To the Editor,Artificial intelligence(AI)usage has been increasing.Many fields have implemented the use of AI and Large LanguageModels(LLMs),especially in medicine.Furthermore,manypatients have increasingly been using...To the Editor,Artificial intelligence(AI)usage has been increasing.Many fields have implemented the use of AI and Large LanguageModels(LLMs),especially in medicine.Furthermore,manypatients have increasingly been using AI;often,they will prompt AI with questions before even stepping into a physi-cian's office.The question lies in whether the information produced by AI is reliable and if this information is concise and easy to read across all patient populations.展开更多
The integration of large-scale foundation models(e.g.,GPT series and AlphaFold)into oncology is fundamentally transforming both research methodologies and clinical practices,driven by unprecedented advancements in com...The integration of large-scale foundation models(e.g.,GPT series and AlphaFold)into oncology is fundamentally transforming both research methodologies and clinical practices,driven by unprecedented advancements in computational power.This review synthesizes recent progress in the application of large language models to core oncological tasks,including medical imaging analysis,genomic interpretation,and personalized treatment planning.Underpinned by advanced computational infrastructures,such as graphics processing unit/tensor processing unit clusters,heterogeneous computing,and cloud platforms,these models enable superior representation learning and generalization across multimodal data sources.This review examines how these infrastructures overcome key bottlenecks in intelligent oncology through scalable optimization strategies,including mixed-precision training,memory optimization,and heterogeneous computing.Alongside these technical advancements,the review explores pressing challenges,such as data heterogeneity,limited model interpretability,regulatory uncertainties,and the environmental impact of artificial intelligence(AI)systems.Special emphasis is placed on emerging solutions,encompassing green AI and edge computing,which offer promising approaches for low-resource deployment scenarios.Additionally,the review highlights the critical role of interdisciplinary collaboration among oncology,computer science,ethics,and policy to ensure that AI systems are not only powerful but also transparent,safe,and clinically relevant.Finally,the review outlines potential avenues for future research aimed at developing robust,scalable,and human-centered frameworks for intelligent oncology.展开更多
Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate unt...Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate until the assistant behaves consistently.This paper investigates how far large language models(LLMs)can automate this development.In this paper,we use two reference corpora,Let’s Go(English,public transport)and MEDIA(French,hotel booking),to prompt four LLM families(GPT-4o,Claude,Gemini,Mistral Small)and generate the core specifications required by the rasa platform.These include intent sets with example utterances,entity definitions with slot mappings,response templates,and basic dialog flows.To structure this process,we introduce a model-and platform-agnostic pipelinewith two phases.The first normalizes and validates LLM-generated artifacts,enforcing crossfile consistency andmaking slot usage explicit.The second uses a lightweight dialog harness that runs scripted tests and incrementally patches failure points until conversations complete reliably.Across eight projects,all models required some targeted repairs before training.After applying our pipeline,all reached≥70%task completion(many above 84%),while NLU performance ranged from mid-0.6 to 1.0 macro-F1 depending on domain breadth.These results show that,with modest guidance,current LLMs can produce workable end-to-end dialog prototypes directly fromraw transcripts.Our main contributions are:(i)a reusable bootstrap method aligned with industry domain-specific languages(DSLs),(ii)a small set of high-impact corrective patterns,and(iii)a simple but effective harness for closed-loop refinement across conversational platforms.展开更多
We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We...We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We systematically analyze the technical pathways for deploying LLMs in disaster scenarios,emphasizing three breakthrough directions:(1)knowledge graph-driven dynamic risk modeling,(2)reinforcement learning-optimized emergency decision systems,and(3)secure local deployment architectures.The DeepSeek model demonstrates unique advantages through its hybrid reasoning mechanism combining semantic analysis with geospatial pattern recognition,enabling cost-effective processing of multi-source data spanning historical disaster records,real-time IoT sensor feeds,and socio-environmental parameters.A modular system architecture is designed to achieve three critical objectives:(a)automated construction of domain-specific knowledge graphs through unsupervised learning of disaster physics relationships,(b)scenario-adaptive resource allocation using risk simulations,and(c)preserving emergency coordination via federated learning across distributed response nodes.The proposed local deployment paradigm addresses critical data security concerns in cross-border disaster management while complying with the FAIR principles(Findable,Accessible,Interoperable,Reusable)for geoscientific data governance.This work establishes a methodological foundation for next-generation AI-earth science convergence in disaster mitigation.展开更多
With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medici...With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medicine and transportation.In this paper,we systematically expound on the intelligent decision-making technology and prospects driven by large AI models.Specifically,we first review the development of large AI models in recent years.Then,from the perspective of methods,we introduce important theories and technologies of large decision models,such as model architecture and model adaptation.Next,from the perspective of applications,we introduce the cutting-edge applications of large decision models in various fields,such as autonomous driving and knowledge decision-making.Finally,we discuss existing challenges,such as security issues,decision bias and hallucination phenomenon as well as future prospects,from both technology development and domain applications.We hope this review paper can help researchers understand the important progress of intelligent decision-making driven by large AI models.展开更多
The rapid advancement of Large Language Models(LLMs)has enabled their application in diverse professional domains,including law.However,research on automatic judicial document generation remains limited,particularly f...The rapid advancement of Large Language Models(LLMs)has enabled their application in diverse professional domains,including law.However,research on automatic judicial document generation remains limited,particularly for taiwan region of China courts.This study proposes a keyword-guided training framework that enhances LLMs’ability to generate structured and semantically coherent judicial decisions in Chinese.The proposed method first employs LLMs to extract representative legal keywords from absolute court judgments.Then it integrates these keywords into Supervised Fine-Tuning(SFT)and Reinforcement Learning withHuman Feedback using Proximal Policy Optimization(RLHF-PPO).Experimental evaluations using models such as Chinese Alpaca 7B and TAIDE-LX-7B demonstrate that keyword-guided training significantly improves generation quality,achieving ROUGE-1,ROUGE-2,and ROUGE-L score gains of up to 17%,16%,and 20%,respectively.The results confirm that the proposed framework effectively aligns generated judgments with human-written legal logic and structural conventions.This research advances domainadaptive LLM fine-tuning strategies and establishes a technical foundation forAI-assisted judicial document generation in the taiwan region of China legal context.This research provides empirical evidence that domain-adaptive LLM fine-tuning strategies can significantly improve performance in complex,structured legal text generation.展开更多
DeepSeek,a Chinese artificial intelligence(AI)startup,has released their V3 and R1 series models,which attracted global attention due to their low cost,high performance,and open-source advantages.This paper begins by ...DeepSeek,a Chinese artificial intelligence(AI)startup,has released their V3 and R1 series models,which attracted global attention due to their low cost,high performance,and open-source advantages.This paper begins by reviewing the evolution of large AI models focusing on paradigm shifts,the mainstream large language model(LLM)paradigm,and the DeepSeek paradigm.Subsequently,the paper highlights novel algorithms introduced by DeepSeek,including multi-head latent attention(MLA),mixture-of-experts(MoE),multi-token prediction(MTP),and group relative policy optimization(GRPO).The paper then explores DeepSeek's engineering breakthroughs in LLM scaling,training,inference,and system-level optimization architecture.Moreover,the impact of DeepSeek models on the competitive AI landscape is analyzed,comparing them to mainstream LLMs across various fields.Finally,the paper reflects on the insights gained from DeepSeek's innovations and discusses future trends in the technical and engineering development of large AI models,particularly in data,training,and reasoning.展开更多
Artificial intelligence technologies are rapidly evolving,with generative AI advancements—particularly those driven by large models—drawing significant attention.Large model technologies will play a pivotal role in ...Artificial intelligence technologies are rapidly evolving,with generative AI advancements—particularly those driven by large models—drawing significant attention.Large model technologies will play a pivotal role in railway intelligent operation and maintenance(O&M)by leveraging natural language as the medium.Based on the multi-source and heterogeneous data characteristics of railway infrastructure,this study investigates data analysis methods and application scenarios for railway infrastructure O&M leveraging large natural language models.An overall architecture is proposed for intelligent O&M of railway infrastructure,centered on railway large natural language models and featuring multi-source model synergy.This architecture is developed through a detailed analysis of O&M knowledge sources and structures,as well as data analysis requirements spanning the entire life cycle of railway infrastructure.These railwayspecific models are employed to derive railway intelligent O&M scenario models,which are driven by intelligent agent technologies and integrate traditional models,knowledge graphs,and other technologies to empower railway intelligent O&M.Further research focuses on key technologies,including the fine-tuning of railway large natural language models,retrievalaugmented generation,and AI agent technologies.These technologies are combined with the capabilities inherent in large natural language models—such as logical reasoning,content generation,and intelligent decision-making—to explore applications of large natural language models in inspection,repair,and maintenance of railway infrastructure,management of equipment maintenance information,equipment condition inspection,fault handling and emergency response in accidents,and intelligent O&M decision-making.展开更多
聚焦大型公共建筑尤其是体育场馆的智慧低碳运维问题,针对当前运维过程中存在的数据割裂、认知鸿沟与流程非标准化等痛点,提出了以大模型为核心的“AI as Hub”运维模式,并构建了数据标准化、认知标准化与流程标准化三位一体的“DCP”...聚焦大型公共建筑尤其是体育场馆的智慧低碳运维问题,针对当前运维过程中存在的数据割裂、认知鸿沟与流程非标准化等痛点,提出了以大模型为核心的“AI as Hub”运维模式,并构建了数据标准化、认知标准化与流程标准化三位一体的“DCP”架构。通过建立标准数据管理体系,实现从数据采集、建模、传输到开放的规范化;通过增强认知框架,将复杂物理实体逐级降维为大模型可理解的语义信息;并在流程层面形成“感知-决策-执行-反馈”的闭环机制。以杭州奥体中心的实践为例,体系化介绍了所述方法的应用过程与措施层面的实现。结果显示,场馆年度节电约517万kW·h,运营期能耗费用降低18%,碳排放降低2634 tCO_(2),并实现碳资产开发与交易,形成经济与环境双重效益。展开更多
文摘To the Editor,Artificial intelligence(AI)usage has been increasing.Many fields have implemented the use of AI and Large LanguageModels(LLMs),especially in medicine.Furthermore,manypatients have increasingly been using AI;often,they will prompt AI with questions before even stepping into a physi-cian's office.The question lies in whether the information produced by AI is reliable and if this information is concise and easy to read across all patient populations.
文摘The integration of large-scale foundation models(e.g.,GPT series and AlphaFold)into oncology is fundamentally transforming both research methodologies and clinical practices,driven by unprecedented advancements in computational power.This review synthesizes recent progress in the application of large language models to core oncological tasks,including medical imaging analysis,genomic interpretation,and personalized treatment planning.Underpinned by advanced computational infrastructures,such as graphics processing unit/tensor processing unit clusters,heterogeneous computing,and cloud platforms,these models enable superior representation learning and generalization across multimodal data sources.This review examines how these infrastructures overcome key bottlenecks in intelligent oncology through scalable optimization strategies,including mixed-precision training,memory optimization,and heterogeneous computing.Alongside these technical advancements,the review explores pressing challenges,such as data heterogeneity,limited model interpretability,regulatory uncertainties,and the environmental impact of artificial intelligence(AI)systems.Special emphasis is placed on emerging solutions,encompassing green AI and edge computing,which offer promising approaches for low-resource deployment scenarios.Additionally,the review highlights the critical role of interdisciplinary collaboration among oncology,computer science,ethics,and policy to ensure that AI systems are not only powerful but also transparent,safe,and clinically relevant.Finally,the review outlines potential avenues for future research aimed at developing robust,scalable,and human-centered frameworks for intelligent oncology.
基金This publication is part of the TrustBoost project,that has received funding from MICIU/AEI/10.13039/501100011033,from FEDER,UEIt is a coordinated project by a multidisciplinary team from the Universidad Politécnica de Madrid(UPM)and University of Granada(UGR),with two subprojects that address TrustBoost’s objectives:“Enhancing Trustworthiness in Conversational AI through Multimodal Affective Awareness”(Trust Boost-UPM,ref.PID2023-150584OB-C21)“Breaking the Duality of Conversational AI:Going beyond Guided Conversations While Ensuring Compliance with Domain Rules and Constraints”(Trust Boost-UGR,ref.PID2023-150584OB-C22).
文摘Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate until the assistant behaves consistently.This paper investigates how far large language models(LLMs)can automate this development.In this paper,we use two reference corpora,Let’s Go(English,public transport)and MEDIA(French,hotel booking),to prompt four LLM families(GPT-4o,Claude,Gemini,Mistral Small)and generate the core specifications required by the rasa platform.These include intent sets with example utterances,entity definitions with slot mappings,response templates,and basic dialog flows.To structure this process,we introduce a model-and platform-agnostic pipelinewith two phases.The first normalizes and validates LLM-generated artifacts,enforcing crossfile consistency andmaking slot usage explicit.The second uses a lightweight dialog harness that runs scripted tests and incrementally patches failure points until conversations complete reliably.Across eight projects,all models required some targeted repairs before training.After applying our pipeline,all reached≥70%task completion(many above 84%),while NLU performance ranged from mid-0.6 to 1.0 macro-F1 depending on domain breadth.These results show that,with modest guidance,current LLMs can produce workable end-to-end dialog prototypes directly fromraw transcripts.Our main contributions are:(i)a reusable bootstrap method aligned with industry domain-specific languages(DSLs),(ii)a small set of high-impact corrective patterns,and(iii)a simple but effective harness for closed-loop refinement across conversational platforms.
基金funded by the Chongqing Water Resources Bureau,China(Project No.CQS24C00836).
文摘We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We systematically analyze the technical pathways for deploying LLMs in disaster scenarios,emphasizing three breakthrough directions:(1)knowledge graph-driven dynamic risk modeling,(2)reinforcement learning-optimized emergency decision systems,and(3)secure local deployment architectures.The DeepSeek model demonstrates unique advantages through its hybrid reasoning mechanism combining semantic analysis with geospatial pattern recognition,enabling cost-effective processing of multi-source data spanning historical disaster records,real-time IoT sensor feeds,and socio-environmental parameters.A modular system architecture is designed to achieve three critical objectives:(a)automated construction of domain-specific knowledge graphs through unsupervised learning of disaster physics relationships,(b)scenario-adaptive resource allocation using risk simulations,and(c)preserving emergency coordination via federated learning across distributed response nodes.The proposed local deployment paradigm addresses critical data security concerns in cross-border disaster management while complying with the FAIR principles(Findable,Accessible,Interoperable,Reusable)for geoscientific data governance.This work establishes a methodological foundation for next-generation AI-earth science convergence in disaster mitigation.
基金supported by the National Natural Science Foundation of China(Grant 62293545)Shenzhen Science and Technology Program(Grant ZDSYS20220323112000001).
文摘With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medicine and transportation.In this paper,we systematically expound on the intelligent decision-making technology and prospects driven by large AI models.Specifically,we first review the development of large AI models in recent years.Then,from the perspective of methods,we introduce important theories and technologies of large decision models,such as model architecture and model adaptation.Next,from the perspective of applications,we introduce the cutting-edge applications of large decision models in various fields,such as autonomous driving and knowledge decision-making.Finally,we discuss existing challenges,such as security issues,decision bias and hallucination phenomenon as well as future prospects,from both technology development and domain applications.We hope this review paper can help researchers understand the important progress of intelligent decision-making driven by large AI models.
文摘The rapid advancement of Large Language Models(LLMs)has enabled their application in diverse professional domains,including law.However,research on automatic judicial document generation remains limited,particularly for taiwan region of China courts.This study proposes a keyword-guided training framework that enhances LLMs’ability to generate structured and semantically coherent judicial decisions in Chinese.The proposed method first employs LLMs to extract representative legal keywords from absolute court judgments.Then it integrates these keywords into Supervised Fine-Tuning(SFT)and Reinforcement Learning withHuman Feedback using Proximal Policy Optimization(RLHF-PPO).Experimental evaluations using models such as Chinese Alpaca 7B and TAIDE-LX-7B demonstrate that keyword-guided training significantly improves generation quality,achieving ROUGE-1,ROUGE-2,and ROUGE-L score gains of up to 17%,16%,and 20%,respectively.The results confirm that the proposed framework effectively aligns generated judgments with human-written legal logic and structural conventions.This research advances domainadaptive LLM fine-tuning strategies and establishes a technical foundation forAI-assisted judicial document generation in the taiwan region of China legal context.This research provides empirical evidence that domain-adaptive LLM fine-tuning strategies can significantly improve performance in complex,structured legal text generation.
基金supported by the National Natural Science Foundation of China(62233005,62293502,U2441245,62176185,U23B2057,62306112)the STCSM Science and Technology Innovation Action Plan Computational Biology Program(24JS2830400)+2 种基金the State Key Laboratory of Industrial Control Technology,China(ICT2024A22)the Shanghai Sailing Program(23YF1409400)the National Science and Technology Major Project(2024ZD0532403).
文摘DeepSeek,a Chinese artificial intelligence(AI)startup,has released their V3 and R1 series models,which attracted global attention due to their low cost,high performance,and open-source advantages.This paper begins by reviewing the evolution of large AI models focusing on paradigm shifts,the mainstream large language model(LLM)paradigm,and the DeepSeek paradigm.Subsequently,the paper highlights novel algorithms introduced by DeepSeek,including multi-head latent attention(MLA),mixture-of-experts(MoE),multi-token prediction(MTP),and group relative policy optimization(GRPO).The paper then explores DeepSeek's engineering breakthroughs in LLM scaling,training,inference,and system-level optimization architecture.Moreover,the impact of DeepSeek models on the competitive AI landscape is analyzed,comparing them to mainstream LLMs across various fields.Finally,the paper reflects on the insights gained from DeepSeek's innovations and discusses future trends in the technical and engineering development of large AI models,particularly in data,training,and reasoning.
文摘Artificial intelligence technologies are rapidly evolving,with generative AI advancements—particularly those driven by large models—drawing significant attention.Large model technologies will play a pivotal role in railway intelligent operation and maintenance(O&M)by leveraging natural language as the medium.Based on the multi-source and heterogeneous data characteristics of railway infrastructure,this study investigates data analysis methods and application scenarios for railway infrastructure O&M leveraging large natural language models.An overall architecture is proposed for intelligent O&M of railway infrastructure,centered on railway large natural language models and featuring multi-source model synergy.This architecture is developed through a detailed analysis of O&M knowledge sources and structures,as well as data analysis requirements spanning the entire life cycle of railway infrastructure.These railwayspecific models are employed to derive railway intelligent O&M scenario models,which are driven by intelligent agent technologies and integrate traditional models,knowledge graphs,and other technologies to empower railway intelligent O&M.Further research focuses on key technologies,including the fine-tuning of railway large natural language models,retrievalaugmented generation,and AI agent technologies.These technologies are combined with the capabilities inherent in large natural language models—such as logical reasoning,content generation,and intelligent decision-making—to explore applications of large natural language models in inspection,repair,and maintenance of railway infrastructure,management of equipment maintenance information,equipment condition inspection,fault handling and emergency response in accidents,and intelligent O&M decision-making.
文摘聚焦大型公共建筑尤其是体育场馆的智慧低碳运维问题,针对当前运维过程中存在的数据割裂、认知鸿沟与流程非标准化等痛点,提出了以大模型为核心的“AI as Hub”运维模式,并构建了数据标准化、认知标准化与流程标准化三位一体的“DCP”架构。通过建立标准数据管理体系,实现从数据采集、建模、传输到开放的规范化;通过增强认知框架,将复杂物理实体逐级降维为大模型可理解的语义信息;并在流程层面形成“感知-决策-执行-反馈”的闭环机制。以杭州奥体中心的实践为例,体系化介绍了所述方法的应用过程与措施层面的实现。结果显示,场馆年度节电约517万kW·h,运营期能耗费用降低18%,碳排放降低2634 tCO_(2),并实现碳资产开发与交易,形成经济与环境双重效益。
