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
针对自然语言到结构化查询语言(Natural Language to SQL,NL2SQL)问题在油气田勘探开发领域数据检索中的挑战,提出了一种基于大型NLP模型并融合外部知识库的智能数据检索新方法。首先,根据油气田勘探开发的业务场景构建种子数据,为模型...针对自然语言到结构化查询语言(Natural Language to SQL,NL2SQL)问题在油气田勘探开发领域数据检索中的挑战,提出了一种基于大型NLP模型并融合外部知识库的智能数据检索新方法。首先,根据油气田勘探开发的业务场景构建种子数据,为模型训练奠定基础。借助“思维链”策略扩充数据集,提升数据覆盖度和多样性。接着,通过引入低秩适应(Low-Rank Adaptation of Large Language Models,LoRA)算法流程,优化模型在油气田数据检索任务上的表现。同时,整合外部知识库以提高模型对油气田专业数据的预测准确性和鲁棒性。实验结果表明,该方法在油气田勘探开发领域私有数据的检索准确率相较现有技术提高了20%。基于此,开发了一套用户友好的应用系统,具有直观的界面和强大的功能,展示了该研究方法在油气田数据智能检索中的实用性和优越性。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.
文摘针对自然语言到结构化查询语言(Natural Language to SQL,NL2SQL)问题在油气田勘探开发领域数据检索中的挑战,提出了一种基于大型NLP模型并融合外部知识库的智能数据检索新方法。首先,根据油气田勘探开发的业务场景构建种子数据,为模型训练奠定基础。借助“思维链”策略扩充数据集,提升数据覆盖度和多样性。接着,通过引入低秩适应(Low-Rank Adaptation of Large Language Models,LoRA)算法流程,优化模型在油气田数据检索任务上的表现。同时,整合外部知识库以提高模型对油气田专业数据的预测准确性和鲁棒性。实验结果表明,该方法在油气田勘探开发领域私有数据的检索准确率相较现有技术提高了20%。基于此,开发了一套用户友好的应用系统,具有直观的界面和强大的功能,展示了该研究方法在油气田数据智能检索中的实用性和优越性。
