The development of a large language model (LLM) infrastructure is a pivotal undertaking in artificial intelligence. This paper explores the intricate landscape of LLM infrastructure, software, and data management. By analyzing these core components, we emphasize the pivotal considerations and safeguards crucial for successful LLM development. This work presents a concise synthesis of the challenges and strategies inherent in constructing a robust and effective LLM infrastructure, offering valuable insights for researchers and practitioners alike.
GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure Segmentation
Department of Computer Science, Wayne State University, Detroit, MI, USA 48202（计算机科学系，韦恩州立大学）；Department of Radiation Oncology, Henry Ford Health, Detroit, MI, USA 48202（放射肿瘤科，亨利福特健康）；Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA 43210（电气与计算机工程系，俄亥俄州立大学）
MedBuild AI: An Agent-Based Hybrid Intelligence Framework for Reshaping Agency in Healthcare Infrastructure Planning through Generative Design for Medical Architecture
School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture（北京建筑大学建筑与城市规划学院）
What Makes LVLMs Hallucinate Less? Unveiling the Architectural Factors Behind Hallucination Robustness
School of Computer Science, Engineering Research Center of Machine Learning and Industry Intelligence, Sichuan University（计算机科学学院，机器学习与产业智能工程研究中心，四川大学）；School of Computer and Information Engineering, Xiamen University of Technology（计算机与信息工程学院，厦门理工大学）；Department of Electrical and Computer Engineering, University of Hong Kong（电气与计算机工程系，香港大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
