@inproceedings{lee-etal-2025-enginius,
title = "{ENG}inius: A Bilingual {LLM} Optimized for Plant Construction Engineering",
author = "Lee, Wooseong and
Kim, Minseo and
Hur, Taeil and
Jang, Gyeong Hwan and
Lee, Woncheol and
Na, Maro and
Kim, Taeuk",
editor = "Rehm, Georg and
Li, Yunyao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-industry.95/",
doi = "10.18653/v1/2025.acl-industry.95",
pages = "1350--1364",
ISBN = "979-8-89176-288-6",
abstract = "Recent advances in large language models (LLMs) have drawn attention for their potential to automate and optimize processes across various sectors.However, the adoption of LLMs in the plant construction industry remains limited, mainly due to its highly specialized nature and the lack of resources for domain-specific training and evaluation.In this work, we propose ENGinius, the first LLM designed for plant construction engineering.We present procedures for data construction and model training, along with the first benchmarks tailored to this underrepresented domain.We show that ENGinius delivers optimized responses to plant engineers by leveraging enriched domain knowledge.We also demonstrate its practical impact and use cases, such as technical document processing and multilingual communication."
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<abstract>Recent advances in large language models (LLMs) have drawn attention for their potential to automate and optimize processes across various sectors.However, the adoption of LLMs in the plant construction industry remains limited, mainly due to its highly specialized nature and the lack of resources for domain-specific training and evaluation.In this work, we propose ENGinius, the first LLM designed for plant construction engineering.We present procedures for data construction and model training, along with the first benchmarks tailored to this underrepresented domain.We show that ENGinius delivers optimized responses to plant engineers by leveraging enriched domain knowledge.We also demonstrate its practical impact and use cases, such as technical document processing and multilingual communication.</abstract>
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%0 Conference Proceedings
%T ENGinius: A Bilingual LLM Optimized for Plant Construction Engineering
%A Lee, Wooseong
%A Kim, Minseo
%A Hur, Taeil
%A Jang, Gyeong Hwan
%A Lee, Woncheol
%A Na, Maro
%A Kim, Taeuk
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F lee-etal-2025-enginius
%X Recent advances in large language models (LLMs) have drawn attention for their potential to automate and optimize processes across various sectors.However, the adoption of LLMs in the plant construction industry remains limited, mainly due to its highly specialized nature and the lack of resources for domain-specific training and evaluation.In this work, we propose ENGinius, the first LLM designed for plant construction engineering.We present procedures for data construction and model training, along with the first benchmarks tailored to this underrepresented domain.We show that ENGinius delivers optimized responses to plant engineers by leveraging enriched domain knowledge.We also demonstrate its practical impact and use cases, such as technical document processing and multilingual communication.
%R 10.18653/v1/2025.acl-industry.95
%U https://aclanthology.org/2025.acl-industry.95/
%U https://doi.org/10.18653/v1/2025.acl-industry.95
%P 1350-1364
Markdown (Informal)
[ENGinius: A Bilingual LLM Optimized for Plant Construction Engineering](https://aclanthology.org/2025.acl-industry.95/) (Lee et al., ACL 2025)
ACL
- Wooseong Lee, Minseo Kim, Taeil Hur, Gyeong Hwan Jang, Woncheol Lee, Maro Na, and Taeuk Kim. 2025. ENGinius: A Bilingual LLM Optimized for Plant Construction Engineering. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 1350–1364, Vienna, Austria. Association for Computational Linguistics.