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Multimodal Learning in natural language processing: Fact-Checking, Ethics and Misinformation Detection

Multimodal Learning in Natural Language Processing: Fact-Che...

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2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Joshi, Himanshu Sanjay Taherdoost, Hamed College of Business Westcliff University CA United States College of Technology & Engineering Westcliff University CA United States University Canada West Vancouver Canada Gus Institute Global University Systems London United Kingdom Q Minded Quark Minded Technology Inc. Vancouver Canada

ISBN: (纸本)9798331509828

Multimodal learning (MML) is a subtype of deep learning that improves the capabilities of machine learning models by integrating modalities from multiple data sources such as text, images, audio, and video. This integration of modalities produces a more comprehensive understanding of complex data and nuanced outputs. Applications of this approach in real-time decision-making, information retrieval, and knowledge representation can improve the insights derived from the natural language processing (NLP) system. Moreover, in a time of information overload, there are concerns regarding the credibility of the content. The role of NLP systems is central to processing textual data in MML systems. In this environment, the ethical development of multimodal systems for fact-checking is crucial. This paper addresses an existing gap in use of multimodal learning in the context of fact-checking by investigating issues related to integration of NLP, Question-Answering (QA) systems, and knowledge graphs to combat misinformation. This research presents multimodal data based frameworks based on knowledge graph integration. The exploration of the frameworks enables to enhance the accuracy, reliability, and ethical soundness of fact-checking mechanisms. This study also highlights new directions for overcoming challenges related to modality alignment, computational efficiency, and ethical transparency. © 2025 ieee.

关键词： knowledge graph

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Revolutionizing Staffing and Recruiting with Contextual knowledge Graphs and QNLP: An End-to-End Quantum Training Paradigm 14

Revolutionizing Staffing and Recruiting with Contextual Know...

引用

14th ieee international conference on knowledge Graph (ieee ICKG)

作者： Vijayakumar, Senthilkumar Louis, Filious Dallas TX United States

ISBN: (纸本)9798350307092

The staffing and recruiting industry is continuously evolving, and recent advancements in knowledge Graphs (KG) and Quantum natural language processing (QNLP) has garnered considerable attention. The integration of these state-of-the-art technologies is fueled by the necessity to improve language models' capacity to comprehend context and make precise decisions. This research paper presents a novel approach to revolutionize the staffing and recruiting industry by integrating knowledge Graph (KG) and Quantum natural language processing (QNLP) to formulate an end-to-end QNLP training pipeline. The proposed solution consists of three interdependent subsystems that work in unison to construct contextual KG and train language models. The Information Extraction subsystem extracts semantic relationships and connections between entities from large and complex recruitment data to construct domain specific contextual KG. The QNLP model training pipeline subsystem, which is fed with domain-rich KG data, runs on Quantum Circuits, accelerates the training process by effectively incorporating high-dimensional features to the deep layers of language models. Finally, the Information Retrieval subsystem is based on semantic data taxonomy, retrieving contextual data from the KG for the trained language models to be implemented on various distinctive use cases in the staffing and recruiting industry. This solution provides a faster and more contextual approach to analyze recruitment data, empowering recruiters to concentrate on strategic tasks such as candidate engagement and client relationship building, ultimately leading to better business decision-making capabilities.

关键词： Artificial Intelligence (AI) knowledge Graph (KG) Quantum natural language processing (QNLP) Large language Models (LLM) Contextual Information Extraction & Retrieval Systems

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Research on Educational knowledge Base Question Answering System Based on Large language Model 7

Research on Educational Knowledge Base Question Answering Sy...

引用

7th international conference on Computer Information Science and Application Technology, CISAT 2024

作者： Gong, Yonggang Liu, Hongyu Lian, Xiaoqin Ma, Guochun Beijing Technology and Business University Beijing China

ISBN: (纸本)9798350375107

In recent years, methods based on pre-trained large models have received extensive attention in the field of natural language processing. The large language model has the potential to reduce the development cost of question answering systems in the education field and improve the accuracy of question answering system. In this paper, based on the curriculum standards of compulsory education, an educational knowledge base is constructed by using the method of label alignment optimization, and a professional large language model question answering system is constructed based on the large language model to meet the requirements of the new curriculum standards, so as to generate a high-availability teaching scheme. Secondly, a prompt word method based on educational keyword template is proposed, aiming at improving the quality of question answering generation of large language model in educational question answering system. The experimental results show that this system is superior to the universal large language model in both objective and subjective evaluation, and has higher usability in the field of education and more accuracy in generating answers. © 2024 ieee.

关键词： ChatGLM3-6B Educational knowledge base prompt engineering Question answering system The field of education

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A Comprehensive Survey on Methods Used for Automatic Assessment of Subjective Answers

A Comprehensive Survey on Methods Used for Automatic Assessm...

引用

2024 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Joshi, Sudarshan Bachkar, Akshay Awaje, Omkar Bhoir, Rhutuj Savitribai Phule Pune University Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350348460

The paper explores various techniques for identifying multilingual text similarities and basic image similarities, covering a wide array of methods and opportunities for comparing and evaluating textual and diagrammatic content. By delving into these categories and studying the pros and cons of various methods, the survey equips researchers, practitioners, and enthusiasts in the field of NLP with a deeper understanding of text similarity *** paper delves into tokenization, vectorization, and similarity metrics, among other approaches. It also discusses modelling techniques, such as LSA and LDA, text alignment algorithms, cosine similarity, BERT, and optimization techniques. The survey categorizes these techniques into corpus-based, knowledge-based, semantic, term-based, and character-based similarities. The survey aims to provide a comprehensive overview of the methods available for checking multilingual text similarity identification and its significance in contemporary text analysis. Index Terms - Similarity Techniques, Machine learning, natural language processing. © 2024 ieee.

关键词： Image Similarity Multilingualism natural language processing Semantic Analysis Similarity Techniques

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Enhanced Database Interaction using Large language Models for Improved Data Retrieval and Analysis 2

Enhanced Database Interaction using Large Language Models fo...

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2nd international conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Usha, V Abhinash, Nalagarla Chiru Chowdary, Sakhamuri Nitin Sathya, V Reddy, Eeda Ramakrishna Priya, Sathiya S. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept CSE Chennai Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Chennai Tamil Nadu India Velammal Engn Coll Dept Informat Technol Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

One of the difficult task for many users on SQL is to write the SQL Query due to its syntax and structure. If a person needs to query a database, they should know everything about how data is distributed and what the internal dependencies are. For this reason, it is not easy for everyone to access data in database without proper knowledge. This paper presents a novel application that leverages generative AI and natural language processing (NLP) to enable users to interact with databases using natural language queries. Built on the Gemini API, the application translates user queries into SQL queries, simplifying database interactions for non-technical users. The Python-based backend, SQLite database management, and Streamlit frontend provide a comprehensive solution for database querying and analysis. This approach democratizes data retrieval and analysis, offering automated insights and visualizations to users of all skill levels. The app also features automated data analysis, which boosts insight generation for users of all skill levels. Further, the traditional ways of querying SQL generally require specialized knowledge and are only accessible to those with technical backgrounds. When the application takes charge of the query construction process and offers data as UI elements, it can invigorate users to have a higher degree of insight into what their data actually is and explore it even more efficiently. The Python software stack combines Python for backend processing, SQLite for database management and a web-based frontend for user interaction to provide an all-encompassing database querying and analysis solution.

关键词： Generative AI natural language processing Text to SQL Generation Gemini API Large language Model Machine Learning Deep Learning Google LLM SQLQuery Generation Prompt engineering Data Retrieval

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Stop Words for processing Software engineering Documents: Do they Matter? 2

Stop Words for Processing Software Engineering Documents: Do...

引用

2nd ieee/ACM international Workshop on natural language-Based Software engineering (NLBSE)

作者： Fan, Yaohou Arora, Chetan Treude, Christoph Univ Melbourne Melbourne Vic Australia Monash Univ Melbourne Vic Australia

ISBN: (纸本)9798350301786

Stop words, which are considered non-predictive, are often eliminated in natural language processing tasks. However, the definition of uninformative vocabulary is vague, so most algorithms use general knowledge-based stop lists to remove stop words. There is an ongoing debate among academics about the usefulness of stop word elimination, especially in domain-specific settings. In this work, we investigate the usefulness of stop word removal in a software engineering context. To do this, we replicate and experiment with three software engineering research tools from related work. Additionally, we construct a corpus of software engineering domain-related text from 10,000 Stack Overflow questions and identify 200 domain-specific stop words using traditional information-theoretic methods. Our results show that the use of domain-specific stop words significantly improved the performance of research tools compared to the use of a general stop list and that 17 out of 19 evaluation measures showed better performance.

关键词： Software engineering Documents natural language processing (NLP) Stop Words

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Thai knowledge-Augmented language Model Adaptation (ThaiKALA) 18

Thai Knowledge-Augmented Language Model Adaptation (ThaiKALA...

引用

18th international Joint Symposium on Artificial Intelligence and natural language processing (ISAI-NLP) / 4th international conference on Artificial Intelligence and Internet of Things (AIoT)

作者： Ruangchutiphophan, Pavaris Saetia, Chanatip Ayutthaya, Thititorn Seneewong Na Chalothorn, Tawunrat Kasikorn Business Technol Grp Bangkok Thailand

ISBN: (纸本)9798350371215

Large language models have exhibited considerable prowess in diverse NLP tasks, demonstrating promising performance. However, they still have limitations in effectively capturing domain-specific knowledge and contextually-relevant information, resulting in hallucination issues. To address these challenges, This paper presents ThaiKALA, a framework designed for the Thai language to augment domain-specific knowledge into the language model. The framework utilizes three modules to handle Thai language specifically: event extraction, a self-defined ID database, and a multilingual language model. To confirm the performance, the framework is also evaluated with strong generative baselines like GPT-3 and GPT-3.5-turbo-16k. As a result, ThaiKALA, with only Entity Memory, outperforms all baselines including GPT-3 and GPT-3.5 in extractive Question Answering (EQA) tasks, achieving a higher exact match (42.48%) and competitive F1 scores (67.07%). These results demonstrate that ThaiKALA is effective in enhancing the language model's performance on Thai extractive QA by augmenting the extracted knowledge.

关键词： knowledge-Augmented language Model Question Answering

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Leveraging Large language Models to Improve REST API Testing 46

Leveraging Large Language Models to Improve REST API Testing

引用

ieee/ACM 46th international conference on Software engineering - New Ideas and Emerging Results (ICSE-NIER)

作者： Kim, Myeongsoo Stennett, Tyler Shah, Dhruv Sinha, Saurabh Orso, Alessandro Georgia Inst Technol Atlanta GA 30332 USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9798400705007

The widespread adoption of REST APIs, coupled with their growing complexity and size, has led to the need for automated REST API testing tools. Current tools focus on the structured data in REST API specifications but often neglect valuable insights available in unstructured natural-language descriptions in the specifications, which leads to suboptimal test coverage. Recently, to address this gap, researchers have developed techniques that extract rules from these human-readable descriptions and query knowledge bases to derive meaningful input values. However, these techniques are limited in the types of rules they can extract and prone to produce inaccurate results. This paper presents RESTGPT, an innovative approach that leverages the power and intrinsic context-awareness of Large language Models (LLMs) to improve REST API testing. RESTGPT takes as input an API specification, extracts machine-interpretable rules, and generates example parameter values from natural-language descriptions in the specification. It then augments the original specification with these rules and values. Our evaluations indicate that RESTGPT outperforms existing techniques in both rule extraction and value generation. Given these promising results, we outline future research directions for advancing REST API testing through LLMs.

关键词： Large language Models for Testing OpenAPI Specification Analysis

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Meta Learning for Domain Agnostic Soft Prompt 48

Meta Learning for Domain Agnostic Soft Prompt

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48th ieee international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Chen, Ming-Yen Rohmatillah, Mahdin Lee, Ching-Hsien Chien, Jen-Tzung National Yang Ming Chiao Tung University Institute of Electrical and Computer Engineering Taiwan Industrial Technology Research Institute Information and Communications Research Labs Taiwan

ISBN: (纸本)9781728163277

The prompt-based learning, as used in GPT-3, has become a popular approach to extract knowledge from a powerful pre-trained language model (PLM) for natural language understanding tasks. However, either applying the hard prompt for sentences by defining a collection of human-engineering prompt templates or directly optimizing the soft or continuous prompt with labeled data may not really generalize well for unseen domain data. To cope with this issue, this paper presents a new prompt-based unsupervised domain adaptation where the learned soft prompt is able to boost the frozen pre-trained language model to deal with the input tokens from unseen domains. Importantly, the meta learning and optimization is developed to carry out the domain agnostic soft prompt where the loss for masked language model is minimized. The experiments on multi-domain natural language understanding tasks show the merits of the proposed method. © 2023 ieee.

关键词： Domain adaptation meta-learning natural language understanding prompt-based learning

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Exploring Large language Models for natural language processing 2

Exploring Large Language Models for Natural Language Process...

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2nd international conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Salau, Ayodeji Olalekan Emmanuel, Esuku Dickson Alemran, Ahmed Dixit, Chandra Kumar Braide, Sepiribo Lucky Afe Babalola University College of Engineering Department of Electrical/Electronics and Computer Engineering Ado Ekiti Nigeria Misan University Medicine College Department of Software Engineering Iraq Dr. Shakuntala Misra National Rehabilitation University Institute of Engineering and Technology Uttar Pradesh Lucknow India Rivers State University Department of Electrical and Electronics Engineering Port Harcourt5080 Nigeria

ISBN: (纸本)9798350364699

Large language models have become increasingly popular in natural language processing due to their ability to generate high-quality text and perform various language-related tasks. However, there are growing concerns regarding the ethical implications of these models, particularly concerning potential misuse and bias. This paper delves into the advancements and limitations of large language models for natural language processing (NLP), exploring their ability to improve language understanding and generation. The paper also examines the challenges these models face in terms of computational power and model interpretability. Finally, the paper provides recommendations for future research to address these concerns, ensuring the responsible development of large language models and their integration into the future of NLP and Artificial Intelligence (AI). © 2024 ieee.

关键词： Artificial Intelligence natural language processing Bias Ethical concerns Large language models Limitations

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