Knowledge base (KB) is an important aspect in artificial intelligence. One significant challenge faced by KB construction is that it contains many noises, which prevents its effective usage. Even though some KB cleansing algorithms have been proposed, they focus on the structure of the knowledge graph and neglect the relation between the concepts, which could be helpful to discover wrong relations in KB. Motived by this, we measure the relation of two concepts by the distance between their corresponding instances and detect errors within the intersection of the conflicting concept sets. For efficient and effective knowledge base cleansing, we first apply a distance-based Model to determine the conflicting concept sets using two different methods. Then, we propose and analyze several algorithms on how to detect and repairing the errors based on our model, where we use hash method for an efficient way to calculate distance. Experimental results demonstrate that the proposed approaches could cleanse the knowledge bases efficiently and effectively.
IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection
Jheronimus Academy of Data Science（Jheronimus数据科学学院）；Tilburg University（蒂尔堡大学）；Eindhoven University of Technology（埃因霍温理工大学）；University of Sannio（萨诺尼大学）
Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Construction and Large-Scale Evaluation
Stanford Center for Biomedical Informatics Research（斯坦福大学生物医学信息学研究中心）；Department of Medicine, Stanford University（斯坦福大学医学院）；Department of Biomedical Data Science, Stanford University（斯坦福大学生物医学数据科学系）；Stanford Clinical Excellence Research Center, Stanford University（斯坦福大学临床卓越研究中心）
Radiology's Last Exam (RadLE): Benchmarking Frontier Multimodal AI Against Human Experts and a Taxonomy of Visual Reasoning Errors in Radiology
Centre for Responsible Autonomous Systems in Healthcare (CRASH) Lab, Koita Centre for Digital Health（负责任的自主医疗系统中心（CRASH）实验室、Koita数字健康中心）；Ashoka University（阿什oka大学）；Independent Researcher（独立研究者）；Koita Centre for Digital Health（Koita数字健康中心）
Cognitive bias in LLM reasoning compromises interpretation of clinical oncology notes
Mayo Clinic College of Medicine and Science（梅奥诊所医学与科学学院）；School of Computing and AI, Arizona State University（亚利桑那州立大学计算与人工智能学院）；Mayo Clinic Comprehensive Cancer Center（梅奥诊所综合癌症中心）；Department of Radiation Oncology, Mayo Clinic（梅奥诊所放射肿瘤科）；Department of Oncology, Mayo Clinic（梅奥诊所肿瘤科）；Department of Artificial Intelligence and Informatics, Mayo Clinic（梅奥诊所人工智能与信息学系）；Dana-Farber Cancer Institute, Harvard Medical School（哈佛医学院丹娜-法伯癌症研究所）
