Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from...Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.展开更多
The emergence of Medical Large Language Models has significantly transformed healthcare.Medical Large Language Models(Med-LLMs)serve as transformative tools that enhance clinical practice through applications in decis...The emergence of Medical Large Language Models has significantly transformed healthcare.Medical Large Language Models(Med-LLMs)serve as transformative tools that enhance clinical practice through applications in decision support,documentation,and diagnostics.This evaluation examines the performance of leading Med-LLMs,including GPT-4Med,Med-PaLM,MEDITRON,PubMedGPT,and MedAlpaca,across diverse medical datasets.It provides graphical comparisons of their effectiveness in distinct healthcare domains.The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making,documentation,drug discovery,research,patient interaction,and public health.The paper addresses deployment challenges of Medical-LLMs,emphasizing trustworthiness and explainability as essential requirements for healthcare AI.It presents current evaluation techniques that improve model transparency in high-stakes medical contexts and analyzes regulatory frameworks using benchmarking datasets such asMedQA,MedMCQA,PubMedQA,and MIMIC.By identifying ongoing challenges in biasmitigation,reliability,and ethical compliance,thiswork serves as a resource for selecting appropriate Med-LLMs and outlines future directions in the field.This analysis offers a roadmap for developing Med-LLMs that balance technological innovation with the trust and transparency required for clinical integration,a perspective often overlooked in existing literature.展开更多
建筑信息模型(BIM)以其多维模型和多源数据集成的优势,正成为推动公路设计行业创新和数字化转型的关键技术,以BIM模型作为设计交付成果已成为未来公路工程设计领域发展的必然趋势。然而,现阶段公路BIM模型的审查工作以人工手动肉眼审查...建筑信息模型(BIM)以其多维模型和多源数据集成的优势,正成为推动公路设计行业创新和数字化转型的关键技术,以BIM模型作为设计交付成果已成为未来公路工程设计领域发展的必然趋势。然而,现阶段公路BIM模型的审查工作以人工手动肉眼审查为主,存在效率低、易出错、主观性强等问题,难以适应三维数字化设计模式的审查需求。针对该问题,该文提出一种基于知识图谱的公路BIM模型自动审查方法,通过构建公路工程领域的知识图谱,涵盖公路设计标准规范、语义库、元结构等多维度知识,利用自然语言处理技术(NLP)对设计标准规范、条文的审查规则进行结构化处理,进而以IFC(Industry Foundation Classes)构件实体为对象,利用Cypher查询语言实现对公路BIM模型构件属性信息完整性、数据正确性和设计合规性的审查。结果表明:基于知识图谱技术的图数据库,可以为公路BIM模型设计成果审查提供技术方法,显著提升公路三维设计成果的质量和审查效率。展开更多
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[RS-2021-II211341,Artificial Intelligence Graduate School Program(Chung-Ang University)],and by the Chung-Ang University Graduate Research Scholarship in 2024.
文摘Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.
文摘The emergence of Medical Large Language Models has significantly transformed healthcare.Medical Large Language Models(Med-LLMs)serve as transformative tools that enhance clinical practice through applications in decision support,documentation,and diagnostics.This evaluation examines the performance of leading Med-LLMs,including GPT-4Med,Med-PaLM,MEDITRON,PubMedGPT,and MedAlpaca,across diverse medical datasets.It provides graphical comparisons of their effectiveness in distinct healthcare domains.The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making,documentation,drug discovery,research,patient interaction,and public health.The paper addresses deployment challenges of Medical-LLMs,emphasizing trustworthiness and explainability as essential requirements for healthcare AI.It presents current evaluation techniques that improve model transparency in high-stakes medical contexts and analyzes regulatory frameworks using benchmarking datasets such asMedQA,MedMCQA,PubMedQA,and MIMIC.By identifying ongoing challenges in biasmitigation,reliability,and ethical compliance,thiswork serves as a resource for selecting appropriate Med-LLMs and outlines future directions in the field.This analysis offers a roadmap for developing Med-LLMs that balance technological innovation with the trust and transparency required for clinical integration,a perspective often overlooked in existing literature.
文摘建筑信息模型(BIM)以其多维模型和多源数据集成的优势,正成为推动公路设计行业创新和数字化转型的关键技术,以BIM模型作为设计交付成果已成为未来公路工程设计领域发展的必然趋势。然而,现阶段公路BIM模型的审查工作以人工手动肉眼审查为主,存在效率低、易出错、主观性强等问题,难以适应三维数字化设计模式的审查需求。针对该问题,该文提出一种基于知识图谱的公路BIM模型自动审查方法,通过构建公路工程领域的知识图谱,涵盖公路设计标准规范、语义库、元结构等多维度知识,利用自然语言处理技术(NLP)对设计标准规范、条文的审查规则进行结构化处理,进而以IFC(Industry Foundation Classes)构件实体为对象,利用Cypher查询语言实现对公路BIM模型构件属性信息完整性、数据正确性和设计合规性的审查。结果表明:基于知识图谱技术的图数据库,可以为公路BIM模型设计成果审查提供技术方法,显著提升公路三维设计成果的质量和审查效率。
