Answering System Research Articles

Assessing the Quality of Automatic-generated Short Answers using GPT-4

Open-ended assessments play a pivotal role in enabling instructors to evaluate student knowledge acquisition and provide constructive feedback. Integrating large language models (LLMs) such as GPT-4 in educational settings presents a transformative opportunity for assessment methodologies. However, existing literature on LLMs addressing open-ended questions lacks breadth, relying on limited data or overlooking question difficulty levels. This study evaluates GPT-4’s proficiency in responding to open-ended questions spanning diverse topics and cognitive complexities in comparison to human responses. To facilitate this assessment, we generated a dataset of 738 open-ended questions across Biology, Earth Sciences, and Physics and systematically categorized it based on Bloom’s Taxonomy. Each question included eight human-generated responses and two from GPT-4. The outcomes indicate GPT-4’s superior performance over humans, encompassing both native and non-native speakers, irrespective of gender. Nevertheless, this advantage was not sustained in ’remembering’ or ’creating’ questions aligned with Bloom’s Taxonomy. These results highlight GPT-4’s potential for underpinning advanced question-answering systems, its promising role in supporting non-native speakers, and its capacity to augment teacher assistance in assessments. However, limitations in nuanced argumentation and creativity underscore areas necessitating refinement in these models, guiding future research toward bolstering pedagogical support.

Integrating deep learning for visual question answering in Agricultural Disease Diagnostics: Case Study of Wheat Rust.

This paper presents a novel approach to agricultural disease diagnostics through the integration of Deep Learning (DL) techniques with Visual Question Answering (VQA) systems, specifically targeting the detection of wheat rust. Wheat rust is a pervasive and destructive disease that significantly impacts wheat production worldwide. Traditional diagnostic methods often require expert knowledge and time-consuming processes, making rapid and accurate detection challenging. We drafted a new, WheatRustDL2024 dataset (7998 images of healthy and infected leaves) specifically designed for VQA in the context of wheat rust detection and utilized it to retrieve the initial weights on the federated learning server. This dataset comprises high-resolution images of wheat plants, annotated with detailed questions and answers pertaining to the presence, type, and severity of rust infections. Our dataset also contains images collected from various sources and successfully highlights a wide range of conditions (different lighting, obstructions in the image, etc.) in which a wheat image may be taken, therefore making a generalized universally applicable model. The trained model was federated using Flower. Following extensive analysis, the chosen central model was ResNet. Our fine-tuned ResNet achieved an accuracy of 97.69% on the existing data. We also implemented the BLIP (Bootstrapping Language-Image Pre-training) methods that enable the model to understand complex visual and textual inputs, thereby improving the accuracy and relevance of the generated answers. The dual attention mechanism, combined with BLIP techniques, allows the model to simultaneously focus on relevant image regions and pertinent parts of the questions. We also created a custom dataset (WheatRustVQA) with our augmented dataset containing 1800 augmented images and their associated question-answer pairs. The model fetches an answer with an average BLEU score of 0.6235 on our testing partition of the dataset. This federated model is lightweight and can be seamlessly integrated into mobile phones, drones, etc. without any hardware requirement. Our results indicate that integrating deep learning with VQA for agricultural disease diagnostics not only accelerates the detection process but also reduces dependency on human experts, making it a valuable tool for farmers and agricultural professionals. This approach holds promise for broader applications in plant pathology and precision agriculture and can consequently address food security issues.

FAQ-Gen: An automated system to generate domain-specific FAQs to aid content comprehension

Frequently Asked Questions (FAQs) refer to the most common inquiries about specific content. They serve as content comprehension aids by simplifying topics and enhancing understanding through succinct presentation of information. In this paper, we address FAQ generation as a well-defined Natural Language Processing task through the development of an end-to-end system leveraging text-to-text transformation models. We present a literature review covering traditional question-answering systems, highlighting their limitations when applied directly to the FAQ generation task. We propose a system capable of building FAQs from textual content tailored to specific domains, enhancing their accuracy and relevance. We utilise self-curated algorithms to obtain an optimal representation of information to be provided as input and also to rank the question-answer pairs to maximise human comprehension. Qualitative human evaluation showcases the generated FAQs as well-constructed and readable while also utilising domain-specific constructs to highlight domain-based nuances and jargon in the original content.

Improving ranking-based question answering with weak supervision for low-resource Qur’anic texts

AbstractThis work tackles the challenge of ranking-based machine reading comprehension (MRC), where a question answering (QA) system generates a ranked list of relevant answers for each question instead of simply extracting a single answer. We highlight the limitations of traditional learning methods in this setting, particularly under limited training data. To address these issues, we propose a novel ranking-inspired learning method that focuses on ranking multiple answer spans instead of single answer extraction. This method leverages lexical overlap as weak supervision to guide the ranking process. We evaluate our approach on the Qur’an Reading Comprehension Dataset (QRCD), a low-resource Arabic dataset over the Holy Qur’an. We employ transfer learning with external resources to fine-tune various transformer-based models, mitigating the low-resource challenge. Experimental results demonstrate that our proposed method significantly outperforms standard mechanisms across different models. Furthermore, we show its better alignment with the ranking-based MRC task and the effectiveness of external resources for this low-resource dataset. Our best performing model achieves a state-of-the-art partial Reciprocal Rank (pRR) score of 63.82%, surpassing the previous best-known score of 58.60%. To foster further research, we release code [GitHub repository:github.com/mohammed-elkomy/weakly-supervised-mrc], trained models, and detailed experiments to the community.

Open innovation between university-industry: A review of research trends and practices

This study aims to review papers on open innovation in the university by examining the research profile, open innovation form and the process underpinning, key variables used, and suggestions for potential future studies. This study conducted an analysis of 71 relevant articles among 2.759 obtained from Web of Science and Scopus databases. Content analysis was used to identify significant research concerns and gaps that could be addressed in future studies. This study offers the intersection of open innovation-focused university domains. This paper proposes a conceptual open innovation model in university settings by mapping key characteristics and identifying relevant areas for future research.

An improved frequency domain model for floating offshore wind turbines

Abstract A cost- and time-efficient design evaluation tool can assist the FOWT industry in evaluating many different concepts. An open source frequency domain model for FOWT applications shows promising results for the integral, coupled response, but clear differences to higher fidelity time-domain studies are observed. In this work, the hydrodynamic modelling is improved by using a potential flow diffraction code, and the implementation of the floating feedback mechanism has been improved. Attempts have been made to improve the mooring system modelling by implementing a lumped mass approach, and to include the excitation of a turbulent wind spectrum. A good match in the system’s motion responses is obtained in the wave frequent range when compared to time-domain results. At lower frequencies, the excitation from the turbulent wind spectrum remains to be improved on. At higher frequencies, the mooring line modelling is to be improved for reliable results. For both effects, a clear outline is set up to improve in future work. The current model is capable of accurately modelling fully coupled wave-frequent motions in combined wind-wave environments in an efficient manner. When the suggested improvements are incorporated in future work, it is expected that a good match in the low- and high-frequency ranges can be obtained as well, allowing to study the full motion- and mooring tension response. Finally the tool will give developers and designers the opportunity to evaluate a large amount of designs in a cost and time efficient manner.

Question Answering for Electronic Health Records: Scoping Review of Datasets and Models.

Question answering (QA) systems for patient-related data can assist both clinicians and patients. They can, for example, assist clinicians in decision-making and enable patients to have a better understanding of their medical history. Substantial amounts of patient data are stored in electronic health records (EHRs), making EHR QA an important research area. Because of the differences in data format and modality, this differs greatly from other medical QA tasks that use medical websites or scientific papers to retrieve answers, making it critical to research EHR QA. This study aims to provide a methodological review of existing works on QA for EHRs. The objectives of this study were to identify the existing EHR QA datasets and analyze them, study the state-of-the-art methodologies used in this task, compare the different evaluation metrics used by these state-of-the-art models, and finally elicit the various challenges and the ongoing issues in EHR QA. We searched for articles from January 1, 2005, to September 30, 2023, in 4 digital sources, including Google Scholar, ACL Anthology, ACM Digital Library, and PubMed, to collect relevant publications on EHR QA. Our systematic screening process followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 4111 papers were identified for our study, and after screening based on our inclusion criteria, we obtained 47 papers for further study. The selected studies were then classified into 2 non-mutually exclusive categories depending on their scope: "EHR QA datasets" and "EHR QA models." A systematic screening process obtained 47 papers on EHR QA for final review. Out of the 47 papers, 53% (n=25) were about EHR QA datasets, and 79% (n=37) papers were about EHR QA models. It was observed that QA on EHRs is relatively new and unexplored. Most of the works are fairly recent. In addition, it was observed that emrQA is by far the most popular EHR QA dataset, both in terms of citations and usage in other papers. We have classified the EHR QA datasets based on their modality, and we have inferred that Medical Information Mart for Intensive Care (MIMIC-III) and the National Natural Language Processing Clinical Challenges datasets (ie, n2c2 datasets) are the most popular EHR databases and corpuses used in EHR QA. Furthermore, we identified the different models used in EHR QA along with the evaluation metrics used for these models. EHR QA research faces multiple challenges, such as the limited availability of clinical annotations, concept normalization in EHR QA, and challenges faced in generating realistic EHR QA datasets. There are still many gaps in research that motivate further work. This study will assist future researchers in focusing on areas of EHR QA that have possible future research directions.

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.

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 documents. 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.

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.

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