@inproceedings{park-etal-2025-conflict,
title = "Conflict and Overlap Classification in Construction Standards Using a Large Language Model",
author = "Park, Seong-Jin and
Jin, Youn-Gyu and
Moon, Hyun-Young and
Bong-Hyuck, Choi and
Hwan, Lee Seung and
Kwon, Ohjoon and
Kim, Kang-Min",
editor = "Chen, Weizhu and
Yang, Yi and
Kachuee, Mohammad and
Fu, Xue-Yong",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-industry.67/",
doi = "10.18653/v1/2025.naacl-industry.67",
pages = "903--917",
ISBN = "979-8-89176-194-0",
abstract = "Construction standards across different countries provide technical guidelines to ensure the quality and safety of buildings and facilities, with periodic revisions to accommodate advances in construction technology. However, these standards often contain overlapping or conflicting content owing to their broad scope and interdependence, complicating the revision process and creating public inconvenience. Although current expert-driven manual approaches aim to mitigate these issues, they are time-consuming, costly, and error-prone. To address these challenges, we propose conflict and overlap classification in construction standards using a large language model (COSLLM), a framework that leverages a construction domain-adapted large language model for the semantic comparison of sentences in construction standards. COSLLM utilizes a two-step reasoning process that adaptively employs chain-of-thought reasoning for the in-depth analysis of sentences suspected of overlaps or conflicts, ensuring computational and temporal efficiency while maintaining high classification accuracy. The framework achieved an accuracy of 97.9{\%} and a macro F1-score of 0.907 in classifying real-world sentence pairs derived from Korean construction standards as overlapping, conflicting, or neutral. Furthermore, we develop and deploy a real-time web-based system powered by COSLLM to facilitate the efficient establishment and revision of construction standards."
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<abstract>Construction standards across different countries provide technical guidelines to ensure the quality and safety of buildings and facilities, with periodic revisions to accommodate advances in construction technology. However, these standards often contain overlapping or conflicting content owing to their broad scope and interdependence, complicating the revision process and creating public inconvenience. Although current expert-driven manual approaches aim to mitigate these issues, they are time-consuming, costly, and error-prone. To address these challenges, we propose conflict and overlap classification in construction standards using a large language model (COSLLM), a framework that leverages a construction domain-adapted large language model for the semantic comparison of sentences in construction standards. COSLLM utilizes a two-step reasoning process that adaptively employs chain-of-thought reasoning for the in-depth analysis of sentences suspected of overlaps or conflicts, ensuring computational and temporal efficiency while maintaining high classification accuracy. The framework achieved an accuracy of 97.9% and a macro F1-score of 0.907 in classifying real-world sentence pairs derived from Korean construction standards as overlapping, conflicting, or neutral. Furthermore, we develop and deploy a real-time web-based system powered by COSLLM to facilitate the efficient establishment and revision of construction standards.</abstract>
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%0 Conference Proceedings
%T Conflict and Overlap Classification in Construction Standards Using a Large Language Model
%A Park, Seong-Jin
%A Jin, Youn-Gyu
%A Moon, Hyun-Young
%A Bong-Hyuck, Choi
%A Hwan, Lee Seung
%A Kwon, Ohjoon
%A Kim, Kang-Min
%Y Chen, Weizhu
%Y Yang, Yi
%Y Kachuee, Mohammad
%Y Fu, Xue-Yong
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-194-0
%F park-etal-2025-conflict
%X Construction standards across different countries provide technical guidelines to ensure the quality and safety of buildings and facilities, with periodic revisions to accommodate advances in construction technology. However, these standards often contain overlapping or conflicting content owing to their broad scope and interdependence, complicating the revision process and creating public inconvenience. Although current expert-driven manual approaches aim to mitigate these issues, they are time-consuming, costly, and error-prone. To address these challenges, we propose conflict and overlap classification in construction standards using a large language model (COSLLM), a framework that leverages a construction domain-adapted large language model for the semantic comparison of sentences in construction standards. COSLLM utilizes a two-step reasoning process that adaptively employs chain-of-thought reasoning for the in-depth analysis of sentences suspected of overlaps or conflicts, ensuring computational and temporal efficiency while maintaining high classification accuracy. The framework achieved an accuracy of 97.9% and a macro F1-score of 0.907 in classifying real-world sentence pairs derived from Korean construction standards as overlapping, conflicting, or neutral. Furthermore, we develop and deploy a real-time web-based system powered by COSLLM to facilitate the efficient establishment and revision of construction standards.
%R 10.18653/v1/2025.naacl-industry.67
%U https://aclanthology.org/2025.naacl-industry.67/
%U https://doi.org/10.18653/v1/2025.naacl-industry.67
%P 903-917
Markdown (Informal)
[Conflict and Overlap Classification in Construction Standards Using a Large Language Model](https://aclanthology.org/2025.naacl-industry.67/) (Park et al., NAACL 2025)
ACL
- Seong-Jin Park, Youn-Gyu Jin, Hyun-Young Moon, Choi Bong-Hyuck, Lee Seung Hwan, Ohjoon Kwon, and Kang-Min Kim. 2025. Conflict and Overlap Classification in Construction Standards Using a Large Language Model. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track), pages 903–917, Albuquerque, New Mexico. Association for Computational Linguistics.