From Templates to Natural Language: Generalization Challenges in Instruction-Tuned LLMs for Spatial Reasoning
Chalamalasetti Kranti, Sherzod Hakimov, David Schlangen
机构
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Computational Linguistics, Department of Linguistics University of Potsdam(乌特雷赫特大学语言学系计算语言学部)
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German Research Center for Artificial Intelligence (DFKI), Berlin(德国人工智能研究中心(DFKI)柏林)
机构
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Department of Mechanical Engineering(机械工程系)
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Rajshahi University of Engineering and Technology(拉贾沙希工程与技术大学)
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Department of Industrial and Production Engineering(工业与生产工程系)
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Shahjalal University of Science and Technology(沙赫jalal科学与技术大学)
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Bangladesh University of Engineering and Technology(孟加拉工程与技术大学)
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Department of Urban and Regional Planning(城市与区域规划系)
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Department of Computer Science Engineering(计算机科学与工程系)
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United International University(联合国际大学)
Modeling and Visualization Reasoning for Stakeholders in Education and Industry Integration Systems: Research on Structured Synthetic Dialogue Data Generation Based on NIST Standards
Wei Meng
专题命中
视觉空间推理
:reasoning(title,comments)
CommentsThis paper presents an innovative and rigorous framework for stakeholder modelling in education-industry integration, combining NIST-compliant synthetic data generation with interpretable visual reasoning
机构
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School of Computer Science and Technology, East China Normal University(东华大学计算机科学与技术学院)
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Bosch Corporate Research(博世企业研究)
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King Abdullah University of Science and Technology(卡布斯大学)
机构
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Department of Electrical Engineering and Computer Science, York University(约克大学电气工程与计算机科学系)
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Samsung Research America(三星美国研究院)
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Department of Software and IT Engineering, École de technologie supérieure (ÉTS), University of Quebec(魁北克大学软件与信息技术工程系,École de technologie supérieure)
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Non-Terrestrial Networks (Carleton-NTN) Lab and the Department of Systems and Computer Engineering, Carleton University(非地面网络(Carleton-NTN)实验室和系统与计算机工程系,卡尔顿大学)
CommentsThe paper is currently under investigation regarding concerns of potential academic misconduct. While the investigation is ongoing, the authors have voluntarily requested to withdraw the manuscript
LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarios
Mingxing Peng, Yuting Xie, Xusen Guo, Ruoyu Yao, Hai Yang, Jun Ma
机构
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The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学(广州))
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School of Computer Science and Engineering, Sun Yat-sen University(中山大学计算机科学与工程学院)