, each specialized in different aspects: (1) one specialized in understanding multiple languages (e.g., mT5 encoder) and (2) one specialized in reasoning (e.g., MetaMath). LangBridge connects the two models by introducing minimal trainable parameters between them. Despite utilizing only English data for training, LangBridge considerably enhances the performance of language models on low-resource languages across mathematical reasoning, code completion, logical reasoning, and commonsense reasoning. Our analysis suggests that the efficacy of LangBridge stems from the language-agnostic characteristics of multilingual representations. We publicly release our code and models.</abstract>
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%0 Conference Proceedings
%T LangBridge: Multilingual Reasoning Without Multilingual Supervision
%A Yoon, Dongkeun
%A Jang, Joel
%A Kim, Sungdong
%A Kim, Seungone
%A Shafayat, Sheikh
%A Seo, Minjoon
%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 yoon-etal-2024-langbridge
%X We introduce LangBridge, a zero-shot approach to adapt language models for multilingual reasoning tasks without multilingual supervision. LangBridge operates by bridging two models, each specialized in different aspects: (1) one specialized in understanding multiple languages (e.g., mT5 encoder) and (2) one specialized in reasoning (e.g., MetaMath). LangBridge connects the two models by introducing minimal trainable parameters between them. Despite utilizing only English data for training, LangBridge considerably enhances the performance of language models on low-resource languages across mathematical reasoning, code completion, logical reasoning, and commonsense reasoning. Our analysis suggests that the efficacy of LangBridge stems from the language-agnostic characteristics of multilingual representations. We publicly release our code and models.
%R 10.18653/v1/2024.acl-long.405
%U https://aclanthology.org/2024.acl-long.405
%U https://doi.org/10.18653/v1/2024.acl-long.405
%P 7502-7522
Markdown (Informal)
[LangBridge: Multilingual Reasoning Without Multilingual Supervision](https://aclanthology.org/2024.acl-long.405) (Yoon et al., ACL 2024)
ACL
- Dongkeun Yoon, Joel Jang, Sungdong Kim, Seungone Kim, Sheikh Shafayat, and Minjoon Seo. 2024. LangBridge: Multilingual Reasoning Without Multilingual Supervision. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7502–7522, Bangkok, Thailand. Association for Computational Linguistics.