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A Quantization Approach for the Reduced Size of Large language Models

A Quantization Approach for the Reduced Size of Large Langua...

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international conference on knowledge and Smart Technology (KST)

作者： Ravi Kishore Kodali Yatendra Prasad Upreti Lakshmi Boppana Department of Electronics and Communication Engineering National Institute of Technology Warangal WARANGAL India

The use of large-language models is widespread in a range of applications, including natural language processing and multimodal tasks. However, these models are computationally intensive. This work presents a novel approach that shows the ability to reduce the size of publicly available LLMs, including Llama-2-7B, GPT-J, and LLaMA. This work uses a parameter-efficient fine-tuning (PEFT) library. The experiment reveals that the quantized version of LLM had a considerable reduction in memory size and significantly improved the model's operational efficiency. This quantization process has the potential to bridge the gap between sophisticated language models and practical deployment scenarios, providing opportunities for the use of large languaae models in resource-constrained applications.

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Leveraging NLP approaches to define and implement text relevance hierarchy framework for business news classification 27

Leveraging NLP approaches to define and implement text relev...

引用

27th international conference on knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Arslan, Muhammad Cruz, Christophe 9 Avenue Alain Savary Dijon21000 France

Managing large volumes of business news data is important for companies. However, identifying relevant data can be challenging due to the dynamic nature of business environments and varying market needs. The article acknowledges this challenge and suggests that the lack of a standardized definition for relevant data makes it even more difficult for companies to prioritize and manage their data effectively, which can lead to missed opportunities. To address this challenge, the article proposes a text relevance hierarchy framework consisting of five levels that assess the relevance of business news texts to a company's specific operations and interests. The framework uses criteria such as specific topics, organizations, people, locations, and financial figures involved in the article to evaluate the importance of texts. natural language processing (NLP) approaches such as entity recognition, topic modeling, and similarity analysis can be leveraged to implement the text relevancy hierarchy. By using this framework, companies can prioritize and manage their business news information efficiently, focus on the most important and relevant texts, and identify new areas of interest based on changing market needs. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： business news classification business topics contextual closeness entity recognition information prioritization natural language processing (NLP) similarity analysis taxonomy text relevancy hierarchy

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Temporal relation identification in functional requirements 27

Temporal relation identification in functional requirements

引用

27th international conference on knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Onishi, Maiko Ogata, Shinpei Okano, Kozo Bekki, Daisuke Ochanomizu University Otsuka Bunkyo-ku Tokyo Japan Shinshu University Wakasato Nagano Japan

In this study, we propose a method for applying a temporal relation identification model to functional requirements. We discuss the limited availability of data in the requirements engineering domain compared to other fields when used for supervised learning, and therefore employ a corpus from the news domain for training. The experimental results demonstrate that the types of temporal relations present in functional requirements are limited, indicating that focusing on learning with a narrowed set of labels is effective. Additionally, We incorporate Dependency Path (DP) into the temporal relation identification model and report, through comparative experiments, that leveraging DP is effective, but minor modifications to DP do not lead to significant improvements in accuracy. By demonstrating specific application methods of temporal relation identification in requirements engineering, we anticipate contributing to the analysis of functional requirements in software development. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： natural language processing Requirements Specification Software engineering Temporal Relation Identification

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Detecting Health Misinformation on Social Networking Sites Using Large language Models and Deep Learning-based natural language processing

Detecting Health Misinformation on Social Networking Sites U...

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Foundation and Large language Models (FLLM), international conference on

作者： Amenah Misfer Alshahrani Hafiz Farooq Ahmad Jamil Hussain Department of Computer Science College of Computer Sciences and Information Technology King Faisal University Al-Ahsa Saudi Arabia Department of Computer Science Sejong University Seoul Korea

ISBN: (数字)9798350354799

ISBN: (纸本)9798350354805

Health misinformation on social networking sites (SNS) is a critical issue, particularly during health crises like the COVID-19 pandemic. The spread of inaccurate health information can lead to severe outcomes, including reduced vaccine uptake and decreased trust in healthcare. This paper introduces a system using large language models (LLMs) and deep learning-based natural language processing (NLP) techniques to detect and mitigate health misinformation on SNS. The model incorporates transformer-based architectures, zero-shot (ZS) and few-shot (FS) learning, and prompt engineering to classify health-related content as true or false. A key feature is the integration of a knowledge graph that enhances verification capabilities. Applied to both English and Arabic datasets, the system’s performance is evaluated using accuracy, F1-score, BLEU, and METEOR metrics, demonstrating its multilingual effectiveness in real-time misinformation detection.

关键词： Measurement Adaptation models Social networking (online) Large language models knowledge graphs natural language processing Real-time systems Multilingual Meteors Fake news

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Caskow: Context-Aware Stance Detection Using External knowledge-Augmented LLM

Caskow: Context-Aware Stance Detection Using External Knowle...

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international conference on Web Research (ICWR)

作者： Maryam Shafiei Hossein Rahmani Amirhossein Derakhshan Milad Allahgholi School of Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Stance detection in texts is an important task in natural language processing with diverse applications in political, economic and marketing. The research conducted in this area is categorized into traditional methods, deep learning methods and language model-based methods. However, to the best of our knowledge, none of these methods have effectively addressed context-aware stance detection. In this paper, we propose a method called CASKOW, which first collects context for a given text from Wikipedia as an external resource using information retrieval techniques and then employs the context alongside a large language model (LLM) to detect the stance. Experimental results show that CASKOW achieves a 7% improvement in F1-measure when the label “none” is included and a 10 % improvement when the label “none” is excluded, compared to models that do not utilize contextual information. Additionally, non-numerical interpretation of results reveals key insights, allowing for the identification of errors and challenges in stance detection datasets.

关键词： Economics Deep learning Large language models Biological system modeling Encyclopedias Context awareness natural language processing Internet Online services Context modeling

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Multiple knowledge Graphs Semantic Interaction based on Graph Embedding and Matching processing

Multiple Knowledge Graphs Semantic Interaction based on Grap...

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IEEE international conference on Data Science in Cyberspace (DSC)

作者： Ling Wang Haoyu Hao Xue Yan Xiwei Wang Tie Hua Zhou Keun Ho Ryu School of Computer Science Northeast Electric Power University Jilin China Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Vietnam College of Electrical and Computer Engineering Chungbuk National University Cheongju Republic of Korea

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the rapid development of natural language processing technology, various fields are constructing domain knowledge graphs and trying to integrate multi-domain knowledge graphs to better grasp the information of each domain. Aiming at this trend, we proposed the GE-CSM model, which is a novel model that combines graph embedding and context semantic matching. By combining entity-relationship graph embedding and context semantic matching, the model is able to understand the relationships between entities more accurately and provide more semantic information based on the context. Finally, through experimental validation, the effectiveness of the model in performing multiple knowledge graph integration is demonstrated, and the response time and semantic richness of the downstream Q&A task are improved, proving its potential in enhancing semantic interaction performance.

关键词： Accuracy Semantics Cyberspace knowledge graphs Data science Market research natural language processing Time factors Context modeling

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Implicit Offensive Speech Detection Based on Multi-feature Fusion 1

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16th international conference on knowledge Science, engineering and Management (KSEM)

作者： Guo, Tengda Lin, Lianxin Liu, Hang Zheng, Chengping Tu, Zhijian Wang, Haizhou Sichuan Univ Sch Cyber Sci & Engn Chengdu 610207 Peoples R China

ISBN: (数字)9783031402869

ISBN: (纸本)9783031402852;9783031402869

As social media platforms become increasingly strict in censorship of user posts, aggressive and insulting language has gradually shifted to implicit expressions using homophones, metaphors, and other forms of camouflage. This not only intensifies the satirical attacks of negative posts but also leads to a significant decrease in the effectiveness of models designed to detect offensive speech. This paper aims to achieve high accurate detection of implicit offensive speech. We conducted targeted investigations on the speech characteristics on Weibo, which is one of the largest social media platform in China. Based on the identified features of implicit offensive speech, including semantic, emotional, metaphorical, and fallacy characteristics, this paper constructs a BERT-based Multi-Task learning model named BMA (BERT-Mate-Ambiguity) to accurately detect the implicit offensive speech in the real world. Additionally, this paper establishes a dataset based on posts of Weibo that contain implicit offensive speech and conducts various comparative, robustness, and ablation experiments. The effectiveness of the model is demonstrated by comparing it to existing models that perform well in this field. Finally, this paper discusses some of its limitations and proposes future research work.

关键词： Implicit Offensive Speech Classification Deep Learning natural language processing Weibo

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Research and Application of Multimodal knowledge Graph in Construction Risk Management of Transmission and Transformation engineering

Research and Application of Multimodal Knowledge Graph in Co...

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Signal processing, Communications and engineering Management conference (ISPCEM), international

作者： Zhang You Qian Yi Zhang Yiquan Na Yu Zhao Jundan Yu Ting Hu Yue Lv Peng Engineering Construction Consulting Branch of State Grid Shanghai Electric Power Company Shanghai China Shanghai Power Transmission and Transformation Engineering Co. Ltd. Shanghai China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

This paper proposes a construction risk management method based on multimodal knowledge graph, aiming to improve the risk identification and prediction capabilities in the construction process. The system combines advanced natural language processing (NLP) algorithms, which can automatically extract and integrate construction-related information from multi-source heterogeneous data, and construct a comprehensive knowledge graph covering multimodal data such as text, images, and videos. In terms of algorithm design, an improved NLP model is used to conduct in-depth semantic analysis and association mining of text data, while integrating image recognition and video analysis technologies to achieve all-round data integration and analysis of complex construction scenes. In the system simulation stage, this paper conducts comprehensive verification through a large amount of actual construction data, focusing on evaluating the risk identification and prediction performance of the system in different scenarios. The simulation results show that the accuracy of this method in identifying construction risks has reached more than 95%, and the error rate of predicting construction risks is controlled within 5%, which significantly improves the accuracy and efficiency of construction risk management, fully verifies the practicality and reliability of the system, and provides strong technical support for construction management.

关键词： Accuracy Power transmission Signal processing algorithms knowledge graphs Prediction algorithms natural language processing Systems simulation Risk management Data mining Videos

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The Application of LLMs in the Analysis and Modeling of Software Requirements

The Application of LLMs in the Analysis and Modeling of Soft...

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IEEE international conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Zhipeng Wang Changxi Feng Longfei Liu Guotao Jiao Peng Ye School of Computer Science and Artificial Intelligence WuGuotao Jiaohan Textile University China Engineering Research Center of Hubei Province for Clothing Information Wuhan Textile University China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

In modern software engineering, requirements analysis and modeling are key steps in requirements engineering, influencing the subsequent system design and implementation. Traditional requirements analysis and modeling methods usually involve multiple stakeholders such as requirements providers and analysts working together collaboratively and iteratively, requiring a significant amount of manpower. It is of great significance to reduce the burden on requirements providers and analysts and improve the efficiency of analysis and modeling. In existing work, some merely utilize knowledge repositories to provide more knowledge in support of analysis or modeling, while others employ natural language processing (NLP) techniques to automate the analysis or modeling process. However, neither has relieved the burden on requirements providers and analysts. The emergence of large language models(LLMs) has brought new possibilities to requirements analysis and modeling. LLMs can combine knowledge base processing and understanding of large amounts of natural language data, providing a new automated requirement analysis method. This paper proposes an automated requirements analysis and modeling method for Support Vector Machine(SVM) classifier and LLMs - SVM-LLMs. The method is deployed based on the intelligent requirement service platform - “Wisdom Requirement Communication”, which combines SVM and LLMs to effectively improve the accuracy of requirements analysis and significantly reduce the time in the software development process.

关键词： Support vector machines Analytical models Software quality natural language processing Vectors Software reliability Stakeholders System analysis and design Software engineering Software development management

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Construction and Application of NLP-based knowledge Graph in CNC Equipment Fault Field 13

Construction and Application of NLP-based Knowledge Graph in...

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13th international conference on Mechanical and Aerospace engineering, ICMAE 2022

作者： Zhao, Qian Wang, Rui Xu, Peng Yang, Wei Avic Manufacturing Technology Institute Information Center Beijing China Beihang University School of Computer Science and Engineering Beijing China

ISBN: (数字)9781665472357

ISBN: (纸本)9781665472357

Computer Numerical Control (CNC) equipment is a technology-intensive mechatronics complex system cov-ering multidisciplinary knowledge. How to effectively convert historical fault data into useful fault knowledge base is an urgent problem to be solved. A construction technology of CNC equipment fault knowledge graph based on natural language processing (NLP) is proposed. BERT deep learning classification technology is used to build the sample classification model, the named entity recognition model is trained based on the Bi-LSTM technology, as well as the CNC equipment fault corpus sample data is trained, recognized, and modeled. Finally, Neo4j is used to form a knowledge graph model. It has been verified that the recognition rate of the model basically meets the requirements, and on this basis, a recommended solution for repairing equipment faults is proposed, which realizes the effective use of fault knowledge. © 2022 IEEE.

关键词： knowledge graph

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