A topic-enhanced python question-answering model by WANG Shuo, LIU Xin, LU Xuesong

“…With the development of large language model technology, the application of retrieval enhancement in the field of education has become one of the hot research directions, with the aim of alleviating the hallucination problem of large language models and improving the accuracy of large language models in answering educational questions. …”
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A topic-enhanced python question-answering model by WANG Shuo, LIU Xin, LU Xuesong

“…With the development of large language model technology, the application of retrieval enhancement in the field of education has become one of the hot research directions, with the aim of alleviating the hallucination problem of large language models and improving the accuracy of large language models in answering educational questions. …”
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Design: the answer of contemporary businesses to the requirements of sustainable development by Rodica Pamfilie, Roxana Procopie, Magdalena Bobe, Smaranda Vişan, Robert Bumbac

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Enhancing Visual Question Answering for Multiple Choice Questions by Rashi Goel, Harsh Nandwani, Eshaan Shah, Ashalatha Nayak, Archana Praveen Kumar

“…Using MCQs provides the model with some context of the correct answer, improving its performance over a simple multiclass classification task. …”
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Question Dependent Recurrent Entity Network for Question Answering by Andrea Madotto, Giuseppe Attardi

“…Question Answering is a task which requires building models capable of providing answers to questions expressed in human language. …”
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STRIVING FOR QUALITY - THE ANSWER OF HISTORY (AN EXAMPLE OF HOSPITAL SECTOR) by Darina Mineva

“…The internal motivation of the owners of the means of production increases with a motive for profit, at the expense of the external goal - the duty to society: paying taxes, improving the quality of life and securing salaries. …”
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TSQ: An Optimized Framework for Efficiently Answering Time Series Queries by Feifan Pu, Jianqiu Xu

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Question Answering System for Applicant Support by Using Modern Messengers by Dmitry R. Filonov, Dmitry Ju. Chalyy, Dmitry M. Murin, Valery G. Durnev, Valery A. Sokolov

“…In this paper, we consider a specialized question answering system that uses today’s messaging services infrastructure to support university applicants. …”
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Advancing medical question answering with a knowledge embedding transformer. by Xiang Zhu, Mustaqeem Khan, Abdelmalik Taleb-Ahmed, Alice Othmani

“…The results demonstrate the system's ability to deliver accurate and ethical answers. This integrated method improves response speed and quality. …”
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Recent Developments to the ANSWERS® Monte Carlo Codes MONK® and MCBEND® by Fildes Jessica, Richards Simon, Bird Adam, Cox Andrew, Fry Timothy, Hanlon David, Jones Brian, Long David, Tantillo Francesco, Wright George, Hiles Richard

“…MONK® and MCBEND® are two advanced Monte Carlo codes developed by the ANSWERS® Software Service, which have been used in over 30 countries worldwide for a range of applications, and have been well-established in the UK criticality and shielding communities respectively over the course of several decades. …”
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Visual Question Answering in Robotic Surgery: A Comprehensive Review by Di Ding, Tianliang Yao, Rong Luo, Xusen Sun

“…Visual Question Answering (VQA) in robotic surgery is rapidly becoming a pivotal technology in medical AI, addressing the complex challenge of interpreting multimodal surgical data to support real-time decision-making. …”
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Deep Memory Fusion Model for Long Video Question Answering by SUN Guanglu, WU Meng, QIU Jing, LIANG Lili

“…Long video question answering contains rich multimodal semantic information and inference information. …”
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Medical Knowledge-Based Differential Image Visual Question Answering by Fangpeng Lu, Songyan Liu, Wenbin Lu, Peng Chen, Boyang Ding

“…Experimental results demonstrate that our method significantly enhances the performance of the differential medical visual question answering task. This advancement is of considerable reference value in improving the applicability and interpretability of medical visual question answering.…”
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Solving Linear Programming Problems by Reducing to the Form with an Obvious Answer by Gleb D. Stepanov

“…The method is obtained by improving the classical simplex method, which when involving geometric considerations, in fact, generalizes the Gauss complete exclusion method for solving systems of equations. …”
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ReceiptQA: A Question-Answering Dataset for Receipt Understanding by Mahmoud Abdalla, Mahmoud SalahEldin Kasem, Mohamed Mahmoud, Bilel Yagoub, Mostafa Farouk Senussi, Abdelrahman Abdallah, Seung Hun Kang, Hyun Soo Kang

“…In this paper, we introduce <span style="font-variant: small-caps;">ReceiptQA</span>, a novel large-scale dataset designed for receipt understanding through question-answering (QA). <span style="font-variant: small-caps;">ReceiptQA</span> contains 171,000 question–answer pairs derived from 3500 receipt images, constructed via two complementary methodologies: (1) LLM-Generated Dataset: 70,000 synthetically generated QA pairs, where each receipt is paired with 20 unique, context-specific questions. …”
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MSAM：Video Question Answering Based on Multi-Stage Attention Model by LIANG Li-li, LIU Xin-yu, SUN Guang-lu, ZHU Su-xia

“…The video question answering (VideoQA) task requires understanding of semantic information of both the video and question to generate the answer.At present, it is difficult for VideoQA methods that are based on attention model to fully understand and accurately locate video information related to the question.To solve this problem, a multi-stage attention model network (MSAMN) is proposed. …”
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Is it a pediatric orthopaedic urgency or not? Can ChatGPT answer this question? by Sema Ertan Birsel, Onur Oto, Baris Görgün, İrem Hazal İnan, Ali Şeker, Muharrem İnan

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Proteomics approaches – their potential for answering complex questions in meat science research by Elisabeth Huff-Lonergan, Steven M. Lonergan

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Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion by Junkai Zhang, Bin Li, Shoujun Zhou

“…Experiments on the Rad-Restruct benchmark demonstrate that the HiCA-VQA framework outperforms existing state-of-the-art methods in answering hierarchical fine-grained questions, especially achieving an 18 percent improvement in the F1 score. …”
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Unsupervised Context-Linking Retriever for Question Answering on Long Narrative Books by Mohammad A. Ateeq, Sabrina Tiun, Hamed Abdelhaq, Wandeep Kaur

“…This method optimizes the retriever to identify passages with sufficient context to accurately reconstruct both the question and the answer, improving retrieval accuracy. UCLR also identifies key events surrounding each passage in the retrieved set and constructs a new set of passages from these key events, enabling coverage of both broader narrative structures and finer details. …”
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