TQAgent: Enhancing Table-Based Question Answering with Knowledge Graphs and Tree-Structured Reasoning by Jianbin Zhao, Pengfei Zhang, Yuzhen Wang, Rui Xin, Xiuyuan Lu, Ripeng Li, Shuai Lyu, Zhonghong Ou, Meina Song

“…Table-based question answering (TableQA) has emerged as an important task in natural language processing, yet existing models face challenges in handling complex reasoning and mitigating hallucinations, especially when dealing with diverse table structures. …”
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Japanese Short Answer Grading for Japanese Language Learners Using the Contextual Representation of BERT by Dyah Lalita Luhurkinanti, Prima Dewi Purnamasari, Takashi Tsunakawa, Anak Agung Putri Ratna

“…The automatization of grading short answers in examinations aims to help teachers grade more efficiently and fairly. …”
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A Dataset of Medical Questions Paired with Automatically Generated Answers and Evidence-supported References by Deepak Gupta, Davis Bartels, Dina Demner-Fushman

“…Abstract New Large Language Models (LLM)-based approaches to medical Question Answering show unprecedented improvements in the fluency, grammaticality, and other qualities of the generated answers. …”
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Analyzing the Accuracy of Answer Sheet Data in Paper-based Test Using Decision Tree by Edy Suharto, Aris Puji Widodo, Suryono Suryono

“…In this study, a method was proposed in order to analyze the accuracy of answer sheet filling out in paper-based test using data mining technique. …”
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Intelligent accounting question-answering robot based on a large language model and knowledge graph by Shi Shengyun, Li Guoxi, Wang Yong

“…To build a complete knowledge graph, this study uses the attention mechanism and convolutional neural network to build a connection prediction model and completes the accounting question-answering knowledge graph. After that, the bidirectional gated loop unit is used to improve the large language model so as to further improve the correlation between knowledge and explore potential information. …”
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Bridging the Question–Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddings by Domen Vake, Jernej Vicic, Aleksandar Tosic

“…., Hypothetical Document Embeddings (HyDE)) that attempt to improve alignment but introduce extra computational overhead at query time. …”
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Volume Kinetic Analysis in Living Humans: Background History and Answers to 15 Questions in Physiology and Medicine by Robert G. Hahn

“…The approach has primarily been used to improve the planning of fluid therapy during surgery but is also useful for answering physiological questions. …”
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Benchmarking the Confidence of Large Language Models in Answering Clinical Questions: Cross-Sectional Evaluation Study by Mahmud Omar, Reem Agbareia, Benjamin S Glicksberg, Girish N Nadkarni, Eyal Klang

“…Models were prompted to provide answers and to also provide their confidence for the correct answers (score: range 0%‐100%). …”
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ChatGPT performance in answering medical residency questions in nephrology: a pilot study in Brazil by Helvécio Neves Feitosa Filho, João Filipe Cavalcante Uchoa Furtado, Eduardo Correia Eulálio, Pedro Vianna Caldas Ribeiro, Lucas Macêdo Aurélio Paiva, Matheus Maia Gonçalves Bringel Correia, Geraldo Bezerra da Silva Júnior

“…Abstract Objective: This study evaluated the performance of ChatGPT 4 and 3.5 versions in answering nephrology questions from medical residency exams in Brazil. …”
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Large Language Model Synergy for Ensemble Learning in Medical Question Answering: Design and Evaluation Study by Han Yang, Mingchen Li, Huixue Zhou, Yongkang Xiao, Qian Fang, Shuang Zhou, Rui Zhang

“… Abstract BackgroundLarge language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, including medical question-answering (QA). However, individual LLMs often exhibit varying performance across different medical QA datasets. …”
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Toward Generating Quality Test Questions and Answers Using Quantized Low-Rank Adapters in LLMs by Jebum Choi, Seongjun Hong, Seoyoon Hong, Jiyeon Park, Eun-Sung Jung

“…Compared to a non-fine-tuned LLaMA-3-8B-Instruct model, our question generation model demonstrated a 41.5% improvement, and the answer generation model achieved a 16.1% improvement. …”
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A topic clustering approach to finding similar questions from large question and answer archives. by Wei-Nan Zhang, Ting Liu, Yang Yang, Liujuan Cao, Yu Zhang, Rongrong Ji

“…With the blooming of Web 2.0, Community Question Answering (CQA) services such as Yahoo! Answers (http://answers.yahoo.com), WikiAnswer (http://wiki.answers.com), and Baidu Zhidao (http://zhidao.baidu.com), etc., have emerged as alternatives for knowledge and information acquisition. …”
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Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring With Feedback by Menna Fateen, Bo Wang, Tsunenori Mine

“…Automatic short answer scoring (ASAS) helps reduce the grading burden on educators but often lacks detailed, explainable feedback. …”
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Is the Smart cities of hybrid model of local government - The type III cities: Four possible answers by Valerii LOGVINOV, Natalia LEBID

“…Raising the questions and searching for the answers will serve as a guide to some researchers and the search for other possible answers, encourage scientific research by young researchers, students. …”
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DEANE: Context-Aware Dual-Craft Graph Contrastive Learning for Enhanced Extractive Question Answering by Dongfen Ye, Jianqiang Zhou, Gang Huang

“…Abstract Extractive Question Answering (EQA) involves extracting accurate answer spans from a background passage in response to a given question. …”
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Exploiting question-answer framework with multi-GRU to detect adverse drug reaction on social media by Jiao-huang Luo, Ai-hua Yang

“…As a result of the training process, word sequences are mapped to a low-latitude vector space, generating corresponding answers. Experimental results obtained from two Twitter ADR datasets affirm that our Question-Answer Mechanism, leveraging multi-GRU architecture, significantly improves the accuracy of ADR detection in tweets. …”
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Hajj-FQA: A benchmark Arabic dataset for developing question-answering systems on Hajj fatwas by Hayfa A. Aleid, Aqil M. Azmi

“…Abstract Deep learning has significantly advanced the question-answering (QA) systems across various sectors. However, Arabic-language systems for Hajj-related fatwas (non-binding Islamic legal opinions issued by muftis) remain underdeveloped. …”
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A Few‐Shot Learning Approach for a Multilingual Agro‐Information Question Answering System by Fiskani Ella Banda, Vukosi Marivate, Joyce Nakatumba‐Nabende

“…One solution that can effectively bridge the support gap for farmers in the local community is a question–answer system based on agricultural expertise and agro‐information. …”
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FGB-OPRAm: integrating fuzzy granular-ball and OPRAm for spatial query answering in uncertain environments by Bongjae Kwon, Kiyun Yu

“…This paper addresses the critical problem of spatial query answering in uncertain environments, where traditional methods relying on crisp boundaries often fail to capture the inherent imprecision of spatial data. …”
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Evaluation of Large Language Model Performance in Answering Clinical Questions on Periodontal Furcation Defect Management by Georgios S. Chatzopoulos, Vasiliki P. Koidou, Lazaros Tsalikis, Eleftherios G. Kaklamanos

“…<b>Background/Objectives</b>: Large Language Models (LLMs) are artificial intelligence (AI) systems with the capacity to process vast amounts of text and generate human-like language, offering the potential for improved information retrieval in healthcare. This study aimed to assess and compare the evidence-based potential of answers provided by four LLMs to common clinical questions concerning the management and treatment of periodontal furcation defects. …”
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