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LLMs struggle with Hong Kong fire safety code interpretation, new benchmark reveals

By PulseAugur Editorial · [1 sources] ·

A new benchmark dataset has been developed to evaluate the performance of large language models (LLMs) in interpreting Hong Kong's fire safety codes. The study found that while proprietary LLMs achieved 80-90% accuracy on multiple-choice questions, their performance dropped to 50-70% on open-ended questions when assessed by human experts. This indicates a significant gap in LLM capabilities for specialized regulatory compliance tasks. AI

IMPACT Highlights the need for improved LLM accuracy in specialized regulatory compliance tasks, particularly in the AEC industry.

RANK_REASON The cluster contains an academic paper detailing a new benchmark for evaluating LLM performance on a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle with Hong Kong fire safety code interpretation, new benchmark reveals

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The cluster contains an academic paper detailing a new benchmark for evaluating LLM performance on a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Le Qiu, Zhuan Anne Zhang, Hok Ming Adeline Chan, Emmanuele Chersoni ·

    Towards a Benchmark for Fire Safety Code Interpretation in the Hong Kong AEC Industry

    arXiv:2610.16127v1 Announce Type: cross Abstract: 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 o…

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