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Linguistic Reflection on the Online Catchword of Appreciation —From the Perspective of Usage-based Language Model and Complex Adaptive System Theory
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作者 HE Xiang 《北京第二外国语学院学报》 2016年第5期139-139,共1页
Language is a special social phenomenon and is always on the changing process with the development of society. During the evolving process of language, new language varieties will continuously emerge due to the change... Language is a special social phenomenon and is always on the changing process with the development of society. During the evolving process of language, new language varieties will continuously emerge due to the changes of some social and cultural factors. Cyber language is universally accepted as one type of the social language varieties. Basically, cyber language can be treated as a complex adaptive system which is influenced by the interaction between users’ cognition, social culture and the surrounding environments. Thus it is safe to say that cyber language is always undergoing a dynamic evolving process. With the usage-based language model as the theoretical foundation, this paper proposes a Complex Adaptive System (CAS) approach to analyze the expression of Appreciation to explore the complex, dynamic and nonlinear development of cyber language from the angle of meaning construction, grammaticalization and functional adaption respectively. It is found that the expression of Appreciation is experiencing adaptively a semantic connotations development and a process of grammatical functions expansion as well. This paper suggests that the emergence and development of cyber language is a novel and trendy social language phenomenon. Network language can achieve its process and evolution under the huge impact of social changes and social promotions. When faced with the changing surroundings, cyber language itself enjoys a timely adaption and responsive development to keep up with the new environments, which reflects the basic principle of language development, namely, language changes with the development of society. 展开更多
关键词 usage-based language model Complex Adaptive System Theory the expression of APPRECIATION cyber language language var
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Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models 被引量:1
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作者 Zheyi Chen Liuchang Xu +5 位作者 Hongting Zheng Luyao Chen Amr Tolba Liang Zhao Keping Yu Hailin Feng 《Computers, Materials & Continua》 SCIE EI 2024年第8期1753-1808,共56页
Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the ... Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field. 展开更多
关键词 Artificial intelligence large language models large multimodal models foundation models
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DeBERTa-GRU: Sentiment Analysis for Large Language Model
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作者 Adel Assiri Abdu Gumaei +2 位作者 Faisal Mehmood Touqeer Abbas Sami Ullah 《Computers, Materials & Continua》 SCIE EI 2024年第6期4219-4236,共18页
Modern technological advancements have made social media an essential component of daily life.Social media allow individuals to share thoughts,emotions,and ideas.Sentiment analysis plays the function of evaluating whe... Modern technological advancements have made social media an essential component of daily life.Social media allow individuals to share thoughts,emotions,and ideas.Sentiment analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the text.Sentiment analysis is essential in business and society because it impacts strategic decision-making.Sentiment analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance correlations.The execution time increases due to the sequential processing of the sequence models.However,the calculation times for the Transformer models are reduced because of the parallel processing.This study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their limitations.In particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment analysis.Using the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics correctly.The proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques. 展开更多
关键词 DeBERTa GRU Naive Bayes LSTM sentiment analysis large language model
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Enhancing Orthopedic Knowledge Assessments:The Performance of Specialized Generative Language Model Optimization
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作者 Hong ZHOU Hong-lin WANG +11 位作者 Yu-yu DUAN Zi-neng YAN Rui LUO Xiang-xin LV Yi XIE Jia-yao ZHANG Jia-ming YANG Ming-di XUE Ying FANG Lin LU Peng-ran LIU Zhe-wei YE 《Current Medical Science》 SCIE CAS 2024年第5期1001-1005,共5页
Objective This study aimed to evaluate and compare the effectiveness of knowledge base-optimized and unoptimized large language models(LLMs)in the field of orthopedics to explore optimization strategies for the applic... Objective This study aimed to evaluate and compare the effectiveness of knowledge base-optimized and unoptimized large language models(LLMs)in the field of orthopedics to explore optimization strategies for the application of LLMs in specific fields.Methods This research constructed a specialized knowledge base using clinical guidelines from the American Academy of Orthopaedic Surgeons(AAOS)and authoritative orthopedic publications.A total of 30 orthopedic-related questions covering aspects such as anatomical knowledge,disease diagnosis,fracture classification,treatment options,and surgical techniques were input into both the knowledge base-optimized and unoptimized versions of the GPT-4,ChatGLM,and Spark LLM,with their generated responses recorded.The overall quality,accuracy,and comprehensiveness of these responses were evaluated by 3 experienced orthopedic surgeons.Results Compared with their unoptimized LLMs,the optimized version of GPT-4 showed improvements of 15.3%in overall quality,12.5%in accuracy,and 12.8%in comprehensiveness;ChatGLM showed improvements of 24.8%,16.1%,and 19.6%,respectively;and Spark LLM showed improvements of 6.5%,14.5%,and 24.7%,respectively.Conclusion The optimization of knowledge bases significantly enhances the quality,accuracy,and comprehensiveness of the responses provided by the 3 models in the orthopedic field.Therefore,knowledge base optimization is an effective method for improving the performance of LLMs in specific fields. 展开更多
关键词 artificial intelligence large language models generative articial intelligence ORTHOPEDICS
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A large language model-powered literature review for high-angle annular dark field imaging
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作者 Wenhao Yuan Cheng Peng Qian He 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第9期76-81,共6页
High-angle annular dark field(HAADF)imaging in scanning transmission electron microscopy(STEM)has become an indispensable tool in materials science due to its ability to offer sub-°A resolution and provide chemic... High-angle annular dark field(HAADF)imaging in scanning transmission electron microscopy(STEM)has become an indispensable tool in materials science due to its ability to offer sub-°A resolution and provide chemical information through Z-contrast.This study leverages large language models(LLMs)to conduct a comprehensive bibliometric analysis of a large amount of HAADF-related literature(more than 41000 papers).By using LLMs,specifically ChatGPT,we were able to extract detailed information on applications,sample preparation methods,instruments used,and study conclusions.The findings highlight the capability of LLMs to provide a new perspective into HAADF imaging,underscoring its increasingly important role in materials science.Moreover,the rich information extracted from these publications can be harnessed to develop AI models that enhance the automation and intelligence of electron microscopes. 展开更多
关键词 large language models high-angle annular dark field imaging deep learning
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Large language models in laparoscopic surgery: A transformative opportunity
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作者 Partha Pratim Ray 《Laparoscopic, Endoscopic and Robotic Surgery》 2024年第4期174-180,共7页
This opinion paper explores the transformative potential of large language models(LLMs)in laparoscopic surgery and argues for their integration to enhance surgical education,decision support,reporting,and patient care... This opinion paper explores the transformative potential of large language models(LLMs)in laparoscopic surgery and argues for their integration to enhance surgical education,decision support,reporting,and patient care.LLMs can revolutionize surgical education by providing personalized learning experiences and accelerating skill acquisition.Intelligent decision support systems powered by LLMs can assist surgeons in making complex decisions,optimizing surgical workflows,and improving patient outcomes.Moreover,LLMs can automate surgical reporting and generate personalized patient education materials,streamlining documentation and improving patient engagement.However,challenges such as data scarcity,surgical semantic capture,real-time inference,and integration with existing systems need to be addressed for successful LLM integration.The future of laparoscopic surgery lies in the seamless integration of LLMs,enabling autonomous robotic surgery,predictive surgical planning,intraoperative decision support,virtual surgical assistants,and continuous learning.By harnessing the power of LLMs,laparoscopic surgery can be transformed,empowering surgeons and ultimately benefiting patients. 展开更多
关键词 Large language model Artificial intelligence Generative artificial intelligence LAPAROSCOPY SURGERY
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Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter
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作者 R.Sujatha K.Nimala 《Computers, Materials & Continua》 SCIE EI 2024年第2期1669-1686,共18页
Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requir... Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88. 展开更多
关键词 Bidirectional encoder for representation of transformer conversation ensemble model fine-tuning generalized autoregressive pretraining for language understanding generative pre-trained transformer hyperparameter tuning natural language processing robustly optimized BERT pretraining approach sentence classification transformer models
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Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models
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作者 Jiakai Li Jianpeng Hu Geng Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第5期2481-2503,共23页
In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple e... In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach. 展开更多
关键词 Relational triple extraction semantic interaction large language models data augmentation specific domains
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LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework
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作者 Hao Chen Runfeng Xie +4 位作者 Xiangyang Cui Zhou Yan Xin Wang Zhanwei Xuan Kai Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第6期4283-4296,共14页
Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news text... Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR. 展开更多
关键词 Large language models news recommendation knowledge graphs(KG)
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Evaluating the role of large language models in inflammatory bowel disease patient information
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作者 Eun Jeong Gong Chang Seok Bang 《World Journal of Gastroenterology》 SCIE CAS 2024年第29期3538-3540,共3页
This letter evaluates the article by Gravina et al on ChatGPT’s potential in providing medical information for inflammatory bowel disease patients.While promising,it highlights the need for advanced techniques like r... This letter evaluates the article by Gravina et al on ChatGPT’s potential in providing medical information for inflammatory bowel disease patients.While promising,it highlights the need for advanced techniques like reasoning+action and retrieval-augmented generation to improve accuracy and reliability.Emphasizing that simple question and answer testing is insufficient,it calls for more nuanced evaluation methods to truly gauge large language models’capabilities in clinical applications. 展开更多
关键词 Crohn’s disease Ulcerative colitis Inflammatory bowel disease Chat generative pre-trained transformer Large language model Artificial intelligence
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Potential use of large language models for mitigating students’problematic social media use:ChatGPT as an example
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作者 Xin-Qiao Liu Zi-Ru Zhang 《World Journal of Psychiatry》 SCIE 2024年第3期334-341,共8页
The problematic use of social media has numerous negative impacts on individuals'daily lives,interpersonal relationships,physical and mental health,and more.Currently,there are few methods and tools to alleviate p... The problematic use of social media has numerous negative impacts on individuals'daily lives,interpersonal relationships,physical and mental health,and more.Currently,there are few methods and tools to alleviate problematic social media,and their potential is yet to be fully realized.Emerging large language models(LLMs)are becoming increasingly popular for providing information and assistance to people and are being applied in many aspects of life.In mitigating problematic social media use,LLMs such as ChatGPT can play a positive role by serving as conversational partners and outlets for users,providing personalized information and resources,monitoring and intervening in problematic social media use,and more.In this process,we should recognize both the enormous potential and endless possibilities of LLMs such as ChatGPT,leveraging their advantages to better address problematic social media use,while also acknowledging the limitations and potential pitfalls of ChatGPT technology,such as errors,limitations in issue resolution,privacy and security concerns,and potential overreliance.When we leverage the advantages of LLMs to address issues in social media usage,we must adopt a cautious and ethical approach,being vigilant of the potential adverse effects that LLMs may have in addressing problematic social media use to better harness technology to serve individuals and society. 展开更多
关键词 Problematic use of social media Social media Large language models ChatGPT Chatbots
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Evaluating Privacy Leakage and Memorization Attacks on Large Language Models (LLMs) in Generative AI Applications
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作者 Harshvardhan Aditya Siddansh Chawla +6 位作者 Gunika Dhingra Parijat Rai Saumil Sood Tanmay Singh Zeba Mohsin Wase Arshdeep Bahga Vijay K. Madisetti 《Journal of Software Engineering and Applications》 2024年第5期421-447,共27页
The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Infor... The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. We describe different black-box attacks from potential adversaries and study their impact on the amount and type of information that may be recovered from commonly used and deployed LLMs. Our research investigates the relationship between PII leakage, memorization, and factors such as model size, architecture, and the nature of attacks employed. The study utilizes two broad categories of attacks: PII leakage-focused attacks (auto-completion and extraction attacks) and memorization-focused attacks (various membership inference attacks). The findings from these investigations are quantified using an array of evaluative metrics, providing a detailed understanding of LLM vulnerabilities and the effectiveness of different attacks. 展开更多
关键词 Large language models PII Leakage Privacy Memorization OVERFITTING Membership Inference Attack (MIA)
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Security Vulnerability Analyses of Large Language Models (LLMs) through Extension of the Common Vulnerability Scoring System (CVSS) Framework
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作者 Alicia Biju Vishnupriya Ramesh Vijay K. Madisetti 《Journal of Software Engineering and Applications》 2024年第5期340-358,共19页
Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, a... Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, and more. However, their widespread usage emphasizes the critical need to enhance their security posture to ensure the integrity and reliability of their outputs and minimize harmful effects. Prompt injections and training data poisoning attacks are two of the most prominent vulnerabilities in LLMs, which could potentially lead to unpredictable and undesirable behaviors, such as biased outputs, misinformation propagation, and even malicious content generation. The Common Vulnerability Scoring System (CVSS) framework provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. By extending the current CVSS framework, we generate scores for these vulnerabilities such that organizations can prioritize mitigation efforts, allocate resources effectively, and implement targeted security measures to defend against potential risks. 展开更多
关键词 Common Vulnerability Scoring System (CVSS) Large language models (LLMs) DALL-E Prompt Injections Training Data Poisoning CVSS Metrics
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Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models
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作者 Gunika Dhingra Siddansh Chawla +1 位作者 Vijay K. Madisetti Arshdeep Bahga 《Journal of Software Engineering and Applications》 2024年第1期23-42,共20页
With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily meas... With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study. 展开更多
关键词 Large language models (LLMs) Smaller language models (SLMs) FINANCE NETWORKING Supervisor model Scoring Function
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Large Language Model Based Semantic Parsing for Intelligent Database Query Engine
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作者 Zhizhong Wu 《Journal of Computer and Communications》 2024年第10期1-13,共13页
With the rapid development of artificial intelligence, large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. These models have great potential to enha... With the rapid development of artificial intelligence, large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. These models have great potential to enhance database query systems, enabling more intuitive and semantic query mechanisms. Our model leverages LLM’s deep learning architecture to interpret and process natural language queries and translate them into accurate database queries. The system integrates an LLM-powered semantic parser that translates user input into structured queries that can be understood by the database management system. First, the user query is pre-processed, the text is normalized, and the ambiguity is removed. This is followed by semantic parsing, where the LLM interprets the pre-processed text and identifies key entities and relationships. This is followed by query generation, which converts the parsed information into a structured query format and tailors it to the target database schema. Finally, there is query execution and feedback, where the resulting query is executed on the database and the results are returned to the user. The system also provides feedback mechanisms to improve and optimize future query interpretations. By using advanced LLMs for model implementation and fine-tuning on diverse datasets, the experimental results show that the proposed method significantly improves the accuracy and usability of database queries, making data retrieval easy for users without specialized knowledge. 展开更多
关键词 Semantic Query Large language models Intelligent Database Natural language Processing
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Adapter Based on Pre-Trained Language Models for Classification of Medical Text
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作者 Quan Li 《Journal of Electronic Research and Application》 2024年第3期129-134,共6页
We present an approach to classify medical text at a sentence level automatically.Given the inherent complexity of medical text classification,we employ adapters based on pre-trained language models to extract informa... We present an approach to classify medical text at a sentence level automatically.Given the inherent complexity of medical text classification,we employ adapters based on pre-trained language models to extract information from medical text,facilitating more accurate classification while minimizing the number of trainable parameters.Extensive experiments conducted on various datasets demonstrate the effectiveness of our approach. 展开更多
关键词 Classification of medical text ADAPTER Pre-trained language model
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A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence
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作者 Xianwei Jiang Yanqiong Zhang +1 位作者 Juan Lei Yudong Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期1-40,共40页
Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign La... Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 years.Hidden Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional identificationmethods.Benefiting from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung up.Deep Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence recognitionmethods.In addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition photography.Meanwhile,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition research.Additionally,suitable datasets and evaluation criteria are also worth pursuing. 展开更多
关键词 Chinese Sign language Recognition deep neural networks artificial intelligence transfer learning hybrid network models
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Multi-scale context-aware network for continuous sign language recognition
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作者 Senhua XUE Liqing GAO +1 位作者 Liang WAN Wei FENG 《虚拟现实与智能硬件(中英文)》 EI 2024年第4期323-337,共15页
The hands and face are the most important parts for expressing sign language morphemes in sign language videos.However,we find that existing Continuous Sign Language Recognition(CSLR)methods lack the mining of hand an... The hands and face are the most important parts for expressing sign language morphemes in sign language videos.However,we find that existing Continuous Sign Language Recognition(CSLR)methods lack the mining of hand and face information in visual backbones or use expensive and time-consuming external extractors to explore this information.In addition,the signs have different lengths,whereas previous CSLR methods typically use a fixed-length window to segment the video to capture sequential features and then perform global temporal modeling,which disturbs the perception of complete signs.In this study,we propose a Multi-Scale Context-Aware network(MSCA-Net)to solve the aforementioned problems.Our MSCA-Net contains two main modules:(1)Multi-Scale Motion Attention(MSMA),which uses the differences among frames to perceive information of the hands and face in multiple spatial scales,replacing the heavy feature extractors;and(2)Multi-Scale Temporal Modeling(MSTM),which explores crucial temporal information in the sign language video from different temporal scales.We conduct extensive experiments using three widely used sign language datasets,i.e.,RWTH-PHOENIX-Weather-2014,RWTH-PHOENIX-Weather-2014T,and CSL-Daily.The proposed MSCA-Net achieve state-of-the-art performance,demonstrating the effectiveness of our approach. 展开更多
关键词 Continuous sign language recognition Multi-scale motion attention Multi-scale temporal modeling
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On the Teaching Design of Graduates’EAP Course:Enhancing Language Proficiency and Critical Thinking Skills
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作者 LIU Yuan 《Sino-US English Teaching》 2024年第5期238-241,共4页
This paper explores the integration of the bridge-in,objectives,pre-assessment,participatory activities,post-assessment and summary(BOPPPS)teaching model within the context of the post-graduates Academic English cours... This paper explores the integration of the bridge-in,objectives,pre-assessment,participatory activities,post-assessment and summary(BOPPPS)teaching model within the context of the post-graduates Academic English course.It discusses how this structured approach can effectively enhance students’language proficiency,foster critical thinking skills,and align with the multifaceted objectives of advanced English language education.The study provides a detailed examination of each BOPPPS component as applied to the post-graduates Academic English curriculum,supported by theoretical underpinnings and practical implications. 展开更多
关键词 Academic English course BOPPPS teaching model language proficiency critical thinking active learning
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Analysis of an event study using the Fama–French five‑factor model:teaching approaches including spreadsheets and the R programming language
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作者 Monica Martinez‑Blasco Vanessa Serrano +1 位作者 Francesc Prior Jordi Cuadros 《Financial Innovation》 2023年第1期2042-2075,共34页
The current financial education framework has an increasing need to introduce tools that facilitate the application of theoretical models to real-world data and contexts.However,only a limited number of free tools are... The current financial education framework has an increasing need to introduce tools that facilitate the application of theoretical models to real-world data and contexts.However,only a limited number of free tools are available for this purpose.Given this lack of tools,the present study provides two approaches to facilitate the implementa-tion of an event study.The first approach consists of a set of MS Excel files based on the Fama–French five-factor model,which allows the application of the event study methodology in a semi-automatic manner.The second approach is an open-source R-programmed tool through which results can be obtained in the context of an event study without the need for programming knowledge.This tool widens the calculus possibilities provided by the first approach and offers the option to apply not only the Fama–French five-factor model but also other models that are common in the finan-cial literature.It is a user-friendly tool that enables reproducibility of the analysis and ensures that the calculations are free of manipulation errors.Both approaches are freely available and ready-to-use. 展开更多
关键词 Event study Fama–French five-factor model Financial education Teaching innovation SPREADSHEET R programming language
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