目前医院财务领域亟需一种能够高效、准确理解自然语言查询,并智能检索复杂财务数据的专业化解决方案,以支持决策和提升管理效率。针对医院财务数据检索中自然语言到结构化查询语言(Natural Language to SQL,NL2SQL)的挑战,文章提出了...目前医院财务领域亟需一种能够高效、准确理解自然语言查询,并智能检索复杂财务数据的专业化解决方案,以支持决策和提升管理效率。针对医院财务数据检索中自然语言到结构化查询语言(Natural Language to SQL,NL2SQL)的挑战,文章提出了一种结合大语言模型的医院财务数据智能检索方法。首先,基于医院财务业务场景构建数据集,为模型训练提供了基础支持,并通过应用思维链策略扩展数据集,提升其覆盖范围和多样性。随后,采用低秩适应(LoRA)算法,进一步优化模型在医院财务数据检索任务中的表现。实验结果表明,该方法在医院私有财务数据的检索准确率上相比现有主流模型BERT提升了20.5%,充分展示了该方法在医院财务数据智能检索中的应用价值与优越性。展开更多
在智慧城市发展进程中,交通系统的精细化管理和智能化服务面临海量异构数据处理的挑战。传统交通信息查询系统存在数据源异构性强、自然语言交互能力不足、长尾查询场景覆盖有限等问题。文章基于ChatGLM3大语言模型,创新性地构建了融合N...在智慧城市发展进程中,交通系统的精细化管理和智能化服务面临海量异构数据处理的挑战。传统交通信息查询系统存在数据源异构性强、自然语言交互能力不足、长尾查询场景覆盖有限等问题。文章基于ChatGLM3大语言模型,创新性地构建了融合NL2SQL(Natural Language to Structured Query Language)技术的智能问数系统,通过动态Schema对齐、LoRA微调优化及多维度提示工程技术,实现了交通领域复杂自然语言查询到精准SQL指令的智能转换。实验结果表明,经过微调的模型在交通信息查询任务中准确率达到78.9%,较基线模型提升15.8个百分点。本研究为交通管理智能化转型提供了创新技术路径,并对大模型在垂直领域的深度适配进行了系统性探索。展开更多
With the increasing use of web applications,challenges in the field of cybersecurity are becoming more complex.This paper explores the application of fine-tuned large language models(LLMs)for the automatic generation ...With the increasing use of web applications,challenges in the field of cybersecurity are becoming more complex.This paper explores the application of fine-tuned large language models(LLMs)for the automatic generation of synthetic attacks,including XSS(Cross-Site Scripting),SQL Injections,and Command Injections.A web application has been developed that allows penetration testers to quickly generate high-quality payloads without the need for in-depth knowledge of artificial intelligence.The fine-tuned language model demonstrates the capability to produce synthetic payloads that closely resemble real-world attacks.This approach not only improves the model’s precision and dependability but also serves as a practical resource for cybersecurity professionals to enhance the security of web applications.The methodology and structured implementation underscore the importance and potential of advanced language models in cybersecurity,illustrating their effectiveness in generating high-quality synthetic data for penetration testing purposes.The research results demonstrate that this approach enables the identification of vulnerabilities that traditional methods may not uncover,providing deeper insights into potential threats and enhancing overall security measures.The performance evaluation of the model indicated satisfactory results,while further hyperparameter optimization could improve accuracy and generalization capabilities.This research represents a significant step forward in improving web application security and opens new opportunities for the use of LLMs in security testing,thereby contributing to the development of more effective cybersecurity strategies.展开更多
为解决铁路编组站工作人员难以快速查询生产数据和规章制度的问题,基于国产大模型DeepSeek-R1,设计了一套编组站智询系统。通过本地化私有部署DeepSeek-R1并将其与编组站综合自动化系统(CIPS,Computer Integrated Process System)安全集...为解决铁路编组站工作人员难以快速查询生产数据和规章制度的问题,基于国产大模型DeepSeek-R1,设计了一套编组站智询系统。通过本地化私有部署DeepSeek-R1并将其与编组站综合自动化系统(CIPS,Computer Integrated Process System)安全集成,结合分层智能体架构、混合检索及NL2SQL(Natural Language to Structured Query Language)技术,构建了自然语言交互式服务,支持工作人员实时获取列车状态、调车计划等生产数据及规章知识。应用表明,该系统能够准确回答用户提出的问题,为铁路货运智能化提供技术支撑。展开更多
针对J2EE信息系统开发阶段从性能优化的角度实现了数据访问层的设计和一个简易的性能监测工具.首先在数据访问层设计阶段,兼顾考虑开发效率和性能问题提供专门的数据访问操作方法;利用面向方面编程(AOP,Aspect-Oriented Programm ing)...针对J2EE信息系统开发阶段从性能优化的角度实现了数据访问层的设计和一个简易的性能监测工具.首先在数据访问层设计阶段,兼顾考虑开发效率和性能问题提供专门的数据访问操作方法;利用面向方面编程(AOP,Aspect-Oriented Programm ing)技术对系统数据库访问操作及性能指标进行监视,帮助开发者在系统调试运行阶段根据SQL语句执行情况发现数据访问过程中影响性能的因素;开发根据具体的情况调整数据访问的源代码,然后对造成系统性能瓶颈的SQL语句进行性能调优,并替换数据访问层中低效的SQL语句,最终达到优化系统数据访问性能的目的.展开更多
文摘目前医院财务领域亟需一种能够高效、准确理解自然语言查询,并智能检索复杂财务数据的专业化解决方案,以支持决策和提升管理效率。针对医院财务数据检索中自然语言到结构化查询语言(Natural Language to SQL,NL2SQL)的挑战,文章提出了一种结合大语言模型的医院财务数据智能检索方法。首先,基于医院财务业务场景构建数据集,为模型训练提供了基础支持,并通过应用思维链策略扩展数据集,提升其覆盖范围和多样性。随后,采用低秩适应(LoRA)算法,进一步优化模型在医院财务数据检索任务中的表现。实验结果表明,该方法在医院私有财务数据的检索准确率上相比现有主流模型BERT提升了20.5%,充分展示了该方法在医院财务数据智能检索中的应用价值与优越性。
文摘在智慧城市发展进程中,交通系统的精细化管理和智能化服务面临海量异构数据处理的挑战。传统交通信息查询系统存在数据源异构性强、自然语言交互能力不足、长尾查询场景覆盖有限等问题。文章基于ChatGLM3大语言模型,创新性地构建了融合NL2SQL(Natural Language to Structured Query Language)技术的智能问数系统,通过动态Schema对齐、LoRA微调优化及多维度提示工程技术,实现了交通领域复杂自然语言查询到精准SQL指令的智能转换。实验结果表明,经过微调的模型在交通信息查询任务中准确率达到78.9%,较基线模型提升15.8个百分点。本研究为交通管理智能化转型提供了创新技术路径,并对大模型在垂直领域的深度适配进行了系统性探索。
基金supported by the Ministry of Science,Technological Development and Innovation of the Republic of Serbia,and these results are parts of Grant No.451-03-66/2024-03/200132 with the University of Kragujevac-Faculty of Technical Sciences Cacak.
文摘With the increasing use of web applications,challenges in the field of cybersecurity are becoming more complex.This paper explores the application of fine-tuned large language models(LLMs)for the automatic generation of synthetic attacks,including XSS(Cross-Site Scripting),SQL Injections,and Command Injections.A web application has been developed that allows penetration testers to quickly generate high-quality payloads without the need for in-depth knowledge of artificial intelligence.The fine-tuned language model demonstrates the capability to produce synthetic payloads that closely resemble real-world attacks.This approach not only improves the model’s precision and dependability but also serves as a practical resource for cybersecurity professionals to enhance the security of web applications.The methodology and structured implementation underscore the importance and potential of advanced language models in cybersecurity,illustrating their effectiveness in generating high-quality synthetic data for penetration testing purposes.The research results demonstrate that this approach enables the identification of vulnerabilities that traditional methods may not uncover,providing deeper insights into potential threats and enhancing overall security measures.The performance evaluation of the model indicated satisfactory results,while further hyperparameter optimization could improve accuracy and generalization capabilities.This research represents a significant step forward in improving web application security and opens new opportunities for the use of LLMs in security testing,thereby contributing to the development of more effective cybersecurity strategies.
文摘为解决铁路编组站工作人员难以快速查询生产数据和规章制度的问题,基于国产大模型DeepSeek-R1,设计了一套编组站智询系统。通过本地化私有部署DeepSeek-R1并将其与编组站综合自动化系统(CIPS,Computer Integrated Process System)安全集成,结合分层智能体架构、混合检索及NL2SQL(Natural Language to Structured Query Language)技术,构建了自然语言交互式服务,支持工作人员实时获取列车状态、调车计划等生产数据及规章知识。应用表明,该系统能够准确回答用户提出的问题,为铁路货运智能化提供技术支撑。
文摘针对J2EE信息系统开发阶段从性能优化的角度实现了数据访问层的设计和一个简易的性能监测工具.首先在数据访问层设计阶段,兼顾考虑开发效率和性能问题提供专门的数据访问操作方法;利用面向方面编程(AOP,Aspect-Oriented Programm ing)技术对系统数据库访问操作及性能指标进行监视,帮助开发者在系统调试运行阶段根据SQL语句执行情况发现数据访问过程中影响性能的因素;开发根据具体的情况调整数据访问的源代码,然后对造成系统性能瓶颈的SQL语句进行性能调优,并替换数据访问层中低效的SQL语句,最终达到优化系统数据访问性能的目的.
