mlsanityData to 7 Oct 2026

Large Language Models in Fire Engineering: An Examination of Technical Questions Against Domain Knowledge

arXiv 2403.04795 ↗First posted 4 Mar 2024cs.CL cs.AI cs.LGcited by 5

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Abstract (arXiv)

This communication presents preliminary findings from comparing two recent chatbots, OpenAI's ChatGPT and Google's Bard, in the context of fire engineering by evaluating their responses in handling fire safety related queries. A diverse range of fire engineering questions and scenarios were created and examined, including structural fire design, fire prevention strategies, evacuation, building code compliance, and fire suppression systems (some of which resemble those commonly present in the Fire Protection exam (FPE)). The results reveal some key differences in the performance of the chatbots, with ChatGPT demonstrating a relatively superior performance. Then, this communication highlights the potential for chatbot technology to revolutionize fire engineering practices by providing instant access to critical information while outlining areas for further improvement and research. Evidently, and when it matures, this technology will likely be elemental to our engineers' practice and education.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 1 of its 55 references that are in the map. First posted on arXiv 4 Mar 2024.

Builds on

1 of 55 references.

References · 1

  1. 2023Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncologycited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.