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.

比较 OpenAI 的 ChatGPT 和 Google 的 Bard 两个聊天机器人在消防工程领域的表现，结果显示 ChatGPT 表现较为优秀，该技术有望在消防工程实践和教育中起到重要作用。

火灾工程中的大型语言模型：领域知识对技术问题的考察

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

This research paper delves into the integration of OpenAI's ChatGPT into
embodied agent systems, evaluating its influence on interactive decision-making
benchmark. Drawing a parallel to the concept of people assuming roles according
to their unique strengths, we introduce InterAct. In this approach, we feed
ChatGPT with varied prompts, assigning it a numerous roles like a checker and a
sorter, then integrating them with the original language model. Our research
shows a remarkable success rate of 98% in AlfWorld, which consists of 6
different tasks in a simulated household environment, emphasizing the
significance of proficient prompt engineering. The results highlight ChatGPT's
competence in comprehending and performing intricate tasks effectively in
real-world settings, thus paving the way for further advancements in task
planning.

通过将 OpenAI 的 ChatGPT 与具身智能系统相结合，本研究评估了它对交互式决策基准的影响。我们引入了 InterAct 的概念，将 ChatGPT 赋予多个角色，如检查员和分类员，并与原始语言模型进行整合。我们的研究在 AlfWorld 中展示了惊人的 98% 成功率，该环境模拟了一个家庭环境中的 6 个不同任务，强调了有效的提示工程的重要性。结果突显了 ChatGPT 在理解和高效完成复杂任务方面的能力，为任务规划的进一步发展铺平了道路。