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Multiple knowledge Graphs Semantic Interaction based on Graph Embedding and Matching processing 9

Multiple Knowledge Graphs Semantic Interaction based on Grap...

引用

9th ieee international conference on Data Science in Cyberspace, DSC 2024

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

ISBN: (纸本)9798350391367

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. © 2024 ieee.

关键词： knowledge graph

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A Quantization Approach for the Reduced Size of Large language Models 16

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

引用

16th international conference on knowledge and Smart Technology, KST 2024

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

ISBN: (纸本)9798350370737

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. © 2024 ieee.

关键词： Computational linguistics

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A Novel AI-based chatbot Application for Personalized Medical Diagnosis and review using Large language Models

A Novel AI-based chatbot Application for Personalized Medica...

引用

2023 ieee international conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Akilesh, S. Sheik Abdullah, A. Abinaya, R. Dhanushkodi, S. Sekar, Rajeev School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350305708

This research showcases a groundbreaking application in the medical industry by leveraging the cutting-edge OpenAI service's GPT-3.5 model, powered by large language models (LLMs). Through the implementation of a Java-based Android app, it introduces a medical chatbot that redefines the way individuals interact with healthcare systems. By capitalizing on the advanced capabilities of LLMs, the app enables users to effortlessly input their symptoms and receive accurate insights, personalized diagnoses, and actionable medical advice. The utilization of natural language processing and AI algorithms ensures a seamless and intuitive user experience. The significance of this innovation lies in the revolutionary impact of LLMs on the entire medical industry. The integration of LLMs empowers the app to comprehend complex medical queries, interpret contextual information, and deliver reliable and up-to-date medical knowledge to users. This transformative technology facilitates improved access to healthcare information, enhances decision-making processes, and ultimately contributes to the optimization of healthcare services. © 2023 ieee.

关键词： GPT 3.5 Model Large language Models Medical Chatbot natural language processing OpenAI Service

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GAEL: A Global-Aware Entity Linking Model Based on Retriever-Reader Paradigm 7

GAEL: A Global-Aware Entity Linking Model Based on Retriever...

引用

7th international conference on Machine Learning and natural language processing, MLNLP 2024

作者： Wang, Linjie Hao, Shiyi Lu, Yuxing Wang, Chen Liu, Yunfei Li, Shengyang Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China Key Laboratory of Space Utilization Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Tencent AI Lab Shenzhen China

ISBN: (纸本)9798350354973

The field of space science and applications, rich in domain knowledge, has witnessed research on domain knowledge extraction and the preliminary construction of domain knowledge graphs. Entity linking serves as a fundamental component for intelligent applications of knowledge graphs. Recently, the retriever-reader paradigm, inspired by question-answering tasks, has advanced the development of entity linking. However, the existing studies overlook the relationships between entities, resulting in the lack of global consistency in the predictions. Furthermore, they struggle to deal with redundant entities effectively. We propose a Global-Aware Entity Linking Model (GAEL) to address these issues. GAEL employs incremental training during model training, integrating linking results in successive rounds. By incorporating the entity information predicted in previous rounds into new training inputs, GAEL continuously optimizes semantic information and strengthens interactions between candidate entities, which helps to identify potential entities corresponding to mentions. Besides, GAEL utilizes a novel entity screening mechanism that encodes candidate entities and input text separately and ranks them based on semantic similarity, effectively resolving the issue of multiple entities corresponding to the same mention. Experimental results demonstrate that GAEL significantly improves entity linking performance on datasets in the field of space science and applications, as well as on multiple public datasets. © 2024 ieee.

关键词： knowledge graph

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On Large Visual language Models for Medical Imaging Analysis: An Empirical Study

On Large Visual Language Models for Medical Imaging Analysis...

引用

9th ieee/ACM international conference on Connected Health - Applications, Systems and engineering Technologies (CHASE)

作者： Minh-Hao Van Verma, Prateek Wu, Xintao Univ Arkansas Fayetteville AR 72701 USA

ISBN: (纸本)9798350345025;9798350345018

Recently, large language models (LLMs) have taken the spotlight in natural language processing. Further, integrating LLMs with vision enables the users to explore emergent abilities with multimodal data. Visual language models (VLMs), such as LLaVA, Flamingo, or CLIP, have demonstrated impressive performance on various visio-linguistic tasks. Consequently, there are enormous applications of large models that could be potentially used in the biomedical imaging field. Along that direction, there is a lack of related work to show the ability of large models to diagnose the diseases. In this work, we study the zeroshot and few-shot robustness of VLMs on the medical imaging analysis tasks. Our comprehensive experiments demonstrate the effectiveness of VLMs in analyzing biomedical images such as brain MRIs, microscopic images of blood cells, and chest X-rays. While VLMs can not outperform classic vision models like CNN or ResNet, it is worth noting that VLMs can serve as chat assistants to provide pre-diagnosis before making decisions without the need for retraining or finetuning stages.

关键词： visual language model zero-shot learning medical imaging analysis

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Fine Tuning Idefic 9b with LORA for Multimodal Medical VQA

Fine Tuning Idefic 9b with LORA for Multimodal Medical VQA

引用

2024 international conference on knowledge engineering and Communication Systems, ICKECS 2024

作者： Punneshetty, Shashwath Ashok, Shashank Niranjanamurthy, M. Murthy, S.V.N. Bms Institute of Technology and Management Dept. of Ai & Ml Bangalore India S Jc Institute of Technology Department. of Cse Karnataka Chikkaballapur India

ISBN: (纸本)9798350359688

We introduce an innovative approach to Medical Visual Question Answering (VQA) by fine-tuning the Idefic 9b model on a combined dataset from SLAKE and VQARAD. Our methodology focuses on loading the model using a 4bit quantization method, striking a balance between computational efficiency and model performance. To enhance the model's ability to adapt to specific tasks, we employ Low Rank Adaptation (LORA) to fine-tune the model's attention layers. This technique contributes to reducing the model's trainable parameters. For evaluation purposes, we employ the BLEU (Bilingual Evaluation Understudy) scores, a widely accepted metric in natural language processing, to evaluate the generated responses quality. The results of our approach in the field of Medical VQA are promising, paving the way for the development of more advanced and efficient models in the future. This paper aims to offer insights into the effective application of fine-tuning techniques, showcasing their impact on enhancing the performance of VQA models in the medical domain. © 2024 ieee.

关键词： natural language processing systems

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Descriptive Answer Evaluation System using natural language processing 1

Descriptive Answer Evaluation System using Natural Language ...

引用

1st international conference on Cognitive Computing and engineering Education, ICCCEE 2023

作者： Nalavade, J.E. Kondiba Dabade, Tejashree MITADT School of Computing Department of Computer Science and Engineering Pune India Tata Consultancy Services Ltd India

ISBN: (纸本)9798350332803

In the education system, written exams play a vital role to test knowledge gained by students. But the total process from evaluating answers, grading, verification, and publishing results is time-consuming. A lot of human efforts are required for evaluating descriptive answers. This evaluation also depends on various factors like knowledge of the evaluator, application-level understanding of the evaluator. When any human being performs evaluation it varies along with the emotions of that person. So the goal of this paper is to develop the descriptive answer evaluation system which ensures uniformity in marking, saves a lot of time, and trouble of checking bundles of papers. In this evaluation system, first pre-processing techniques are applied to student's answers. Then answers are matched against manually entered model answer using different techniques like keyword matching, Bert and finally score is calculated. © 2023 ieee.

关键词： natural language processing systems

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Efficient Detection of Toxic Prompts in Large language Models 24

Efficient Detection of Toxic Prompts in Large Language Model...

引用

39th ACM/ieee international conference on Automated Software engineering (ASE)

作者： Liu, Yi Yu, Junzhe Sun, Huijia Shi, Ling Deng, Gelei Chen, Yuqi Liu, Yang Nanyang Technol Univ Singapore Singapore ShanghaiTech Univ Shanghai Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Large language models (LLMs) like ChatGPT and Gemini have significantly advanced natural language processing, enabling various applications such as chatbots and automated content generation. However, these models can be exploited by malicious individuals who craft toxic prompts to elicit harmful or unethical responses. These individuals often employ jailbreaking techniques to bypass safety mechanisms, highlighting the need for robust toxic prompt detection methods. Existing detection techniques, both blackbox and whitebox, face challenges related to the diversity of toxic prompts, scalability, and computational efficiency. In response, we propose TOXICDETECTOR, a lightweight greybox method designed to efficiently detect toxic prompts in LLMs. TOXICDETECTOR leverages LLMs to create toxic concept prompts, uses embedding vectors to form feature vectors, and employs a Multi-Layer Perceptron (MLP) classifier for prompt classification. Our evaluation on various versions of the LLama models, Gemma-2, and multiple datasets demonstrates that TOXICDETECTOR achieves a high accuracy of 96.39% and a low false positive rate of 2.00%, outperforming state-of-the-art methods. Additionally, ToxicDetector's processing time of 0.0780 seconds per prompt makes it highly suitable for real-time applications. TOXICDETECTOR achieves high accuracy, efficiency, and scalability, making it a practical method for toxic prompt detection in LLMs.

关键词： Accuracy Scalability Large language models Chatbots Feature extraction Vectors Real-time systems Safety Software engineering Glass box

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Large language Models for Indian Legal Text Summarisation 10

Large Language Models for Indian Legal Text Summarisation

引用

10th ieee international conference on Electronics, Computing and Communication Technologies (ieee CONECCT)

作者： Hemanth, Kumar M. Jayanth, P. Anand, Kumar M. Natl Inst Technol Karnataka Dept Informat Technol Artificial Intelligence Surathkal India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939;9798350385922

Summarizing legal case judgments is a complex task in Legal natural language processing (NLP), with a gap in understanding how various summarization models, including extractive and abstractive approaches and analysing the perform within the domain of legal documents. Since there are around 4 crore pending cases in the Indian court system, this study addresses the challenge of laborious task of manually summarizing legal documents. It introduces both supervised and unsupervised models for both extractive and abstractive summarization, showcasing their effective performance through evaluations using ROUGE metrics and BERT score. BART, T5, PEGASUS, ROBERTA, Legal-PEGASUS, Legal-BERT models are used for abstractive summarisation. TextRank, LexRank, LSA, Summarizer BERT, KL-Summ are used in case of extractive summarisation. Longformer, Bert - Legal Pegasus are also considered for the task of Summarisation. In the domain of legal document summarization, we used GPT-4 and LLAMA-2, employing prompt engineering with both Zero-shot and One-shot prompts to extract summaries. As far of our knowledge, this is the first paper that used Large language Models like GPT-4 and LLama-2 for the task of Legal Text summarisation. Along with that a user-friendly chatbot has been developed utilizing the Llama model and specifically designed to respond for queries related to legal texts. Additionally, a web application has been created, allowing users to upload legal documents for summarization. An option is given to users to select from various languages including Telugu, Tamil, Kannada, Malayalam, and Hindi. As a result the summarised text is converted into respective language.

关键词： Legal,Summarisation NLP Large language models GPT-4 LLAMA-2

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Fusion of Stance Labels and Event knowledge for Effective Stance Detection 6

Fusion of Stance Labels and Event Knowledge for Effective St...

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6th international conference on natural language processing (ICNLP)

作者： Yin, Zhaoning Zhang, Yuanshuo Li, Aohua Chen, Bo Zhao, Xiaobing Minzu Univ China Sch Informat Engn Natl Language Resource Monitoring & Res Ctr Minor Beijing Peoples R China

ISBN: (纸本)9798350349122;9798350349115

Stance detection on social media has attracted significant attention recently. However, for a hot event, it is not easy to determine the stance of a comment on a given target, because there is complex comment-target-event-stance relationship behind it. To alleviate this problem, a new fusion method is proposed, which integrates explicit stance label and brief event background into BERT-based stance classifier. By fine-tuning the BERT model, it learns to reason about accurate stances based on the fused information. Experiments on a Weibo stance detection dataset demonstrate the effectiveness of the method.

关键词： Stance detection stance label event BERT

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