Human-in-the-Loop Learning With LLMs for Efficient RASE Tagging in Building Compliance Regulations
Automated compliance checking (ACC) in the Architecture, Engineering, and Construction (AEC) sector represents a pivotal task which is traditionally executed manually, demanding significant time and labor. This work investigates the automation of the Requirement, Applicability, Selection, and Except...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10778545/ |
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| author | Dhoyazan Al-Turki Hansi Hettiarachchi Mohamed Medhat Gaber Mohammed M. Abdelsamea Shadi Basurra Sima Iranmanesh Hadeel Saadany Edlira Vakaj |
| author_facet | Dhoyazan Al-Turki Hansi Hettiarachchi Mohamed Medhat Gaber Mohammed M. Abdelsamea Shadi Basurra Sima Iranmanesh Hadeel Saadany Edlira Vakaj |
| author_sort | Dhoyazan Al-Turki |
| collection | DOAJ |
| description | Automated compliance checking (ACC) in the Architecture, Engineering, and Construction (AEC) sector represents a pivotal task which is traditionally executed manually, demanding significant time and labor. This work investigates the automation of the Requirement, Applicability, Selection, and Exception (RASE) methodology for building regulatory compliance through the utilization of Large Language Models (LLMs) and active learning techniques. Specifically, we focus on the development and assessment of a system using the OpenAI GPT-4o model to transmute building regulation texts into structured YAML formats conducive to ACC processes. The study encompasses three experimental paradigms: few-shot learning, fine-tuning learning, and progressive active learning. Initial results from the few-shot learning experiment illustrate the model’s preliminary ability to interpret and process regulatory texts with limited examples. Fine-tuning enhances model performance by training it on a specialized dataset, thereby improving structural and textual accuracy. Progressive active learning, by iteratively incorporating expert feedback, further refines the accuracy of the model. The findings demonstrate substantial enhancements in both structural and semantic accuracies of the generated YAML files, underscoring the potential of integrating LLMs with active learning to streamline regulatory compliance automation. The methodologies and results presented here offer a comprehensive framework for advancing future research and practical applications in the domain of automated regulatory compliance. |
| format | Article |
| id | doaj-art-6d434dc84fec47069cc040a7f9068588 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6d434dc84fec47069cc040a7f90685882025-08-20T02:38:46ZengIEEEIEEE Access2169-35362024-01-011218529118530610.1109/ACCESS.2024.351243410778545Human-in-the-Loop Learning With LLMs for Efficient RASE Tagging in Building Compliance RegulationsDhoyazan Al-Turki0Hansi Hettiarachchi1https://orcid.org/0000-0003-4609-5001Mohamed Medhat Gaber2https://orcid.org/0000-0003-0339-4474Mohammed M. Abdelsamea3https://orcid.org/0009-0000-5677-6818Shadi Basurra4Sima Iranmanesh5Hadeel Saadany6https://orcid.org/0000-0002-2620-1842Edlira Vakaj7School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.School of Computing and Communications, Lancaster University, Lancaster, U.K.School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.Department of Computer Science, University of Exeter, Exeter, U.K.School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.Automated compliance checking (ACC) in the Architecture, Engineering, and Construction (AEC) sector represents a pivotal task which is traditionally executed manually, demanding significant time and labor. This work investigates the automation of the Requirement, Applicability, Selection, and Exception (RASE) methodology for building regulatory compliance through the utilization of Large Language Models (LLMs) and active learning techniques. Specifically, we focus on the development and assessment of a system using the OpenAI GPT-4o model to transmute building regulation texts into structured YAML formats conducive to ACC processes. The study encompasses three experimental paradigms: few-shot learning, fine-tuning learning, and progressive active learning. Initial results from the few-shot learning experiment illustrate the model’s preliminary ability to interpret and process regulatory texts with limited examples. Fine-tuning enhances model performance by training it on a specialized dataset, thereby improving structural and textual accuracy. Progressive active learning, by iteratively incorporating expert feedback, further refines the accuracy of the model. The findings demonstrate substantial enhancements in both structural and semantic accuracies of the generated YAML files, underscoring the potential of integrating LLMs with active learning to streamline regulatory compliance automation. The methodologies and results presented here offer a comprehensive framework for advancing future research and practical applications in the domain of automated regulatory compliance.https://ieeexplore.ieee.org/document/10778545/Automated compliance checkingRASElarge language modelsactive learning |
| spellingShingle | Dhoyazan Al-Turki Hansi Hettiarachchi Mohamed Medhat Gaber Mohammed M. Abdelsamea Shadi Basurra Sima Iranmanesh Hadeel Saadany Edlira Vakaj Human-in-the-Loop Learning With LLMs for Efficient RASE Tagging in Building Compliance Regulations IEEE Access Automated compliance checking RASE large language models active learning |
| title | Human-in-the-Loop Learning With LLMs for Efficient RASE Tagging in Building Compliance Regulations |
| title_full | Human-in-the-Loop Learning With LLMs for Efficient RASE Tagging in Building Compliance Regulations |
| title_fullStr | Human-in-the-Loop Learning With LLMs for Efficient RASE Tagging in Building Compliance Regulations |
| title_full_unstemmed | Human-in-the-Loop Learning With LLMs for Efficient RASE Tagging in Building Compliance Regulations |
| title_short | Human-in-the-Loop Learning With LLMs for Efficient RASE Tagging in Building Compliance Regulations |
| title_sort | human in the loop learning with llms for efficient rase tagging in building compliance regulations |
| topic | Automated compliance checking RASE large language models active learning |
| url | https://ieeexplore.ieee.org/document/10778545/ |
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