Large language models such as ChatGPT have the potential to revolutionize the construction industry by automating repetitive and time-consuming tasks. This paper presents a study in which ChatGPT was used to generate a construction schedule for a simple construction project. The output from ChatGPT was evaluated by a pool of participants that provided feedback regarding their overall interaction experience and the quality of the output. The results show that ChatGPT can generate a coherent schedule that follows a logical approach to fulfill the requirements of the scope indicated. The participants had an overall positive interaction experience and indicated the great potential of such a tool to automate many preliminary and time-consuming tasks. However, the technology still has limitations, and further development is needed before it can be widely adopted in the industry. Overall, this study highlights the potential of using large language models in the construction industry and the need for further research.
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
Mila-Québec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）；Arizona State University（亚利桑那州立大学）；Princeton University（普林斯顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Université de Montréal（蒙特利尔大学）；National Research Council Canada（加拿大国家研究委员会）
Using LLMs in Software Design: An Empirical Study of GitHub and A Practitioner Survey
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Computer Science, Central China Normal University（中央财经大学计算机学院）；School of Computing Technologies, RMIT University（皇家墨尔本理工大学计算技术学院）；M3S Research Unit, University of Oulu（奥卢大学M3S研究单位）；School of Computer Science, Nanjing University of Science（南京理工大学计算机学院）
Investigating the Potential of Large Language Model-Based Router Multi-Agent Architectures for Foundation Design Automation: A Task Classification and Expert Selection Study
King Mongkut's University of Technology Thonburi（国王科技大学）
Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems
Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen（数据科学与可视化系，信息学院，德布勒恩大学）；Doctoral School of Informatics, University of Debrecen（信息学博士学校，德布勒恩大学）；Neumann Technology Platform, Neumann Nonprofit Ltd.（内梅尔技术平台，内梅尔非营利有限公司）；Data-Driven Health Division of National Laboratory for Health Security, Health Services Management Training Centre, Semmelweis University（国家安全国家实验室数据驱动健康部，卫生服务管理培训中心，塞梅尔维斯大学）
Natural Language based Context Modeling and Reasoning for Ubiquitous Computing with Large Language Models: A Tutorial
Big Data Lab, Baidu, Inc.（百度大数据实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；Baidu, Inc.（百度公司）；Shanghai Jiaotong University（上海交通大学）；Télécom SudParis, Institut Polytechnique de Paris（巴黎综合理工学院电信南巴黎）
Estimating the Effects of Sample Training Orders for Large Language Models without Retraining
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学耿丽人工智能学院）；Center for Data Science, Peking University（北京大学数据科学中心）；School of Engineering Mathematics and Technology, University of Bristol（布里斯托大学工程数学与技术学院）；Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学机器学习系）
