In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilizati...In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.展开更多
This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with p...This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with patients. The system is hypothesized to improve healthcare accessibility, operational efficiency, and patient outcomes by automating routine tasks and delivering accurate health information. The assistant leverages natural language processing and real-time data retrieval models to respond to patient inquiries, schedule appointments, provide medication reminders, assist with symptom triage, and answer insurance-related questions. By integrating RAG-based virtual care, the system reduces the burden on healthcare specialists and helps mitigate healthcare disparities, particularly in rural areas where traditional care is limited. Although the initial scope of testing did not validate all potential benefits, the results demonstrated high patient satisfaction and strong response accuracy, both critical for systems of this nature. These findings underscore the transformative potential of AI-driven virtual health assistants in enhancing patient engagement, streamlining operational workflows, and improving healthcare accessibility, ultimately contributing to better outcomes and more cost-effective care delivery.展开更多
The emergence of artificial intelligence natural language large models has brought new dawn for the in-depth empowerment of the industry.Research on key technologies and applications of railway natural language large ...The emergence of artificial intelligence natural language large models has brought new dawn for the in-depth empowerment of the industry.Research on key technologies and applications of railway natural language large model is of great significance to promoting and coordinating the development of railway artificial intelligence.This paper puts forward the application scenarios of railway natural language large model according to the application requirements of railway artificial intelligence;designs the overall architecture of the railway natural language large model by relying on the railway artificial intelligence platform,studies the key technologies of the natural language large model,builds a railway industry large model oriented to intelligent question-answering,and verifies the model with actual data;finally,this paper prospects for the development and application of railway natural language large model from the aspects of railway traffic organization,railway operation safety and passenger service.展开更多
This paper investigates the transformative potential of Generative AI(Gen-AI)technologies,particularly large language models,within the building industry.By leveraging these advanced AI tools,the study explores their ...This paper investigates the transformative potential of Generative AI(Gen-AI)technologies,particularly large language models,within the building industry.By leveraging these advanced AI tools,the study explores their application across key areas such as automated compliance checking and building design assistance.The research highlights how Gen-AI can automate labor-intensive processes,significantly improving efficiency and reducing costs in building practices.The paper first discusses the two widely applied fundamental models—Transformer and Diffusion model—and summarizes current pathways for accessing Gen-AI models and the most common techniques for customizing them.It then explores applications for text generation,such as compliance checking,control support,data mining,and building simulation input file editing.Additionally,it examines image generation,including direct generation through diffusion models and indirect generation through language model-supported template creation based on existing Computer-Aided Design or other design tools with rendering.The paper concludes with a comprehensive analysis of the current capabilities of Gen-AI in the building industry,outlining future directions for research and development,with the goal of paving the way for smarter,more effective,and responsive design,construction,and operational practices.展开更多
Magnesium alloys,known for their lightweight advantages,are increasingly in demand across a range of applications,from aerospace to the automotive industry.With rising requirements for strength and corrosion resistanc...Magnesium alloys,known for their lightweight advantages,are increasingly in demand across a range of applications,from aerospace to the automotive industry.With rising requirements for strength and corrosion resistance,the development of new magnesium alloy systems has become critical.Phase diagrams play a crucial role in guiding the magnesium alloy design by providing key insights into phase stability,composition,and temperature ranges,enabling the optimization of alloy properties and processing conditions.However,accessing and interpreting phase diagram data with thermodynamic calculation software can be complex and time-consuming,often requiring intricate calculations and iterative refinement based on thermodynamic models.To address this challenge,we introduce PDGPT,a ChatGPT-based large language model designed to streamline the acquisition of magnesium alloys Phase Diagram information with high efficiency and accuracy.Enhanced by promptengineering,supervised fine-tuning and retrieval-augmented generation,PDGPT leverages the predictive and reasoning capabilities of large language models along with computational phase diagram data.By combining large language models with traditional phase diagram research tools,PDGPT not only improves the accessibility of critical phase diagram information but also sets the stage for future advancements in applying large language models to materials science.展开更多
This research explores the integration of large language models (LLMs) into scientific data assimilation, focusing on combustion science as a case study. Leveraging foundational models integrated with Retrieval-Augmen...This research explores the integration of large language models (LLMs) into scientific data assimilation, focusing on combustion science as a case study. Leveraging foundational models integrated with Retrieval-Augmented Generation (RAG) framework, the study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature. The multifaceted nature of combustion research emphasizes the critical role of knowledge processing in navigating and extracting valuable information from a vast and diverse pool of sources. The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy. It incorporates prompt engineering and offline open-source LLMs, offering user autonomy in selecting base models. The study provides a thorough examination of text segmentation strategies, conducts comparative studies between LLMs, and explores various optimized prompts to demonstrate the effectiveness of the framework. By incorporating an external vector database, the framework outperforms a conventional LLM in generating accurate responses and constructing robust arguments. Additionally, the study delves into the investigation of optimized prompt templates for the purpose of efficient extraction of scientific literature. Furthermore, we present a targeted scaling study to quantify the algorithmic performance of the framework as the number of prompt tokens increases. The research addresses concerns related to hallucinations and false research articles by introducing a custom workflow developed with a detection algorithm to filter out inaccuracies. Despite identified areas for improvement, the framework consistently delivers accurate domain-specific responses with minimal human oversight. The prompt-agnostic approach introduced holds promise for future improvements. The study underscores the significance of integrating LLMs and knowledge processing techniques in scientific research, providing a foundation for advancements in data assimilation and utilization.展开更多
文摘In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.
文摘This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with patients. The system is hypothesized to improve healthcare accessibility, operational efficiency, and patient outcomes by automating routine tasks and delivering accurate health information. The assistant leverages natural language processing and real-time data retrieval models to respond to patient inquiries, schedule appointments, provide medication reminders, assist with symptom triage, and answer insurance-related questions. By integrating RAG-based virtual care, the system reduces the burden on healthcare specialists and helps mitigate healthcare disparities, particularly in rural areas where traditional care is limited. Although the initial scope of testing did not validate all potential benefits, the results demonstrated high patient satisfaction and strong response accuracy, both critical for systems of this nature. These findings underscore the transformative potential of AI-driven virtual health assistants in enhancing patient engagement, streamlining operational workflows, and improving healthcare accessibility, ultimately contributing to better outcomes and more cost-effective care delivery.
文摘The emergence of artificial intelligence natural language large models has brought new dawn for the in-depth empowerment of the industry.Research on key technologies and applications of railway natural language large model is of great significance to promoting and coordinating the development of railway artificial intelligence.This paper puts forward the application scenarios of railway natural language large model according to the application requirements of railway artificial intelligence;designs the overall architecture of the railway natural language large model by relying on the railway artificial intelligence platform,studies the key technologies of the natural language large model,builds a railway industry large model oriented to intelligent question-answering,and verifies the model with actual data;finally,this paper prospects for the development and application of railway natural language large model from the aspects of railway traffic organization,railway operation safety and passenger service.
基金support of the U.S.Department of Energy’s Office of Energy Efficiency and Renewable Energy(EERE)through Battelle Memorial Institute under Contract No.DE-AC05-76RL01830.
文摘This paper investigates the transformative potential of Generative AI(Gen-AI)technologies,particularly large language models,within the building industry.By leveraging these advanced AI tools,the study explores their application across key areas such as automated compliance checking and building design assistance.The research highlights how Gen-AI can automate labor-intensive processes,significantly improving efficiency and reducing costs in building practices.The paper first discusses the two widely applied fundamental models—Transformer and Diffusion model—and summarizes current pathways for accessing Gen-AI models and the most common techniques for customizing them.It then explores applications for text generation,such as compliance checking,control support,data mining,and building simulation input file editing.Additionally,it examines image generation,including direct generation through diffusion models and indirect generation through language model-supported template creation based on existing Computer-Aided Design or other design tools with rendering.The paper concludes with a comprehensive analysis of the current capabilities of Gen-AI in the building industry,outlining future directions for research and development,with the goal of paving the way for smarter,more effective,and responsive design,construction,and operational practices.
基金the financial support provided by the National Natural Science Foundation of China(Grant Nos.52425101,52401216,52471012)Hongbin Zhang acknowledges also the funding by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)-Project-ID 405553726-TRR 270.
文摘Magnesium alloys,known for their lightweight advantages,are increasingly in demand across a range of applications,from aerospace to the automotive industry.With rising requirements for strength and corrosion resistance,the development of new magnesium alloy systems has become critical.Phase diagrams play a crucial role in guiding the magnesium alloy design by providing key insights into phase stability,composition,and temperature ranges,enabling the optimization of alloy properties and processing conditions.However,accessing and interpreting phase diagram data with thermodynamic calculation software can be complex and time-consuming,often requiring intricate calculations and iterative refinement based on thermodynamic models.To address this challenge,we introduce PDGPT,a ChatGPT-based large language model designed to streamline the acquisition of magnesium alloys Phase Diagram information with high efficiency and accuracy.Enhanced by promptengineering,supervised fine-tuning and retrieval-augmented generation,PDGPT leverages the predictive and reasoning capabilities of large language models along with computational phase diagram data.By combining large language models with traditional phase diagram research tools,PDGPT not only improves the accessibility of critical phase diagram information but also sets the stage for future advancements in applying large language models to materials science.
基金support from the Defense Threat Reduction Agency(DTRA)under Grant No.HDTRA12110012with Dr.Richard Fry as the Program Officer,and partial project support from the Air Force Office of Scientific Research(AFOSR)under Grant No.FA9550-24-1-0017with Dr.Chiping Li as the Program Officer.
文摘This research explores the integration of large language models (LLMs) into scientific data assimilation, focusing on combustion science as a case study. Leveraging foundational models integrated with Retrieval-Augmented Generation (RAG) framework, the study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature. The multifaceted nature of combustion research emphasizes the critical role of knowledge processing in navigating and extracting valuable information from a vast and diverse pool of sources. The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy. It incorporates prompt engineering and offline open-source LLMs, offering user autonomy in selecting base models. The study provides a thorough examination of text segmentation strategies, conducts comparative studies between LLMs, and explores various optimized prompts to demonstrate the effectiveness of the framework. By incorporating an external vector database, the framework outperforms a conventional LLM in generating accurate responses and constructing robust arguments. Additionally, the study delves into the investigation of optimized prompt templates for the purpose of efficient extraction of scientific literature. Furthermore, we present a targeted scaling study to quantify the algorithmic performance of the framework as the number of prompt tokens increases. The research addresses concerns related to hallucinations and false research articles by introducing a custom workflow developed with a detection algorithm to filter out inaccuracies. Despite identified areas for improvement, the framework consistently delivers accurate domain-specific responses with minimal human oversight. The prompt-agnostic approach introduced holds promise for future improvements. The study underscores the significance of integrating LLMs and knowledge processing techniques in scientific research, providing a foundation for advancements in data assimilation and utilization.
