The effect of pending feedback on the IELTS speaking test result: grammatical range and accuracy in focus by Zahra Zargaran

“…It was found experimental group demonstrated an improvement in IELTS speaking scores, indicating a strong correlation between pending feedback and better speaking performance. …”
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Is Quality of Life Better in Hemophilia Patients with or Without Prophylactic Treatment? Using Haemo-SYM to Answer the Question by Catalina GURAN, Mieta-Gabriela HATEGAN, Dacian Nicolae OROS, Despina CALAMAR-POPOVICI, Marioara IONITA HORTENSIA, Claudiu IONITA, Florica GHILEZAN, Ioana IONITA

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PARP Inhibitors for Metastatic CRPC: More Answers than Questions, a Systematic Review and Meta-Analysis by Ray Manneh, Javier Molina-Cerrillo, Guillermo de Velasco, Linda Ibatá, Susan Martínez, Álvaro Ruiz-Granados, Teresa Alonso-Gordoa

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Question-answering enhancement method for large educational models based on re-ranking and post-retrieval reflection by SUN Haoran, WANG Zhihao, WU Yifan, GAO Xiaoying, XIANG Yang

“…This approach significantly improves the accuracy of large language models in computer question-answering tasks. …”
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Evaluating the accuracy of CHATGPT models in answering multiple-choice questions on oral and maxillofacial pathologies and oral radiology by Doaa Felemban, Ahoud Jazzar, Yasmin Mair, Maha Alsharif, Alla Alsharif, Saba Kassim

“…Only 98 questions (72%) were correctly answered by the three models. Ten months later, the unpaid ChatGPT version showed a significant improvement in accuracy, while the paid versions maintained consistent performance over time with no significant differences. …”
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Question Answering Enhancement Method for Large Educational Models Based on Re-ranking and Post-retrieval Reflection by SUN Haoran, WANG Zhihao, WU Yifan, XIANG Yang

“…This approach significantly improves the accuracy of large language models in computer question-answering tasks. …”
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Detecting changes in retinal function: Analysis with Non-Stationary Weibull Error Regression and Spatial enhancement (ANSWERS). by Haogang Zhu, Richard A Russell, Luke J Saunders, Stefano Ceccon, David F Garway-Heath, David P Crabb

“…Furthermore, the spatial correlation utilised in ANSWERS was shown to improve the ability to detect deterioration, compared to equivalent models without spatial correlation, especially in short follow-up series. …”
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Is Self-Mark dependable in Very Short Answer Question formats among pre-clinical medical students? by Sethapong Lertsakulbunlue, Anupong Kantiwong

“…Introduction: Very Short Answer Questions (VSAQs) minimise cueing and simulate actual clinical practice more accurately than Single Best Answer Questions, as multiple-choice options might not be realistic. …”
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A lightweight knowledge graph-driven question answering system for field-based mineral resource survey by Mingguo Wang, Chengbin Wang, Jianguo Chen, Bo Wang, Wei Wang, Xiaogang Ma, Jiangtao Ren, Zichen Li, Yicai Ye, Jiakai Zhang, Yue Wang

“…The results also suggest that further studies on geoscience pre-trained models, an informative library of question templates, and multimodal knowledge graphs are necessary to improve the performance of the knowledge graph-driven question-answering system.…”
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A brain-inspired memory transformation based differentiable neural computer for reasoning-based question answering by Yao Liang, Yao Liang, Yuwei Wang, Yuwei Wang, Hongjian Fang, Hongjian Fang, Feifei Zhao, Yi Zeng, Yi Zeng, Yi Zeng, Yi Zeng, Yi Zeng

“…Reasoning and question answering, as fundamental cognitive functions in humans, remain significant hurdles for artificial intelligence. …”
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Assessing the accuracy and readability of ChatGPT-4 and Gemini in answering oral cancer queries—an exploratory study by Márcio Diniz-Freitas, Rosa María López-Pintor, Alan Roger Santos-Silva, Saman Warnakulasuriya, Pedro Diz-Dios

“…Conclusions: Gemini provides more complete and accurate responses to questions about oral cancer that lay people may seek answers to compared to ChatGPT-4, although its responses were less readable. …”
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DRKG: Faithful and Interpretable Multi-Hop Knowledge Graph Question Answering via LLM-Guided Reasoning Plans by Yan Chen, Shuai Sun, Xiaochun Hu

“…Traditional embedding-based methods map natural language questions and knowledge graphs into vector spaces for answer matching through vector operations. While these approaches have improved model performance, they face two critical challenges: the lack of clear interpretability caused by implicit reasoning mechanisms, and the semantic gap between natural language queries and structured knowledge representations. …”
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Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis by Ling Wang, Jinglin Li, Boyang Zhuang, Shasha Huang, Meilin Fang, Cunze Wang, Wen Li, Mohan Zhang, Shurong Gong

“…Studies on the accuracy of LLMs when answering clinical research questions were included and screened by reading published reports. …”
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Intelligent question answering for water conservancy project inspection driven by knowledge graph and large language model collaboration by Yangrui Yang, Sisi Chen, Yaping Zhu, Xuemei Liu, Shifeng Pan, Xin Wang

“…The results affirm the method’s effectiveness in improving the accuracy of intelligent question-answering in hydroengineering inspection, with potential implications for similar applications in other domains of hydraulic engineering.…”
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An Image Grid Can Be Worth a Video: Zero-Shot Video Question Answering Using a VLM by Wonkyun Kim, Changin Choi, Wonseok Lee, Wonjong Rhee

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Answering real-world clinical questions using large language model, retrieval-augmented generation, and agentic systems by Yen Sia Low, Michael L Jackson, Rebecca J Hyde, Robert E Brown, Neil M Sanghavi, Julian D Baldwin, C William Pike, Jananee Muralidharan, Gavin Hui, Natasha Alexander, Hadeel Hassan, Rahul V Nene, Morgan Pike, Courtney J Pokrzywa, Shivam Vedak, Adam Paul Yan, Dong-han Yao, Amy R Zipursky, Christina Dinh, Philip Ballentine, Dan C Derieg, Vladimir Polony, Rehan N Chawdry, Jordan Davies, Brigham B Hyde, Nigam H Shah, Saurabh Gombar

“…Results General-purpose LLMs rarely produced relevant, evidence-based answers (2–10% of questions). In contrast, RAG-based and agentic LLM systems, respectively, produced relevant, evidence-based answers for 24% (OpenEvidence) to 58% (ChatRWD) of questions. …”
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Evaluating large language models as graders of medical short answer questions: a comparative analysis with expert human graders by Olena Bolgova, Paul Ganguly, Muhammad Faisal Ikram, Volodymyr Mavrych

“…The assessment of short-answer questions (SAQs) in medical education is resource-intensive, requiring significant expert time. …”
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Interactive instructional teaching method (IITM); contribution towards students’ ability in answering unfamiliar types questions of buffer solution by Habiddin Habiddin, Ulfa Rafika, Utomo Yudhi

“…Students’ adversity quotient correlated positively to their ability to answer unfamiliar questions of buffer solutions. Meanwhile, the effect of students’ learning interests and adversity quotient on students’ ability to answer unfamiliar questions was found uncorrelated.…”
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An elusive causal link between epilepsy and vitamin D in children: More questions than answers? A narrative review by Hong-Li Guo, Xi-Li Sun, Yang-Yang Xu, Yuan-Yuan Zhang, Jie Wang, Lin Fan, Ya-Hui Hu, Fang Sun, Heng Tang, Xiao-Peng Lu, Jin Xu, Feng Chen

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TCMLCM: an intelligent question-answering model for traditional Chinese medicine lung cancer based on the KG2TRAG method by Zhou Chunfang, Gong Qingyue, Zhan Wendong, Zhu Jinyang, Luan Huidan

“…Objective: To improve the accuracy and professionalism of question-answering (QA) model in traditional Chinese medicine (TCM) lung cancer by integrating large language models with structured knowledge graphs using the knowledge graph (KG) to text-enhanced retrieval-augmented generation (KG2TRAG) method. …”
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