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LLMKB:Large Language Models with Knowledge Base Augmentation for Conversational Recommendation 认领 引用
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作者 FANG Xiu QIU Sijia +1 位作者 SUN Guohao LU Jinhu 《Journal of Donghua University(English Edition)》 CAS 2026年第1期91-103,共13页
Conversational recommender systems(CRSs)focus on refining preferences and providing personalized recommendations through natural language interactions and dialogue history.Large language models(LLMs)have shown outstan... Conversational recommender systems(CRSs)focus on refining preferences and providing personalized recommendations through natural language interactions and dialogue history.Large language models(LLMs)have shown outstanding performance across various domains,thereby prompting researchers to investigate their applicability in recommendation systems.However,due to the lack of task-specific knowledge and an inefficient feature extraction process,LLMs still have suboptimal performance in recommendation tasks.Therefore,external knowledge sources,such as knowledge graphs(KGs)and knowledge bases(KBs),are often introduced to address the issue of data sparsity.Compared to KGs,KBs possess higher retrieval efficiency,making them more suitable for scenarios where LLMs serve as recommenders.To this end,we introduce a novel framework integrating LLMs with KBs for enhanced retrieval generation,namely LLMKB.LLMKB initially leverages structured knowledge to create mapping dictionaries,extracting entity-relation information from heterogeneous knowledge to construct KBs.Then,LLMKB achieves the embedding calibration between user information representations and documents in KBs through retrieval model fine-tuning.Finally,LLMKB employs retrievalaugmented generation to produce recommendations based on fused text inputs,followed by post-processing.Experiment results on two public CRS datasets demonstrate the effectiveness of our framework.Our code is publicly available at the link:http://gffzze280b34df20547e2s9o9uo9n0nwvq666u.ffgz.tsg.suse.edu.cn/LLMKB-6FD0. 展开更多
关键词 recommender system large language model(LLM) knowledge base(KB)
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From LLM to Agent:A large-language-model-driven machine learning framework for catalyst design of MgH2dehydrogenation 认领 引用
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作者 Tongao Yao Yang Yang +7 位作者 Jianghao Cai Rui Liu Zhaoyan Dong Xiaotian Tang Xuqiang Shao Zhengyang Gao Guangyao An Weijie Yang 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2026年第1期410-426,共17页
Magnesium hydride(MgH2),a promising high-capacity hydrogen storage material,is hindered by slow dehydrogenation kinetics.AIdriven catalyst discovery to address this is often hampered by the laborious extraction of ... Magnesium hydride(MgH2),a promising high-capacity hydrogen storage material,is hindered by slow dehydrogenation kinetics.AIdriven catalyst discovery to address this is often hampered by the laborious extraction of data from unstructured literature.To overcome this,we introduce a transformative“LLM to Agent”framework that synergistically integrates Large Language Models(LLMs)for automated data curation with Machine Learning(ML)for predictive design.We automatically constructed a comprehensive database of 809 MgH2catalysts(6555 data rows)with high fidelity and an~40-fold acceleration over manual methods.The resulting ML models achieved high accuracy(average R2>0.91)in predicting dehydrogenation temperature and activation energy,subsequently guiding a Genetic Algorithm(GA)in an exploratory inverse design that autonomously uncovered key design principles for high-performance catalysts.Encouragingly,a strong alignment was found between these AI-discovered principles and the design strategies of recently reported,state-of-the-art experimental systems,providing substantial evidence for the validity of our approach.The framework culminates in Cat-Advisor,a novel,domain-adapted multi-agent system.Cat-Advisor translates ML predictions and retrieval-augmented knowledge into actionable design guidance,demonstrating capabilities that surpass those of general-purpose LLMs in this specialized domain.This work delivers a practical AI toolkit for accelerated materials discovery and advances the emerging Agent-based paradigm for designing next-generation energy technologies. 展开更多
关键词 MgH2dehydrogenation Large language model Machine learning Genetic algorithm Catalyst design Hydrogen storage
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PrivLLM-Guard: A Differentially-Private Large Language Model for Real-Time Confidential Medical Text Generation and Summarization 认领 引用
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作者 Ans D.Alghamdi 《Computers, Materials & Continua》 SCIE EI 2026年第6期1687-1727,共41页
How can AI assist doctors in generating clinical reports without compromising patient privacy?This question motivates our development of PrivLLM-Guard,a novel framework for differentially private large language models... How can AI assist doctors in generating clinical reports without compromising patient privacy?This question motivates our development of PrivLLM-Guard,a novel framework for differentially private large language models(LLMs)tailored to real-time confidential medical text generation and summarization.While LLMs have shown promise in automating clinical documentation,the sensitivity of healthcare data demands rigorous privacy protections.PrivLLM-Guard addresses this need by combining advanced—differential privacy techniques with adaptive noise calibration,ensuring robust privacy guarantees without sacrificing utility.The framework integrates bidirectional transformer encoders with autoregressive decoders,further enhanced by privacy-aware attention and gradient perturbation mechanisms.Extensive experiments on three large-scale medical datasets demonstrate BLEU-4 scores of 89.7%for generation and ROUGE-L scores of 92.3%for summarization,while maintaining strict privacy budgets.The model processes 512-token sequences in real time with an average latency of 245 ms and memory usage of just 4.2 GB.Compared to state-of-the-art privacy-preserving LLMs,PrivLLM-Guard improves the utility-privacy trade-off by 15.8%and reduces computational overhead by 23.4%.Key contributions include adaptive noise injection,dynamic privacy budgeting,and an integrated privacy auditing module—collectively advancing secure and trustworthy AI deployment in clinical environments. 展开更多
关键词 Differential privacy large language models medical text generation privacy-preserving computing healthcare AI text summarization real-time processing
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When Federated Learning Meets Large Language Models:Taxonomy,Challenges,and Opportunities 认领 引用
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作者 Shan Jiang Wenxin You +2 位作者 Haoran Zhang Shichang Xuan Jiaxing Shen 《Computers, Materials & Continua》 SCIE EI 2026年第8期1-34,共34页
Large Language Models(LLMs)have been playing a transformative role in natural language understanding and generation,yet adapting LLMs to domain-specific and privacy-sensitive data remains challenging under centralized... Large Language Models(LLMs)have been playing a transformative role in natural language understanding and generation,yet adapting LLMs to domain-specific and privacy-sensitive data remains challenging under centralized training.Federated Learning(FL)provides a promising alternative by enabling training LLMs collaboratively without sharing raw data.However,integrating FL and LLMs introduces new challenges,including model size,device heterogeneity,non-IID data,and alignment requirements.This survey offers a structured overview of the federated LLM ecosystem.We present a comprehensive taxonomy encompassing system architectures,advanced data strategies for addressing heterogeneity,and retrieval-augmented generation in federated contexts.Additionally,we review efficient adaptation methods that enable LLM tuning on resource-constrained clients and analyze data security and privacy concerns.We conclude by summarizing emerging applications in healthcare,industry,software engineering,and finance,and by outlining open problems and research opportunities for scalable,secure,and responsible federated LLM deployment. 展开更多
关键词 Large language models federated learning foundation models federated large language models
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Domain-Specific Large Language Model for Maintenance Decision-Making on Wind Farms by Labeled-Data-Supervised Fine-Tuning 认领 引用
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作者 Dongming Fan Meng Liu +5 位作者 Yi Shao Linchao Yang Yiliu Liu Yue Zhang Yi Ren Zili Wang 《Engineering》 SCIE EI CSCD 2026年第5期343-361,共19页
Wind farm operators always need a better maintenance strategy to increase resource utilization efficiency while controlling operation and maintenance costs.However,conventional maintenance decision-making approaches a... Wind farm operators always need a better maintenance strategy to increase resource utilization efficiency while controlling operation and maintenance costs.However,conventional maintenance decision-making approaches are time-consuming and have poor flexibility and adaptability to various scenarios.This study addressed these challenges by using a large language model(LLM)to understand,generate,and plan maintenance strategies for wind farms characterized by various failure modes and maintenance costs.A labelled-data-supervised fine-tuning LLM for maintenance,named LLM4M,is proposed.The proposed LLM4M model is trained on an extensive dataset of mathematical programs for maintenance to generate optimal strategies for wind farms.Compared with other large parameter LLMs,the fine-tuned LLM4M model demonstrates remarkable accuracy,with an error of approximately 2%from the optimal strategy.In addition,the generalization of the proposed LLM4M model has achieved remarkable results.If the LLM4M model correctly generates the maintenance strategy,the maintenance cost deviates from the optimal solution by only approximately 5%.Furthermore,phase transition behavior is observed,which provides considerable guidance for the development of domain-specific LLMs for the maintenance domain. 展开更多
关键词 Large language model Maintenance decision-making Wind farms Fine-tuning
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ZhongdaChat-ED:a medical large language model for personalized erectile dysfunction health consultation and professional clinical decision-making using retrieval-augmented generation 认领 引用
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作者 Yi Xia Yong-Kun Zhu +6 位作者 Chun-Hui Liu Xiang Hong Rui-Xin Zhang Nie-Ke Zhang Ming Chen Guang-Yuan Zhang Chao Sun 《Asian Journal of Andrology》 SCIE CAS CSCD 2026年第1期71-79,共9页
Artificial intelligence(AI)-driven large language models(LLMs)hold potential for medical applications but face challenges,such as inaccurate or outdated training data.In this study,ZhongdaChat-ED,a personalized medica... Artificial intelligence(AI)-driven large language models(LLMs)hold potential for medical applications but face challenges,such as inaccurate or outdated training data.In this study,ZhongdaChat-ED,a personalized medical LLM integrating retrieval-augmented generation(RAG)technology,was developed to enhance erectile dysfunction(ED)counseling and clinical decision-making.The model was built using the open-source Deepseek-r1:32b framework,augmented with two specialized databases:a patient health consultation database and a clinical decision support database updated with real-time medical advancements.Two versions of ZhongdaChat-ED were developed:a Consumer Version for patient-facing health consultations and a Professional Version for clinician support.Performance was evaluated against four commonly used LLMs(ChatGPT4,Copilot,Claude,and Gemini)through simulated clinical consultations and case analyses.Three urologists and three patients assessed responses across various dimensions,including accuracy,human caring,ease of understanding,clinical significance,and informational frontier.The Consumer Version outperformed commonly used LLMs in accuracy(4.77/5),human caring(4.86/5),and ease of understanding(4.88/5)with all P85.2%case score rate)and informational frontier scores(4.52/5)than those of other models(P<0.001).ZhongdaChat-ED effectively addresses limitations of conventional LLMs by leveraging RAG to integrate real-time,domain-specific data.ZhongdaChat-ED shows promise in enhancing patient health consultation and clinician decision-making for ED,underscoring the value of tailored AI systems in bridging gaps between generalized AI and specialized medical needs.Future work should expand multimodal capabilities and cross-disciplinary integration to broaden clinical utility. 展开更多
关键词 artificial intelligence clinical decision-making erectile dysfunction large language models medical consultation ZhongdaChat-ED
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Development of a large language model-driven intelligent agent for predicting relative permeability in oil and gas reservoirs 认领 引用
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作者 Jiulong Wang Yuanchun Zhou +2 位作者 Xuhui Zhang Junming Lao Hongqing Song 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2026年第3期91-98,共8页
To address the challenges and difficulties in predicting the relative permeability of reservoirs using traditional physics-driven and data-driven approaches,this paper proposes a collaborative analysis intelligent age... To address the challenges and difficulties in predicting the relative permeability of reservoirs using traditional physics-driven and data-driven approaches,this paper proposes a collaborative analysis intelligent agent for the relative permeability of oil and gas reservoirs based on a large model.By constructing a multiagent collaborative workflow,integrated collaboration of data,models,and analysis results is achieved.The intelligent agent automatically completes data preprocessing,feature extraction,parameter calibration,small model calling,and output and evaluation of prediction results based on preset task dependencies.At the same time,by introducing deep learning-based embedding of physical information,the analysis efficiency and accuracy are significantly improved.The results show that compared with traditional physical analysis methods,this method improves the accuracy of reservoir relative permeability prediction by 10%,has a computational efficiency 10 times higher than traditional deep learning algorithms,and a computational speed 100–1,000 times higher than conventional physical models.This study further enhances the efficiency and intelligence of physical property analysis of oil and gas reservoirs,providing a new research direction for the intelligent development of oil and gas digitization. 展开更多
关键词 Relative permeability Large language model Intelligent agent
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A Metaheuristic Football Optimization Algorithm Integrated with Large Language Models for Automated Seismic Time-Series Modeling 认领 引用
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作者 Amal H.Alharbi Marwa M.Eid +2 位作者 Nima Khodadadi Ebrahim A.Mattar Sayed Elkenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期947-987,共41页
Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Alt... Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Although recent studies have improved neural architectures and optimization techniques,preprocessing is often treated as a fixed or manually designed stage,with limited integration into model optimization.To address this,this paper proposes an integrated,data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction,specifically forecasting earthquake magnitude from seismic catalog time-series data,with experiments conducted on Canadian seismic records.The method uses a Large Language Model to guide data preparation and feature engineering,rather than fully automate them,and applies deep learning-based forecasting with the N-HITS architecture,optimized via metaheuristic-assisted feature selection and hyperparameter tuning.The Football Optimization Algorithm(FbOA),employed as a metaheuristic optimization strategy in this study,is evaluated and compared with several well-known optimizers under identical conditions.The results show significant performance gains,with FbOA achieving superior accuracy,robustness,and convergence compared to baseline and competing methods.Notably,error metrics are reduced(MSE 3.10×10-7,RMSE 5.57×103),with high performance indicators(r=0.982,R2=0.979,NSE=0.981,WI=0.985).These results highlight the value of integrating guided preprocessing with optimization and demonstrate a scalable framework for high-precision time-series prediction in geophysical and related domains. 展开更多
关键词 Seismic time-series forecasting large language models metaheuristic algorithms football optimization algorithm earthquake modeling
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Multitype Game Optimisation:A Two-Stage Fine-Tuning Framework for Multi-Game Optimisation With Large Language Models 认领 引用
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作者 Xiali Li Jingshi Gu +3 位作者 Feifan He Yang Xiao Yuanli Jia Ping Lan 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期739-753,共15页
Large language models(LLMs)have made remarkable advances in natural language processing,demonstrating great potential in modelling structured sequences.However,adapting these capabilities to machine gaming tasks such ... Large language models(LLMs)have made remarkable advances in natural language processing,demonstrating great potential in modelling structured sequences.However,adapting these capabilities to machine gaming tasks such as Go remains challenging due to limitations in strategy generalisation and optimisation efficiency.This paper presents multitype game optimisation(MyGO),a two-stage fine-tuning framework tailored for two-player perfect information board games,exploring the applicability of LLMs to nonlinguistic decision-making domains.In the supervised fine-tuning stage,we propose a unified structural encoding method,action semantic unit(ASU),which efficiently converts heterogeneous game records into discrete token sequences compatible with LLMs.In the reinforcement learning stage,we design TA-PPO(token-level adaptive proximal policy optimisation),an enhanced PPO-based algorithm to address the issue of sparse feedback commonly encountered in game reinforcement learning.Experimental results demonstrate that the fine-tuned models achieve superior or comparable performance to traditional game-playing algorithms in terms of strategy quality,rule generalisation and inference efficiency.This work provides a scalable paradigm for fine-tuning LLMs in complex decision-making tasks and lays a foundation for future research in game AI and generalisable strategy optimisation. 展开更多
关键词 computer game fine‐tuning large language models reinforcement learning reward function
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Towards Generalisable and Explainable Traffic Signal Control via Deep Reinforcement Learning and Large Language Models 认领 引用
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作者 Hao Huang Wenjie He +6 位作者 Qilie Liu Qian Liu Chao Huang Anwar PPAbdul Majeed Xiangguang Dai Gang Fang Xiaohua Xu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期483-497,共15页
As a government-regulated public service,traffic signal control(TSC)requires reliable and transparent decision-making.However,existing deep reinforcement learning(DRL)methods,despite improvements in control accuracy,s... As a government-regulated public service,traffic signal control(TSC)requires reliable and transparent decision-making.However,existing deep reinforcement learning(DRL)methods,despite improvements in control accuracy,still lack explainability and generalisation,severely limiting their applicability in real-world environments.To address the challenges above,this paper proposes GenEx-TSC,a generalisable and explainable TSC method that integrates deep reinforcement learning with large language models(LLMs).First,starting from vehicle-level states,we train a DRL agent incorporating intersection physical heterogeneity and neighbourhood information,which lays the evaluation foundation for constructing a high-quality LLM dataset.Subsequently,the LLM agent is optimised through a two-stage training mechanism.In the distillation stage,a lightweight LLM agent is trained using the reasoning trajectories of a larger-scale LLM agent,inheriting its semantic understanding and decision-generation capabilities and in the alignment stage,the DRL evaluation network is employed to calibrate the outputs of the distilled LLM agent,ensuring that the generated cycle-level signal timing strategies are both efficient and interpretable.We synthesise 10 intersection networks with different physical attributes in SUMO and set traffic flows of varying scales.Experimental results across diverse traffic environments demonstrate that the proposed GenEx-TSC exhibits clear advantages over traditional methods,mainstream DRL methods and LLM baselines in terms of control accuracy,generalisation and explainability. 展开更多
关键词 deep reinforcement learning explainable intelligence large language model traffic signal control
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Large Language Model-Enabled Constitutive Modeling for Rate-Dependent Plasticity and Automatic UMAT Subroutine Generation 认领 引用
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作者 Yuchuan Gu Lusheng Wang +3 位作者 Jun Ding Yanhong Peng Changgeng Li Shaojie Gu 《Computers, Materials & Continua》 SCIE EI 2026年第5期315-329,共15页
In materials science and engineering design,high-fidelity and high-efficiency numerical simulation has become a driving force for innovation and practical implementation.To address longstanding bottlenecks in the deve... In materials science and engineering design,high-fidelity and high-efficiency numerical simulation has become a driving force for innovation and practical implementation.To address longstanding bottlenecks in the development of conventional material constitutive models—such as lengthy modeling cycles and difficulties in numerical implementation—this study proposes an intelligent modeling and code generation approach powered by large languagemodels.A structured knowledge base integrating constitutive theory,numerical algorithms,and UMAT(User Material)interface specifications is constructed,and a retrieval-augmented generation strategy is employed to establish an end-to-end workflow spanning experimental data parsing,constitutive model formulation,and automatic UMAT subroutine generation.Experimental results show that the method achieves high accuracy for both a classical Johnson–Cookmodel and a physics-informed neural network(PINN)model,with key parameter identification errors below 5%.Moreover,the automatically generated UMAT subroutines yield finite element simulation results in Abaqus that are highly consistent with theoretical predictions(coefficient of determination R2>0.98)while maintaining good numerical stability.This framework is currently focused on the automatic construction of rate-dependent elastoplastic material models,and its core method also provides a clear path for extending to other constitutive categories such as hyperelasticity and viscoelasticity.This work provides an effective technical route for the rapid development and reliable numerical implementation of material constitutive models,significantly advancing the intelligence level of computational mechanics research and improving engineering application efficiency. 展开更多
关键词 Large language model constitutive model UMAT subroutine
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Large language models for battery prognostics 认领 引用
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作者 Jingyuan Zhao Yan Wang +2 位作者 Misheng Cai Lili Xie Hewu Wang 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第4期159-177,I0006,共19页
Predicting battery health with accuracy and interpretability has become a grand challenge at the intersection of electrochemistry,artificial intelligence,and sustainable energy.Conventional data-driven and physics-bas... Predicting battery health with accuracy and interpretability has become a grand challenge at the intersection of electrochemistry,artificial intelligence,and sustainable energy.Conventional data-driven and physics-based methods remain constrained by nonlinear,coupled,and heterogeneous battery dynamics that limit generalization across chemistries,duty cycles,and environments.Recent breakthroughs in large language models(LLMs)and foundation-model artificial intelligence introduce a paradigm shift—enabling machines to learn from multimodal signals,encode physical laws,and reason adaptively across scales.This review unifies these advances into ten foundational methodologies that delineate the emerging landscape of intelligent battery prognostics:transfer learning,knowledge augmentation,physics-informed and explainable intelligence,ensemble fusion,causal reasoning,continual adaptation,multi-agent coordination,digital-twin coupling,and the pursuit of artificial general intelligence.Together,these dimensions redefine batteries from passive electrochemical devices into cognitive energy systems—self-optimizing,trustworthy,and responsive to uncertainty.Framed within the broader evolution toward Industry 5.0,we chart a roadmap for autonomous battery management that fuses physics,data,and reasoning,establishing artificial intelligence as a scientific and technological cornerstone for the next generation of resilient,adaptive,and sustainable electrification. 展开更多
关键词 Battery management system Artificial intelligence Deep learning Large language models Multi-agent Artificial general intelligence Industry 5.0
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CellReasoner:a reasoning-enhanced large language model for cell type annotation 认领 引用
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作者 Guangshuo Cao Yi Shen +4 位作者 Lingyu Zhao Jianghong Wu Haoyu Chao Ming Chen Dijun Chen 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2026年第4期762-765,共4页
Cell type annotation is a fundamental step in single-cell data analysis,and it also represents a reasoning process that integrates diverse sources of evidence,including gene expression profiles,canonical marker genes,... Cell type annotation is a fundamental step in single-cell data analysis,and it also represents a reasoning process that integrates diverse sources of evidence,including gene expression profiles,canonical marker genes,and reference datasets,to accurately infer cellular identities.Similar to stepwise inference in artificial intelligence,this process relies on combining prior knowledge with context-specific features to achieve confident classification.Recent advances in large language models have shown that sufficiently scaled models can perform sophisticated reasoning across mathematical,logical,and programming tasks(Azerbayev et al.,2023;Jaech et al.,2024;Guo et al.,2025;Ye et al.,2025).This progress highlights the potential for leveraging LLM-based reasoning paradigms to enhance complex biological inference tasks such as automated cell type annotation. 展开更多
关键词 combining prior knowledge gene expression profilescanonical cell type annotation artificial intelligencethis reasoning large language models stepwise inference reasoning process
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Bilingual evaluation of large language models for patient education in refractive surgery 认领 引用
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作者 Tsung-Hsien Tsai Chin-Ling Tsai +2 位作者 Jui-Hung Hsu Ching-Hsi Hsiao Hung-Chi Chen 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第6期1019-1027,共9页
AIM:To evaluate the ability of six advanced large language models(LLMs)—in providing accurate,comprehensive,and readable patient education on corneal refractive surgeries[laser in-situ keratomileusis(LASIK),keratoref... AIM:To evaluate the ability of six advanced large language models(LLMs)—in providing accurate,comprehensive,and readable patient education on corneal refractive surgeries[laser in-situ keratomileusis(LASIK),keratorefractive lenticule extraction(KLEx),and photorefractive keratectomy(PRK)]in both English and Chinese.METHODS:This is a cross-sectional,comparative study.Twenty-six questions,compiled from authoritative ophthalmologic sources and covering four domains(procedure basics and eligibility;safety,risks and longterm stability;recovery and postoperative experience;and practical concerns),were administered in both English and Chinese via fresh chat sessions with each LLM,respectively.Five performance metrics were evaluated:accuracy,comprehensiveness,word count,readability,and reproducibility,using appropriate statistical tests.RESULTS:OpenAI o1 and DeepSeek-R1 consistently achieved the highest accuracy and most comprehensive responses,significantly outperforming ChatGPT-4o,Gemini Advanced,Claude Sonnet,and Tongyi Qwen(Friedman P<0.001).Although overall accuracy and comprehensiveness were similar across languages,Chinese responses were significantly longer.Readability varied among the models,with Claude Sonnet generally producing the most readable English texts.Reproducibility analysis revealed moderate consistency,reflecting inherent variability in outputs to identical prompts.CONCLUSION:Reasoning-augmented LLMs,particularly OpenAI o1 and DeepSeek-R1,demonstrate superior performance in delivering bilingual patient education for corneal refractive surgery,with high accuracy and comprehensiveness.However,variations in response length,readability,and reproducibility indicate that further refinement is necessary before these tools can be reliably integrated into clinical practice. 展开更多
关键词 large language models refractive surgery patient education
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CMM-EmbedCluster:a clustering framework for Chinese materia medica based on large language model and Chinese materia medica property theory 认领 引用
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作者 Jiayi He Jiadong Xie +1 位作者 Kongfa Hu Haiyan Li 《Digital Chinese Medicine》 CAS CSCD 2026年第2期278-289,共12页
Objective This study proposes a clustering framework for Chinese materia medica(CMM)based on a large language model(LLM),aiming to explore potential compatibility patterns among CMMs from the semantic perspective of C... Objective This study proposes a clustering framework for Chinese materia medica(CMM)based on a large language model(LLM),aiming to explore potential compatibility patterns among CMMs from the semantic perspective of CMM property theory.Methods First,a CMM property knowledge base was constructed based on Chinese Materia Medica,including 567 commonly used CMMs characterized by four properties,five flavors,and meridian tropism.Then,49 CMMs derived from 10 prescriptions for Zangdu(脏毒,pathogenic toxins)recorded in Waike Zhengzong(《外科正宗》,Orthodox Manual of External Medicine)and Yangke Xinde Ji(《疡科心得集》,Collected Insights on Ulcer Medicine)were selected as the experimental dataset.Five semantic representation methods—One-Hot,Word2Vec,Bidirectional Encoder Representations from Transformers(BERT),Beijing Academy of Artificial Intelligence General Embedding(BGE),and Qwen—were applied to encode CMM property information into vector representations.Subsequently,t-distributed Stochastic Neighbor Embedding(t-SNE)was used for nonlinear dimensionality reduction on high-dimensional semantic vectors,followed by k-means clustering(k=7).Clustering performance was evaluated using the Silhouette Score(SS),Davies-Bouldin Index(DBI),and Calinski-Harabasz Index(CHI).Results The Qwen-based clustering method,CMM-EmbedCluster,achieved the highest SS(0.6074)and CHI(158.0572),as well as the lowest DBI(0.4995),indicating improved cluster separation and compactness compared with other methods.Visualization of CMM clustering results showed that the clusters were well separated in the low-dimensional space,with strong inter-cluster discrimination and high intra-cluster functional consistency.Further interpretability analysis of CMM clustering results revealed stable structural differences among clusters in terms of four properties,five flavors,and meridian tropism,forming functional partitions consistent with CMM property theory.Conclusion CMM-EmbedCluster utilizes an LLM to achieve semantic-level representation and clustering of CMMs within the framework of CMM property theory,providing support for exploring potential compatibility patterns among CMMs from the perspective of CMM property semantics. 展开更多
关键词 Large language model Chinese materia medica property theory Chinese materia medica clustering Semantic representation k-Means clustering CMM-EmbedCluster
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From Lexicons to Large Language Models:A Comprehensive Survey of Sentiment Analysis Methods,Benchmarks,and Emerging Frontiers 认领 引用
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作者 Shuvodeep De Agnivo Gosai +4 位作者 Karun Thankachan Ramadan A.Zein Eldin Abdulaziz T.Almaktoom Mustafa Bayram Ali Wagdy Mohamed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期119-167,共49页
Sentiment analysis(SA)has evolved from a niche text-classification task into a central problem in natural language processing,spanning multiple domains,modalities,and languages.This survey provides a comprehensive rev... Sentiment analysis(SA)has evolved from a niche text-classification task into a central problem in natural language processing,spanning multiple domains,modalities,and languages.This survey provides a comprehensive review of sentiment analysis methods from their origins in lexicon-based approaches through classical machine learning,deep learning architectures,pre-trained transformers,and the current era of large language models(LLMs).We formalize the SA problem across multiple granularity levels(document,sentence,and aspect)and present a taxonomy that encompasses classification,regression,aspect-based sentiment analysis(ABSA),emotion detection,and stance detection tasks across diverse domains including movie reviews,product reviews,healthcare,finance,and social media.We review benchmark datasets spanning text-only corpora(IMDb,SST,SemEval series),multimodal benchmarks(CMU-MOSI,CMU-MOSEI,MELD),and domain-specific evaluation suites such as SentiEval.The methodological evolution is traced from VADER and SentiWordNet,through SVM and Naïve Bayes classifiers,CNN and LSTM architectures,BERT and its variants,to modern LLMs including GPT-4,Llama 3,and ModernBERT,with technical details of key architectures and their mathematical formulations.We provide dedicated analyses of chain-ofthought reasoning for implicit sentiment,multimodal fusion strategies,cross-lingual transfer methods,sarcasm and irony detection,explainability through SHAP and LIME,and the emerging challenge of AI-generated fake reviews.A comparative analysis across paradigms reveals that while LLMs achieve strong zero-shot performance,fine-tuned smaller models remain competitive on standard benchmarks,a finding with significant implications for deployment efficiency.We identify persistent open challenges including domain drift,cultural bias,and the model variability problem,and outline future research directions encompassing reasoning-augmented SA,agentic workflows,federated learning,and real-time edge deployment.With coverage of over 130 references spanning two decades of research and 29 new references from 2024 and 2025,this survey provides a unified roadmap for both newcomers and researchers at the frontier of sentiment analysis. 展开更多
关键词 Sentiment analysis opinion mining large language models transformers BERT aspect-based sentiment analysis multimodal sentiment analysis cross-lingual NLP explainable AI chain-of-thought reasoning sarcasm detection benchmark datasets
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A Grounded Multi-Agent Multimodal Large Language Model Framework for Interpretable Risk Assessment in Driving Scenes 认领 引用
17
作者 Chien-Hao Tseng Min-Yu Chen +2 位作者 Meng-Wei Lin Jyh-Horng Wu Chung-I Huang 《Computers, Materials & Continua》 SCIE EI 2026年第9期1137-1163,共27页
Context-aware driving assistance must do more than detect objects:it has to identify the cues that materially affect risk,separate observable evidence from inference,and produce recommendations that humans can audit.T... Context-aware driving assistance must do more than detect objects:it has to identify the cues that materially affect risk,separate observable evidence from inference,and produce recommendations that humans can audit.This paper presents a grounded multi-agent multimodal large language model(MLLM)framework for interpretable risk assessment in driving scenes.The framework decomposes reasoning into four stages—context relevance evaluation,visual interpretation,factual verification with anomaly extraction,and risk assessment with action recommendation—so that the final advisory is generated only from a verified intermediate representation rather than directly from a free-form scene description.We evaluate the framework on a manually labeled benchmark derived from BDD100K covering traffic-sign interpretation,traffic-density assessment,and pedestrian–vehicle interaction risk.The benchmark contains 600 frames with three-rater annotation and majority-vote labels(Fleiss’κ=0.79 on risk levels);we explicitly discuss the implications of this scale for generalization and complement it with a multi-backbone stress test.Across five independent runs,the proposed framework improves risk accuracy from 74.3±0.9%to 84.8±0.6%and macro-F1 from 72.8±1.1%to 83.1±0.7%over a single-agent MLLM baseline.The hallucination rate—defined as the fraction of outputs containing at least one entity,attribute,or relation that has no visual support in the source frame—drops from 18.7%to 8.9%,and the actionability score—a five-point human rating averaged over usefulness,specificity,and visual consistency—rises from 3.62 to 4.28.McNemar tests confirm that the gain in risk accuracy is statistically significant(p<0.001).The framework is intended as a semantic decision-support layer for explainable advanced driver-assistance systems and human-centered autonomous-driving interfaces. 展开更多
关键词 Autonomous driving context-aware risk assessment multimodal large language model multi-agent system interpretable reasoning driving-scene understanding
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A Review of Applications and Challenges of Large Language Models for Foundry Intelligence in the Casting Industry 认领 引用
18
作者 Yutong Guo Jianying Yang Chao Yang 《Computers, Materials & Continua》 SCIE EI 2026年第7期207-233,共27页
Large language models(LLMs)and related foundation-model workflows are emerging as promising tools for advancing foundry intelligence across the casting value chain.This review examines their applications in material d... Large language models(LLMs)and related foundation-model workflows are emerging as promising tools for advancing foundry intelligence across the casting value chain.This review examines their applications in material design and property prediction,process parameter optimization and intelligent control,and defect detection and quality tracing in casting environments.The surveyed studies indicate that LLM-enabled systems can help integrate unstructured technical knowledge with multimodal industrial data.This integration supports composition design,simulation-assisted process optimization,diagnostic reasoning,and knowledge-grounded decision support.However,current evidence shows that the transition from pilot demonstrations to robust industrial deployment remains constrained by several practical barriers,including heterogeneous data integration,insufficient traceability across process stages,reliability under physical and safety constraints,and the latency and resource limitations of shop-floor environments.We further highlight key research directions for real-world foundry applications,including multimodal cognitive systems,lightweight domain-adapted models,trustworthy retrieval-augmented and physics-aware reasoning,and human-in-the-loop validation frameworks.Overall,the review suggests that the future of foundry intelligence will depend not only on model capability,but also on data governance,deployable system design,and reliable integration with metallurgical knowledge and industrial workflows. 展开更多
关键词 Large scale language models foundry industry material design process optimization defect detection multimodal data fusion
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