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Detecting and Mitigating Bias in LLMs through knowledge Graph-Augmented Training

Detecting and Mitigating Bias in LLMs through Knowledge Grap...

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Artificial Intelligence and Data engineering (AIDE), international conference on

作者： Rajeev Kumar Harishankar Kumar Kumari Shalini Gen AI Research Intute AI San Francisco USA Gen AI Research Intute AI Gurgaon India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Large language models have revolutionized natural language processing with their surprising capability to understand and generate human-like text. However, many of these models inherit and further amplify the biases present in their training data, raising ethical and fairness concerns. The detection and mitigation of such biases are vital to ensuring that LLMs act responsibly and equitably across diverse domains. This work investigates knowledge Graph-Augmented Training (KGAT) as a novel method to mitigate bias in LLM. Using structured domain-specific knowledge from real-world knowledge graphs, we improve the understanding of the model and reduce biased output. Public datasets for bias assessment include Gender Shades, Bias in Bios, and FairFace, while metrics such as demographic parity and equal opportunity facilitate rigorous detection. We also performed targeted mitigation strategies to correct biased associations, leading to a significant drop in biased output and improved bias metrics. Equipped with real-world datasets and knowledge graphs, our framework is both scalable and effective, paving the way toward responsible deployment in sensitive and high-stakes applications.

关键词： Training Measurement Ethics Large language models Prevention and mitigation Biological system modeling Training data knowledge graphs Equal opportunities Data models

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Comprehensive Survey of Event Extraction Methods in natural language processing

Comprehensive Survey of Event Extraction Methods in Natural ...

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Sustainable Communication Networks and Application (ICSCNA), international conference on

作者： Amit Kulkarni Varun Dogra School of Computer Applications Lovely Professional University Phagwara Punjab School of Computer Science and Engineering Lovely Professional University Phagwara Punjab

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Event Extraction (Event Extraction) plays an essential role in NLP (natural language processing) by automating the identification and extraction of events from unstructured textual data. This paper provides a comprehensive overview of event extraction, highlighting its significance in various domains such as security, law, and financial analysis. The paper categorizes event extraction into closed-domain and open-domain approaches. It also talks about various techniques like argument identification, event trigger identification, as well as event type classification. It explores methodologies including machine learning, pattern matching, & deep learning, emphasizing their application in different domains. Evaluation metrics to evaluate the effectiveness of EE systems are explored, including precision, recall, as well as the F1 score. Through a systematic review of the literature, the paper underscores the advancements, challenges, and future directions in EE, positioning it as a critical component in extracting actionable knowledge from textualsources.

关键词： Measurement Surveys Deep learning Green communications Information retrieval natural language processing Data mining Security Pattern matching Systematic literature review

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Hate Sentiment Recognition System for Persian language 12

Hate Sentiment Recognition System for Persian Language

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12th international conference on Computer and knowledge engineering, ICCKE 2022

作者： Jey, Pegah Shams Hemmati, Arash Toosi, Ramin Akhaee, Mohammad Ali K. N. Toosi University of Technology School of Electrical and Computer Engineering Tehran Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran Adak Vira Iranian Rahjoo Company Tehran Iran

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

People's lives in societies are tied to social networks and these networks face problems such as the existence of hateful speech. Most social networks try to identify and prevent the spread of this phenomenon by using natural language processing (NLP) methods. On the internet, hate speech causes arguments between different groups in society. Given that anyone is able to put any content on social media in the form of a short text, this leads to the uncontrollable spread of hatred on social networks and can cause harm to individuals and various groups in society. This is necessary to have control over users' content on social media. In this study, a method for identifying hateful content in short texts is proposed. First, cosine similarities of word-based and character-based n-grams (features) and sentences (samples) are calculated. Then, employing calibrated support vector machine, the probability of each feature related to hatred is calculated. Finally, another SVM is applied for the final classification. The proposed method is compared to the state-of-The-Art methods such as pars-bert and a multi-view SVM approach using Instagram comments in terms of various performance metrics. Results show that the proposed method outperforms the previous studies. © 2022 IEEE.

关键词： Support vector machines

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knowledge-Guided Prompt Learning for Deepfake Facial Image Detection

Knowledge-Guided Prompt Learning for Deepfake Facial Image D...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hao Wang Cheng Deng Zhidong Zhao School of Cyberspace Security Hangzhou Dianzi University Hangzhou China School of Electronic Engineering Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent generative models demonstrate impressive performance on synthesizing photographic images, which makes humans hardly to distinguish them from pristine ones, especially on realistic-looking synthetic facial images. Previous works mostly focus on mining discriminative artifacts from vast amount of visual data. However, they usually lack the exploration of prior knowledge and rarely pay attention to the domain shift between training categories (e.g., natural and indoor objects) and testing ones (e.g., fine-grained human facial images), resulting in unsatisfactory detection performance. To address these issues, we propose a novel knowledge-guided prompt learning method for deepfake facial image detection. Specifically, we retrieve forgery-related prompts from large language models as expert knowledge to guide the optimization of learnable prompts. Besides, we elaborate test-time prompt tuning to alleviate the domain shift, achieving significant performance improvement and boosting the application in real-world scenarios. Extensive experiments on DeepFakeFaceForensics dataset show that our proposed approach notably outperforms state-of-the-art methods.

关键词： Training Representation learning Deepfakes Visualization Large language models Signal processing Speech processing Tuning Optimization Testing

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Construction of Modern Chinese Standard for Computer language Disfluency Detection 7

Construction of Modern Chinese Standard for Computer Languag...

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7th international Symposium on Advances in Electrical, Electronics, and Computer engineering

作者： Li, Jiantao Zhang, Yunqiu Zhou, Jianshe Liu, Jie College of Arts Capital Normal University 105 West Third Ring Road North Haidian District Beijing100048 China Research Center for Language Intelligence of China 105 West Third Ring Road North Haidian District Beijing100048 China School of Information Science North China University of Technology Beijing China ChinaLanguage Intelligence Research Center Capital Normal University 105 West Third Ring Road North Haidian District Beijing100048 China

ISBN: (纸本)9781510656437

Spoken text disfluency detection is an important component of speech computer recognition systems, and its goal is to effectively identify and remove spoken phenomena such as repetition, pauses, corrections and redundancy contained in AI speech recognition text data, thereby making spoken text data more concise and increasing the readability of its text data, and the technology helps to improve the correctness of computer language information processing tasks. However, in computer science or in linguistics, research on computer language disfluency detection techniques for modern Chinese word classes is almost in an academic gap. Based on a qualitative and quantitative analysis of 50, 000 items of Chinese conference corpus, this study defines what is non-sentence elements in modern Chinesen and using the spoken language corpus, established a reference model for computer processing of disfluency detection components. © 2022 SPIE. All rights reserved.

关键词： Speech recognition

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14h international Symposium on Business Modeling and Software Design, BMSD 2024

14h International Symposium on Business Modeling and Softwar...

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14h international Symposium on Business Modeling and Software Design, BMSD 2024

ISBN: (纸本)9783031640728

The proceedings contain 20 papers. The special focus in this conference is on Business Modeling and Software Design. The topics include: From Value Streams and Capability Maps to Protocol Models and Back;a Method to Align Business Capability Maps and Enterprise Data Models;Assigning Declarative Semantics to Some UML Activity Diagrams and BPMN Diagrams;A Systematic Approach to Derive Conceptual Models from BPMN Models;an Analysis of Kinds of Skills and knowledge in Information System Development;VR-SDLC: A Context-Enhanced Life Cycle Visualization of Software-or-Systems Development in Virtual Reality;smart Contracts as Data Quality Consensus Enforcers in Data Markets;from Mutualism to Amensalism: A Case Study of Blockchain and Digital Identity Wallets;A Review on Privacy and Monetization Aspects Within BCI and XR-BCI Ecosystems;researching Multi-Site Artificial Neural Networks’ Activation Rates and Activation Cycles;reproducibility of Firmware Analysis: An Empirical Study;roles of natural language Generation for Requirements engineering;developing Functional Specifications of an Information System a Personal Overview;context Awareness and External Factors;managing Multi-site Artificial Neural Networks’ Activation Rates and Activation Cycles: Experiments on Cross-Enterprise, Multi-site Deep Learning Systems;software Architecture for Object Detection in Images Based on Color Features with Integrated Artificial Intelligence;Challenges in Geolocation for Logistics Delivery: A Case Study on the Accuracy in Bulgaria of Google Maps Directions API;Understanding SBOMs in Real-World Systems – A Practical DevOps/SecOps Perspective.

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Internal-External Information Enhanced Causal Reasoning

Internal-External Information Enhanced Causal Reasoning

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international Joint conference on Neural Networks (IJCNN)

作者： Yucheng Yao Fan Yang Kaiyue Wang Xiabing Zhou School of Computer Science and Technology Soochow University Suzhou China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Causal reasoning is vitally important for various natural language processing, which needs text semantic understanding and rich knowledge information reserve. Causal question-answering (CQA), one of the causal reasoning tasks, aims to choose either the cause or effect of a given story sentence. It requires both background causal knowledge and the ability to infer cause-effect relations. However, existing studies ignore the logical and commonsense relationship between the contexts, which limits the model capability. In this paper, we propose a novel model of Semantic Internal-External Enhancement (SIEE) by enhancing both the internal and external knowledge. The model employs Abstract Meaning Representation (AMR) to capture the core semantic information and explicit structures. In addition, we explore the commonsense knowledge behind the key information in the context to provide more clues for reasoning. Finally, we combine the above internal and external information by using a semantic aggregator to aggregate the semantic information of neighbors on the keyword nodes. Experimental studies show the competitive performance of our proposed model over the state-of-the-art published results on three CQA benchmarks, e-CARE, COPA and BCOPA.

关键词： knowledge engineering Aggregates Semantics Neural networks Linguistics Benchmark testing natural language processing

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AI-Driven Bilingual Talkbot for Academic Counselling 3

AI-Driven Bilingual Talkbot for Academic Counselling

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Krishnam, Nagendra Prasad Bora, Ashim Swathi, R.S.V. Rama Gehlot, Anita Chandraprakash, V. Raghu, T. Telangana Hyderabad India Kampur College Assam Kampur India Koneru Lakshmaiah Education Foundation KL Business School Andhra Pradesh Guntur India Uttaranchal University Uttaranchal Institute of Technology India St. Martin's Engineering College Dept. of IT Telangana Secunderabad India SR University School of Sciences Telangana Warangal India

ISBN: (纸本)9798350399264

Traditional technologies are transforming how businesses interact with their customers, creating demands for prompt responses & continuous accessibility. Students frequently have questions about college & university policies & procedures, academic process, extracurricular, as well as other aspects of educational career. Actually, there is a lack of service satisfaction because the educational advisers as well as the student affairs staff are overloaded with inquiries & unable to respond to them right away. In our work, we develop a multilingual talkbot that converses among learners in both English & Arabic using Artificial Intelligence (AI) & natural language processing (NLP) technologies. The dialog bot was created in Python & is intended for use by students to answer questions about advice. We utilizing a intent built domain-specific compilation as the knowledge source for the talkbots, which is made up of the typical queries students ask advisers & their answers. By analysing the inputs, the talktbot engine ascertains the client's needs and, with an efficiency of 80.00 % in English and 75.00 % in Arabic, finds the most reasonable answer that meets the intention. We also tested the software solution as well as the talkbot's precision using field tests with pupils to see how well it responded to live input. © 2023 IEEE.

关键词： Students

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AI-Based advanced Talk-chatbot for Implementation 3

AI-Based advanced Talk-chatbot for Implementation

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Krishnam, Nagendra Prasad Bora, Ashim Swathi, R.S.V.Rama Gehlot, Anita Talwar, Suresh Raghu, T. Telangana Hyderabad India Kampur College Assam Kampur India Koneru Lakshmaiah Education Foundation Kl Business School Andhra Pradesh Guntur India Uttaranchal University Uttaranchal Institute of Technology India St. Martin's Engineering College Dept. of It Telangana Secunderabad India Sr University School of Sciences Telangana Warangal India

ISBN: (纸本)9798350399264

Traditional technologies are transforming how businesses interact with their customers, creating demands for prompt responses & continuous accessibility. Students frequently have questions about college & university policies & procedures, academic process, extracurricular, as well as other aspects of educational career. There is a lack of service satisfaction because the educational advisers as well as the student affairs staff are overloaded with inquiries & unable to respond to them right away. In our work, we develop a multilingual talkbot that converses among learners in both English & Arabic using Artificial Intelligence (AI) & natural language processing (NLP) technologies. The dialog bot was created in Python & is intended for use by students to answer questions about advice. We are utilizing a intent built domain-specific compilation as the knowledge source for the talkbots, which is made up of the typical queries students ask advisers & their answers. By analysing the inputs, the talktbot engine ascertains the client's needs and, with an efficiency of 80.00 % in English and 75.00 % in Arabic, finds the most reasonable answer that meets the intention. We also tested the software solution as well as the talkbot's precision using field tests with pupils to see how well it responded to live input. © 2023 IEEE.

关键词： Students

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Automated Software Design Using Machine Learning with natural language processing

Automated Software Design Using Machine Learning with Natura...

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international econference on Computer and knowledge engineering (ICCKE)

作者： Fahimeh Khedmatkon Seyed Mohammad Hossein Hasheminejad Jaleh Shoshtarian Malak Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Information Technology Department Tehran University of Medical Sciences Tehran Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

Designing is the most difficult task in software production and development. Researchers are trying to add automation to the software design and development process. This is because of the daily increase of demanding software production; the quality growth of automotive software design makes designers respond more quickly to the refreshed demands. One of the good designing methods is to use of data mining. In this research, the decision tree algorithm method is used to identify classes and detect relationships between classes automatically. The data input is the proposed method is the use case descriptions software and its output is the list of classes with relationships between them. This method has three 3 stages: First, the use case description scenarios are analyzed by natural language processing tools. In the next step, by setting rules, the data set is created, and then the decision tree algorithm is executed on the dataset. Output is the list of classes along with the type of relationship between them. Extracting of association relations one-to-one, one-to-many, and many-to-many and association relationships. ATM, CBS, GDP, and SC case studies have been used. In this research, an attempt has been made to provide a method that has better results than previous methods. The accuracy and F-score of proposed method on the ATM dataset are 90% and 0.93 which is a value of accuracy and F-score is better than similar research and has better results.

关键词： knowledge engineering Software design Accuracy Economic indicators Software algorithms Production Machine learning Software natural language processing Decision trees

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