Do Multilingual Language Models Think Better in English?

Julen Etxaniz, Gorka Azkune, Aitor Soroa, Oier Lacalle, Mikel Artetxe


Abstract
Translate-test is a popular technique to improve the performance of multilingual language models. This approach works by translating the input into English using an external machine translation system before running inference. However, these improvements can be attributed to the use of a separate translation system, which is typically trained on large amounts of parallel data not seen by the language model. In this work, we introduce a new approach called self-translate that leverages the few-shot translation capabilities of multilingual language models. This allows us to analyze the effect of translation in isolation. Experiments over 5 tasks show that self-translate consistently outperforms direct inference, demonstrating that language models are unable to leverage their full multilingual potential when prompted in non-English languages. Our code is available at https://github.com/juletx/self-translate.
Anthology ID:
2024.naacl-short.46
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
550–564
Language:
URL:
https://aclanthology.org/2024.naacl-short.46
DOI:
Bibkey:
Cite (ACL):
Julen Etxaniz, Gorka Azkune, Aitor Soroa, Oier Lacalle, and Mikel Artetxe. 2024. Do Multilingual Language Models Think Better in English?. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 550–564, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Do Multilingual Language Models Think Better in English? (Etxaniz et al., NAACL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.naacl-short.46.pdf