Towards a Benchmark for Fire Safety Code Interpretation in the Hong Kong AEC Industry
Large Language Models (LLMs) are increasingly explored in the Architecture, Engineering, and Construction (AEC) domain, yet their reliability in regulatory compliance remains uncertain. In this study, we evaluate the performance of LLMs on Hong Kong fire safety codes. In collaboration with domain experts, we create a localized benchmark dataset comprising both multiple-choice questions (MCQs) and open-ended questions (OEQs) to evaluate 12 widely used proprietary and open-source models. Our findings show that top-performing proprietary models achieve 80-90% accuracy on MCQs, whereas open-source models generally lag behind. However, on open-ended evaluation, expert human assessments reveal ...
| Authors | Le Qiu, Zhuan Anne Zhang, Hok Ming Adeline Chan et al. |
|---|---|
| Primary category | cs.CE |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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