Summary of Large Language Models in Fire Engineering: An Examination Of Technical Questions Against Domain Knowledge, by Haley Hostetter et al.
Large Language Models in Fire Engineering: An Examination of Technical Questions Against Domain Knowledge
by Haley Hostetter, M.Z. Naser, Xinyan Huang, John Gales
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study compares two recent chatbots, OpenAI’s ChatGPT and Google’s Bard, in the context of fire engineering by evaluating their responses to fire safety-related queries. A diverse range of questions and scenarios were created and examined, including structural fire design, fire prevention strategies, evacuation, building code compliance, and fire suppression systems. The results show that ChatGPT performed relatively better than Bard. The study 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fire engineers can use chatbots like OpenAI’s ChatGPT and Google’s Bard to get answers to important questions quickly. Researchers tested these chatbots on different types of fire safety problems. They found that ChatGPT did a better job than Bard at giving correct answers. This is exciting because chatbots could make it easier for engineers to find the information they need while working on projects. |
