Human-in-the-Loop Learning With LLMs for Efficient RASE Tagging in Building Compliance Regulations

Abstract

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.

Keywords

• Automated compliance checking
• RASE
• large language models
• active learning