@inproceedings{yano-etal-2025-elaine,
title = "{ELAINE}-med{LLM}: Lightweight {E}nglish {J}apanese {C}hinese Trilingual Large Language Model for Bio-medical Domain",
author = "Yano, Ken and
Luo, Zheheng and
Huang, Jimin and
Xie, Qianqian and
Asada, Masaki and
Yuan, Chenhan and
Yang, Kailai and
Miwa, Makoto and
Ananiadou, Sophia and
Tsujii, Jun{'}ichi",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.313/",
pages = "4670--4688",
abstract = "We propose ELAINE (EngLish-jApanese-chINesE)-medLLM, a trilingual (English, Japanese, Chinese) large language model adapted for the bio-medical domain based on Llama-3-8B. The training dataset was carefully curated in terms of volume and diversity to adapt to the biomedical domain and endow trilingual capability while preserving the knowledge and abilities of the base model. The training follows 2-stage paths: continued pre-training and supervised fine-tuning (SFT). Our results demonstrate that ELAINE-medLLM exhibits superior trilingual capabilities compared to existing bilingual or multilingual medical LLMs without severely sacrificing the base model`s capability."
}
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<abstract>We propose ELAINE (EngLish-jApanese-chINesE)-medLLM, a trilingual (English, Japanese, Chinese) large language model adapted for the bio-medical domain based on Llama-3-8B. The training dataset was carefully curated in terms of volume and diversity to adapt to the biomedical domain and endow trilingual capability while preserving the knowledge and abilities of the base model. The training follows 2-stage paths: continued pre-training and supervised fine-tuning (SFT). Our results demonstrate that ELAINE-medLLM exhibits superior trilingual capabilities compared to existing bilingual or multilingual medical LLMs without severely sacrificing the base model‘s capability.</abstract>
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%0 Conference Proceedings
%T ELAINE-medLLM: Lightweight English Japanese Chinese Trilingual Large Language Model for Bio-medical Domain
%A Yano, Ken
%A Luo, Zheheng
%A Huang, Jimin
%A Xie, Qianqian
%A Asada, Masaki
%A Yuan, Chenhan
%A Yang, Kailai
%A Miwa, Makoto
%A Ananiadou, Sophia
%A Tsujii, Jun’ichi
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F yano-etal-2025-elaine
%X We propose ELAINE (EngLish-jApanese-chINesE)-medLLM, a trilingual (English, Japanese, Chinese) large language model adapted for the bio-medical domain based on Llama-3-8B. The training dataset was carefully curated in terms of volume and diversity to adapt to the biomedical domain and endow trilingual capability while preserving the knowledge and abilities of the base model. The training follows 2-stage paths: continued pre-training and supervised fine-tuning (SFT). Our results demonstrate that ELAINE-medLLM exhibits superior trilingual capabilities compared to existing bilingual or multilingual medical LLMs without severely sacrificing the base model‘s capability.
%U https://aclanthology.org/2025.coling-main.313/
%P 4670-4688
Markdown (Informal)
[ELAINE-medLLM: Lightweight English Japanese Chinese Trilingual Large Language Model for Bio-medical Domain](https://aclanthology.org/2025.coling-main.313/) (Yano et al., COLING 2025)
ACL
- Ken Yano, Zheheng Luo, Jimin Huang, Qianqian Xie, Masaki Asada, Chenhan Yuan, Kailai Yang, Makoto Miwa, Sophia Ananiadou, and Jun’ichi Tsujii. 2025. ELAINE-medLLM: Lightweight English Japanese Chinese Trilingual Large Language Model for Bio-medical Domain. In Proceedings of the 31st International Conference on Computational Linguistics, pages 4670–4688, Abu Dhabi, UAE. Association for Computational Linguistics.