Answering System Research Articles

CPEQA: A Large Language Model Based Knowledge Base Retrieval System for Chinese Confidentiality Knowledge Question Answering

Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, particularly in the construction of the intelligent question-answering system. These systems, especially in specialized fields, usually rely on NLP through the retrieval of corpus and answering databases to efficiently provide accurate and concise answers. This paper focuses on the national confidentiality publicity and education field, aiming to address the dilemma of inaccurate knowledge retrieval in this field. Therefore, we design an intelligent confidentiality question-answering system CPEQA by comprehensively utilizing the LLMs platform and information retrieval technique. CPEQA is capable of providing professional answers to questions about Chinese confidentiality publicity and education raised by users. Additionally, we also integrate the conventional database retrieval technique and LLMs into the database query construction, enabling CPEQA to perform real-time queries and data analysis for both single-table and multi-table querying tasks. Through extensive experiments with generated query sentences, we show both methodological comparisons and empirical evaluations of CPEQA’s performance. Experimental results indicate that CPEQA has achieved competitive results on answering precision, recall rate and other metrics. Finally, we explore the challenges of the CPEQA system associated with these techniques and outline potential avenues for future research in this emerging field.

Quantum-Inspired Fusion for Open-Domain Question Answering

Open-domain question-answering systems need models capable of referencing multiple passages simultaneously to generate accurate answers. The Rational Fusion-in-Decoder (RFiD) model focuses on differentiating between causal relationships and spurious features by utilizing the encoders of the Fusion-in-Decoder model. However, RFiD reliance on partial token information limits its ability to determine whether the corresponding passage is a rationale for the question, potentially leading to inappropriate answers. To address this issue, we propose a Quantum-Inspired Fusion-in-Decoder (QFiD) model. Our approach introduces a Quantum Fusion Module (QFM) that maps single-dimensional into multi-dimensional hidden states, enabling the model to capture more comprehensive token information. Then, the classical mixture method from quantum information theory is used to fuse all information. Based on the fused information, the model can accurately predict the relationship between the question and passage. Experimental results on two prominent ODQA datasets, Natural Questions and TriviaQA, demonstrate that QFiD outperforms the strong baselines in automatic evaluations.

Numerical and experimental frequency analysis of damaged fiber‐reinforced metal laminated composite

AbstractThis research numerically evaluated the eigenvalue responses of the damaged fiber metal laminate (FML) structure. The current numerical model is delved into including crack‐type damage effect on the structural integrity/ stiffness via frequency response. In this regard, a complete mathematical model of FML has been derived through higher‐order shear deformation kinematics for computational purposes. The mathematical model is converted to computer code in the MATLAB platform to predict the eigenvalue responses of FML components with and without damage. The damage influences are considered via circular meshing of finite element steps associated with the isoparametric element (nine nodes and nine degrees of freedom for each node). The solution consistency is checked via convergence, and the results are compared with data available in the open domain. Further, an experimental test is carried out to improve confidence using an in‐house test rig. The experiment was conducted by varying the length of the FML in 0.1 m increments. The results showed good agreement between the numerical and experimental data, with the lowest deviation being 0.29% and the highest 8.97%. The developed numerical model is extended to analyze the influences of design parameters such as geometry shape, aspect ratio (a/b), thickness ratio (a/h) ratio, curvature ratio, and fiber layup sequence on the natural frequency response.

Modular Co-attention Networks in Nepali Visual Question Answering Systems

Visual question answering (VQA) has been regarded as a challenging task requiring a perfect blend of computer vision and natural language processing. As no dataset was available to train such a model for the Nepali language, a new dataset was developed during the research by translating the VQAv2 dataset. Then the dataset consisting of 202,577 images and 886,560 questions was used to train an attention-based VQA model. The dataset consists of yes/no, counting, and other questions with primarily one-word answers. Modular Co-attention Network (MCAN) was applied to the visual features extracted using the Faster RCNN framework and question embeddings extracted using the Nepali GloVe model. After co-attending the visual and language features for a few cascaded MCAN layers, the features are fused to train the whole network. During evaluation, an overall accuracy of 69.87% was obtained with 81.09% accuracy in yes/no type questions. The results surpassed the performance of models developed for Hindi and Bengali languages. Overall, novel research has been done in the Nepali Language VQA domain paving the way for further advancements.

Application of a Question Answering System in the Ontology-Based Health Domain and Question Templates to Improve the Quality of Answers

Developing an effective Question Answering System (QAS) to address a wide range of health-related inquiries presents a significant challenge. This study introduces an innovative approach using an ontology-based QAS within the health domain, leveraging the Resource Description Framework (RDF) and SPARQL query language. By utilizing ontology, the system can more accurately map health concepts, improving the relevance and precision of its responses. Additionally, the incorporation of structured Question Templates enhances the system’s ability to understand and respond to diverse user queries. The system's performance was evaluated using Black Box Testing, which demonstrated that it consistently provides accurate and relevant answers. The system achieved a Mean Average Precision (MAP) of 79.6%, indicating its potential to effectively address a broad spectrum of health issues.

Hypergraph Convolutional Networks with Multi-Ordering Relations for Cross-Document Event Coreference Resolution

Recognizing the coreference relationship between different event mentions in the text (i.e., event coreference resolution) is an important task in natural language processing. It helps to understand the association between various events in the text, and plays an important role in information extraction, question answering systems, and reading comprehension. Existing research has made progress in improving the performance of event coreference resolution, but there are also some shortcomings. For example, most of the existing methods analyze the event data in the document in a serial processing mode, without considering the complex relationship between events, and it is difficult to mine the deep semantics of events. To solve these problems, this paper proposes a cross-document event co-reference resolution method (HGCN-ECR) based on hypergraph convolutional neural networks. Firstly, the BiLSTM-CRF model was used to label the semantic role of the events extracted from a number of scientific and technical kinds of literature. According to the labeling results, the trigger words and non-trigger words of the event were determined, and the multi-document event hypergraph was constructed around the event trigger words. Then hypergraph convolutional neural networks are used to learn higher-order semantic information in multi-document event hypergraphs, and multi-head attention mechanisms are introduced to understand the hidden features of different event relationship types by treating each event relationship as a set of separate attention mechanisms. Finally, the feed-forward neural network and the average link clustering method are used to calculate the coreference score of events and complete the coreference event clustering, and the cross-document event coreference resolution is realized. The experimental results show that the cross-document event co-reference resolution method is superior to the baseline model.

Driving sustainable energy transitions with a multi-source RAG-LLM system

By 2035, the UK aims to upgrade all homes to achieve a net-zero economy by 2050, thereby reducing energy consumption, household costs, and improving living conditions. Small and Medium-sized Enterprises (SMEs) play a crucial role in this transition. However, many SME contractors lack essential information on Sustainable Energy Initiatives (SEIs) and the relevant Energy landscape necessary for driving Sustainable Energy Transitions (SETs). This knowledge gap poses risks to SME interventions, potentially leading to increased costs and inefficiencies. Accessing timely information on SEIs including government policies, funding, technologies, and environmental impacts from various media sources is essential for guiding effective SETs and understanding the relevant Energy landscape, thereby facilitating informed decision-making. Currently, SMEs lack an integrated system that consolidates data from diverse media sources into a centralized Information System (IS), limiting their ability to effectively navigate SEIs. To address this gap, this research introduces an Energy Chatbot, a sustainable IS that utilizes Large Language Models (LLMs) integrated with multi-source Retrieval Augmented Generation (RAG). This system encompasses diverse media sources, including news articles, government reports, industry publications, academic research, and social media. The Energy Chatbot is designed to enhance decision-making for SMEs by providing comprehensive Energy sector insights through a Question Answering (QA) system. Key findings emphasize that this approach reduces costs by utilizing open-source models. Moreover, the Energy Chatbot provides SMEs with access to up-to-date information, enabling them to identify long-term sustainability strategies and maintain a competitive edge in the evolving Energy landscape.

Open Domain Target Detection Based on Image-text Alignment Large Model under Railway Smart Construction Site

Abstract Open domain target detection plays a crucial role in ensuring people’s life safety in Railway Smart Construction Site. This paper proposes a target detection method that is not limited by the number of labels, which is different from traditional models that are usually trained and tested on a fixed set of object categories (or a set of labels). Firstly, the pre-training GLIP model is utilized to train the collected image-text pairing data to generate grounding boxes. Secondly, the pseudo-labeled data formed by associating these boxes with the corresponding text descriptions were combined with the original image-text data to form an enhanced dataset. The final model performs inference based on the prompt and incoming image data to determine the localization information. The GLIP model effectively aligns image and text information and enhances its ability to recognize and handle problems by observing and understanding new situations.

COMPARATIVE ANALYSIS OF MULTILINGUAL QA MODELS AND THEIR ADAPTATION TO THE KAZAKH LANGUAGE

This paper presents a comparative analysis of large pretrained multilingual models for question-answering (QA) systems, with a specific focus on their adaptation to the Kazakh language. The study evaluates models including mBERT, XLM-R, mT5, AYA, and GPT, which were tested on QA tasks using the Kazakh sKQuAD dataset. To enhance model performance, fine-tuning strategies such as adapter modules, data augmentation techniques (back-translation, paraphrasing), and hyperparameter optimization were applied. Specific adjustments to learning rates, batch sizes, and training epochs were made to boost accuracy and stability. Among the models tested, mT5 achieved the highest F1 score of 75.72%, showcasing robust generalization across diverse QA tasks. GPT-4-turbo closely followed with an F1 score of 73.33%, effectively managing complex Kazakh QA scenarios. In contrast, native Kazakh models like Kaz-RoBERTa showed improvements through fine-tuning but continued to lag behind larger multilingual models, underlining the need for additional Kazakh-specific training data and further architectural enhancements. Kazakh’s agglutinative morphology and the scarcity of high-quality training data present significant challenges for model adaptation. Adapter modules helped mitigate computational costs, allowing efficient fine-tuning in resource-constrained environments without significant performance loss. Data augmentation techniques, such as back-translation and paraphrasing, were instrumental in enriching the dataset, thereby enhancing model adaptability and robustness. This study underscores the importance of advanced fine-tuning and data augmentation strategies for QA systems tailored to low-resource languages like Kazakh. By addressing these challenges, this research aims to make AI technologies more inclusive and accessible, offering practical insights for improving natural language processing (NLP) capabilities in underrepresented languages. Ultimately, these findings contribute to bridging the gap between high-resource and low-resource language models, fostering a more equitable distribution of AI solutions across diverse linguistic contexts.

Strong Solutions of Brusselator System

The study involves a mathematical analysis of the Brusselator system on a convex bounded three-dimensional open domain, considering Neumann boundary conditions. We establish the global existence and uniqueness of the strong solution for this system. Achieving high regularity for the strong solution requires stringent conditions on the initial data. The study demonstrates the continuous dependence of the solution on the initial conditions.

Construction of Intelligent Question-Answering System to Improve Knowledge Management Service from the Perspective of Education Informatization

Construction of Intelligent Question-Answering System to Improve Knowledge Management Service from the Perspective of Education Informatization

A Chinese Nested Named Entity Recognition Model for Chicken Disease Based on Multiple Fine-Grained Feature Fusion and Efficient Global Pointer

Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we constructed the CDNER dataset and developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the multiple fine-grained feature fusion (MFGFF) module with a BiLSTM neural network and employs an efficient global pointer (EGP) to predict the entity location encoding. In the MFGFF module, we designed three encoders: the character encoder, word encoder, and sentence encoder. This design effectively captured fine-grained features and improved the recognition accuracy of nested entities. Experimental results showed that the model performed robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of a knowledge graph and intelligent question-answering system for chicken diseases, but also provides a viable solution for extracting disease information that can be applied to other livestock species.

Collaboration of Large and Small Models for Event Type Discovery in Completely Open Domains

Collaboration of Large and Small Models for Event Type Discovery in Completely Open Domains

Government chatbot: Empowering smart conversations with enhanced contextual understanding and reasoning

Currently, an increasing number of governments have adopted question answering systems (QASs) in public service delivery. As some citizens with limited information literacy often express their questions vaguely when interacting with a chatbot, it is necessary to improve the contextual understanding and reasoning ability of government chatbots (G-chatbots). This goal can be achieved through the optimisation of the matching between question, answer and context. By incorporating the Relational Graph Convolutional Networks (R-GCNs) and fuzzy logic, this study proposes a multi-turn dialogue model that introduces a re-question mechanism and a subgraph matching algorithm. The experiment results show that the model can improve the contextual reasoning ability of G-chatbots by about 10% and generate answers in a more explainable way. This study innovatively integrates a question–answer–context matching approach, re-question mechanism into the MTRF-G-chatbot model, reducing barriers to citizens’ access to government services and enhancing contextual reasoning abilities.

A Large Language Model-Based Question Answering System for Online Public Administrative Services

Public administrative services are one of the key concerns for citizens; however, there are still numerous procedures and issues that citizens encounter and need clarification on. This study aims to alleviate some of this burden on officials by developing a Question Answering System (QnA) to address inquiries about public administrative services. We present a framework for utilizing Large Language Models to develop and implement a QnA system on the Online Public Administrative Service Portal in Ha Giang Province.We also provide extensive experimental analysis to demonstrate the effectiveness of each component in the QnA system.

Intelligent maritime question-answering and recommendation system based on maritime vessel activity knowledge graph

Traditional maritime traffic management typically relies on positioning data for data mining without incorporating other multi-source data to analyze the maritime vessel activity, which cannot conduct comprehensive maritime knowledge mining. Thus, this study integrates multi-source data, such as trajectory, maritime accident text, and geographic data, to create a maritime vessel activity knowledge graph. On this basis, a question-answering model is developed based on a bidirectional question-answering attention graph neural network, and a personalized recommendation model is developed based on an attention-enhanced joint knowledge propagation and a user preference graph neural network. The former assists users in extracting valuable information from the maritime vessel activity knowledge graph, while the latter predicts the users' potential interests and automatically recommends vessel entities based on their historical query information. Experimental results show that the proposed question-answering model improved the F1-score by 2.31%–10.09% compared to state-of-the-art baseline models on the MVA question-answering dataset. Similarly, the proposed personalized recommendation model improved the click-through rate prediction accuracy by 2.46%–7.05% compared to state-of-the-art baseline models on the MVA personalized recommendation dataset.

Design and Implementation of an Interactive Question-Answering System with Retrieval-Augmented Generation for Personalized Databases

This study introduces a novel approach to personalized information retrieval by integrating retrieval augmentation generation (RAG) with a personalized database system. Recent advancements in large language models (LLMs) have shown impressive text generation capabilities but face limitations in knowledge accuracy and hallucinations. Our research addresses these challenges by combining LLMs with structured, personalized data to enhance search precision and relevance. By tagging keywords within personal documents and organizing information into context-based categories, users can conduct efficient searches within their data repositories. We conducted experiments using the GPT-3.5 and text-embedding-ada-002 models and evaluated the RAG assessment framework with five different language models and two embedding models. Our results indicate that the combination of GPT-3.5 and text-embedding-ada-002 is effective for a personalized database question-answering system, with potential for various language models depending on the application. Our approach offers improved accuracy, real-time data updates, and enhanced user experience, making a significant contribution to information retrieval by LLMs and impacting various artificial intelligence applications.

TourMapQA: using deep learning to develop a vietnam map-based tourism question answering system

<p>A question answering system is an important task in information retrieval. In recent years, this system has been interested in research and achieved outstanding results. In general, the output of the question answering is text. However, few studies have used a map as an answer to the question answering in Vietnam tourism. This paper introduces a question answering system integrating long short-term memory (LSTM) on the Vietnam map. Specifically, our model received an input question about any road in Vietnam. Then, the model used LSTM to indicate the coordinate of that road and called the Dijkstra algorithm to find the shortest path from the current location to the input road. Next, from the coordinate of the input road, we leveraged the LSTM model to identify sightseeing places that were on the shortest path. Finally, our system showed all the sightseeing places on the Vietnam map. Technically, the experimental results showed that our model’s performance was improved than previous models such as recurrent neural network, recurrent neural network with embedding, bidirectional recurrent neural network, and encoder-decoder recurrent neural network. Practically in terms, we applied our method to build a real application and compared it with Google Maps, and Bing Map.</p>

Strong Solutions of the Neuronal Responses System

We explore a connected reaction-diffusion system involving neural responses within limited convex open domains . Utilizing the Classical Faedo-Galerkin technique and employing compactness arguments, we establish the existence, uniqueness, and continuous dependence on initial data for strong solutions.

Application of large language models based on knowledge graphs in question-answering systems: A review

The integration of Knowledge Graphs (KGs) and Large Language Models (LLMs) is emerging as a transformative advancement in AI, particularly within Question Answering (QA) systems. Traditional QA systems, constrained by static knowledge bases, have struggled with multimodal queries and personalized responses. The deep integration of KGs and LLMs offers a novel approach, combining the structured, contextual understanding of KGs with the semantic parsing capabilities of LLMs. This review explores the methodologies, algorithms, datasets, and applications of KG-LLM integration in QA systems, highlighting key research that enhances model performance, reasoning, and domain-specific applications. It addresses technical challenges such as data consistency, semantic understanding, and system optimization. Future directions focus on improving intrinsic model performance and achieving deeper integration of KGs and LLMs. This comprehensive overview underscores the potential of KG-LLM synergy to significantly improve service quality across diverse industries, including education and healthcare.