Expert Detection In Question Answer Communities by Hamed Salimian, Mohammad Amin Fazli, Jafar Habibi

Subjects: “…community question answering…”
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Systematic review of question answering over knowledge bases by Arnaldo Pereira, Alina Trifan, Rui Pedro Lopes, José Luís Oliveira

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Building a Framework for Visual Question Answering Systems by Maya Abu Hamoud, Wasim Safi

“…VQA (Visual Question Answering) systems are among the latest advancements in the fields of artificial intelligence and deep learning. …”
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Means of Question-Answer Interaction for Collaborative Development Activity by Petr Sosnin

“…In this paper, a number of question-answer means for modeling reasoning are suggested. …”
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Dual-Channel Reasoning Model for Complex Question Answering by Xing Cao, Yun Liu, Bo Hu, Yu Zhang

“…Multihop question answering has attracted extensive studies in recent years because of the emergence of human annotated datasets and associated leaderboards. …”
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Evaluating the effectiveness of prompt engineering for knowledge graph question answering by Catherine Kosten, Farhad Nooralahzadeh, Kurt Stockinger

Subjects: “…knowledge graph question answering…”
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Visual Question Answering in Robotic Surgery: A Comprehensive Review by Di Ding, Tianliang Yao, Rong Luo, Xusen Sun

Subjects: “…Surgical visual question answering…”
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Unveiling the power of language models in chemical research question answering by Xiuying Chen, Tairan Wang, Taicheng Guo, Kehan Guo, Juexiao Zhou, Haoyang Li, Zirui Song, Xin Gao, Xiangliang Zhang

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USING QUESTION ANSWER RELATIONSHIP STRATEGY TO IMPROVE READING COMPREHENSION by Hafizhatun Nadia

“…The strategy proposed in this study is Question Answer Relationship (QAR) Strategy. The research subjects in this study were 36 students.  …”
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QAR (QUESTION ANSWER RELATIONSHIP) AS AN ALTERNATIVE STRATEGY TO TEACH READING by Sa’dulloh Muzammil

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Prompting Large Language Models with Knowledge-Injection for Knowledge-Based Visual Question Answering by Zhongjian Hu, Peng Yang, Fengyuan Liu, Yuan Meng, Xingyu Liu

Subjects: “…visual question answering…”
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Knowledge Graphs as a source of trust for LLM-powered enterprise question answering by Juan Sequeda, Dean Allemang, Bryon Jacob

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Campus question-answering system based on intent recognition and retrieval-augmented generation by TANG Bowen, MA Mingxuan, ZHANG Yining, LI Hourun, WEN Feifan, WANG Dabin, YANG Jia, MA Hao

“…To address the issues of poor information integration and generalization in traditional campus question-answering systems, a campus question-answering system based on a large language model was designed. …”
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Research on campus question answering system supported by retrieval-augmented generation technology by JIA Chunyan, FANG Weijie, XIE Yuwei, LING Zaiying

“…To solve the problem of obtaining effective information from the vast amount of campus information for teachers and students, an intelligent campus question answering (QA) system based on RAG was designed. …”
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Designing and Evaluating a Dual-Stream Transformer-Based Architecture for Visual Question Answering by Faheem Shehzad, Aniello Minutolo, Massimo Esposito

Subjects: “…Visual question answering (VQA)…”
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Answer Distillation Network With Bi-Text-Image Attention for Medical Visual Question Answering by Hongfang Gong, Li Li

Subjects: “…Medical visual question answering…”
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Few-shot cybersecurity event detection method by data augmentation with prompting question answering by TANG Mengmeng, GUO Yuanbo, ZHANG Han, BAI Qingchun, CHEN Qingli, ZHANG Bowen

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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

“…To solve the problem, we regard ADR detection as a question-answer problem and introduces an innovative neural network framework with multiple GRU layers designed for extracting ADR-related information from tweets. …”
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cLegal-QA: a Chinese legal question answering with natural language generation methods by Yizhen Wang, Xueying Shen, Zixian Huang, Lihui Niu, Shiyan Ou

Subjects: “…Legal question answering…”
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Ontology-Driven Mixture-of-Domain Documentation: A Backbone Approach Enabling Question Answering for Additive Construction by Chao Li, Frank Petzold

“…This knowledge graph will interface with advanced large language models (LLMs), enabling effective question answering and knowledge retrieval.…”
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