Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/108012
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Title: The role of large language models (AI chatbots) in fire engineering : an examination of technical questions against domain knowledge
Authors: Hostetter, H
Naser, MZ
Huang, X 
Gales, J
Issue Date: Dec-2024
Source: Natural hazards research, Dec. 2024, v. 4, no. 4, p. 669-688
Abstract: This communication presents a short review of chatbot technology and 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. The results reveal some key differences in the performance of the chatbots, with ChatGPT demonstrating a relatively superior performance of 88% (vs. 80% for Bard). 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.
Keywords: Artificial intelligence (AI)
Chatbots
Fire engineering
Large language models (LLMs)
Publisher: KeAi Publishing Communications Ltd.
Journal: Natural hazards research 
EISSN: 2666-5921
DOI: 10.1016/j.nhres.2024.06.003
Rights: © 2024 National Institute of Natural Hazards, Ministry of Emergency Management of China. Publishing services provided by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
The following publication Hostetter, H., Naser, M. Z., Huang, X., & Gales, J. (2024). The role of large language models (AI chatbots) in fire engineering: An examination of technical questions against domain knowledge. Natural Hazards Research, 4(4), 669-688 is available at https://doi.org/10.1016/j.nhres.2024.06.003.
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