Prospects and Challenges of Large Language Models in the Field of Intelligent Building

Wu Yang, Junjie Wang, Wei-Han Li
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Abstract

: At the end of November 2022, the ChatGPT released by OpenAI Inc. performed excellently and quickly became popular worldwide. Despite some shortcomings, Large Language Models (LLM) represented by Generative Pre-trained Transformer (GPT) is here to stay, leading the way for the new generation of Natural Language Processing (NLP) technique. This commentary presents the potential benefits and challenges of the applications of large language models, from the viewpoint of intelligent building. We briefly discuss the history and current state of large language models and their shortcomings. We then highlight how these models can be used to improve the daily maintenance of intelligent building. With regard to challenges, we address some vital problems to be solved before deployment and argue that large language models in intelligent building require maintenance staff to develop sets of competencies and literacies necessary to both understand the technology as well as the maintenance and maneuver of intelligent building. In addition, a clear strategy within intelligent building troops with a strong focus on AI talents construction and training dataset annotation are required to integrate and take full advantage of large language models in the daily maintenance. We conclude with recommendations for how to address these challenges and prepare for further applications of LLM in the field of intelligent building in the future.
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大型语言模型在智能建筑领域的前景与挑战
: 2022年11月底,OpenAI公司发布的ChatGPT表现优异,迅速在全球范围内流行。尽管存在一些缺点,但以生成预训练转换器(GPT)为代表的大型语言模型(LLM)仍将继续发展,引领新一代自然语言处理(NLP)技术的发展。这篇评论从智能建筑的角度提出了大型语言模型应用的潜在好处和挑战。我们简要地讨论了大型语言模型的历史和现状以及它们的缺点。然后,我们强调如何使用这些模型来改善智能建筑的日常维护。关于挑战,我们讨论了在部署之前需要解决的一些关键问题,并认为智能建筑中的大型语言模型需要维护人员培养一系列必要的能力和素养,以理解技术以及智能建筑的维护和操作。此外,需要在智能建筑部队内部制定明确的战略,重点关注AI人才建设和训练数据集标注,以便在日常维护中整合和充分利用大型语言模型。最后,我们就如何应对这些挑战提出了建议,并为法学硕士在未来智能建筑领域的进一步应用做好了准备。
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