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检索条件"机构=Computer Architecture and Languages Laboratory Institute of Computer Science"
736 条 记 录,以下是11-20 订阅
排序:
PDF-to-Tree: Parsing PDF Text Blocks into a Tree
PDF-to-Tree: Parsing PDF Text Blocks into a Tree
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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024
作者: Zhang, Yue Zhang, Zhihao Lai, Wenbin Zhang, Chong Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China
In many PDF documents, the reading order of text blocks is missing, which can hinder machine understanding of the document's content. Existing works try to extract one universal reading order for a PDF file. Howev... 详细信息
来源: 评论
Improving Discriminative Capability of Reward Models in RLHF Using Contrastive Learning
Improving Discriminative Capability of Reward Models in RLHF...
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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024
作者: Chen, Lu Zheng, Rui Wang, Binghai Jin, Senjie Huang, Caishuang Ye, Junjie Zhang, Zhihao Zhou, Yuhao Xi, Zhiheng Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Key Laboratory of Intelligent Information Processing Fudan University Shanghai China
Reinforcement Learning from Human Feedback (RLHF) is a crucial approach to aligning language models with human values and intentions. A fundamental challenge in this method lies in ensuring that the reward model accur... 详细信息
来源: 评论
LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration
LONGAGENT: Achieving Question Answering for 128k-Token-Long ...
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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024
作者: Zhao, Jun Zu, Can Xu, Hao Lu, Yi He, Wei Ding, Yiwen Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Institute of Modern Languages and Linguistics Fudan University China
Large language models (LLMs) have achieved tremendous success in understanding language and processing text. However, question-answering (QA) on lengthy documents faces challenges of resource constraints and a high pr... 详细信息
来源: 评论
ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios  31
ToolEyes: Fine-Grained Evaluation for Tool Learning Capabili...
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31st International Conference on Computational Linguistics, COLING 2025
作者: Ye, Junjie Li, Guanyu Gao, Songyang Huang, Caishuang Wu, Yilong Li, Sixian Fan, Xiaoran Dou, Shihan Ji, Tao Zhang, Qi Gui, Tao Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Research Institute of Intelligent Complex Systems Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China Pengcheng Laboratory China
Existing evaluations of tool learning primarily focus on validating the alignment of selected tools (e.g., various APIs) for large language models (LLMs) with expected outcomes. However, these approaches rely on a lim... 详细信息
来源: 评论
LONGHEADS: Multi-Head Attention is Secretly a Long Context Processor
LONGHEADS: Multi-Head Attention is Secretly a Long Context P...
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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024
作者: Lu, Yi Zhou, Xin He, Wei Zhao, Jun Ji, Tao Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China Key Laboratory of Intelligent Information Processing Fudan University Shanghai China
Large language models (LLMs) have achieved impressive performance in numerous domains but often struggle to process lengthy inputs effectively and efficiently due to limited length generalization and attention's q... 详细信息
来源: 评论
FlexPDA:A Flexible Programming Framework for Deep Learning Accelerators
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Journal of computer science & Technology 2022年 第5期37卷 1200-1220页
作者: Lei Liu Xiu Ma Hua-xiao Liu Guang-li Li Lei Liu College of Computer Science and Technology Jilin UniversityChangchunChina Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina
There are a wide variety of intelligence accelerators with promising performance and energy efficiency,deployed in a broad range of applications such as computer vision and speech ***,programming productivity hinders ... 详细信息
来源: 评论
NfvInsight:A Framework for Automatically Deploying and Benchmarking VNF Chains
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Journal of computer science & Technology 2022年 第3期37卷 680-698页
作者: Tian-Ni Xu Hai-Feng Sun Di Zhang Xiao-Ming Zhou Xiu-Feng Sui Sa Wang Qun Huang Yun-Gang Bao State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China Peng Cheng Laboratory Shenzhen 518055China Department of Computer Science and Technology Peking UniversityBeijing 100871China
With the advent of virtualization techniques and software-defined networking(SDN),network function virtualization(NFV)shifts network functions(NFs)from hardware implementations to software appliances,between which exi... 详细信息
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Fast and efficient parallel breadth-first search with power-law graph transformation
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Frontiers of computer science 2022年 第5期16卷 225-227页
作者: Zite JIANG Tao LIU Shuai ZHANG Mengting YUAN Haihang YOU School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Comptuer Science Wuhan UniversityWuhan 430072China
1 Introduction Most real-world graphs are large-scale but unstructured and *** of the most notable characteristics of real-world graphs is the skewed power law degree distribution[1]:most vertices have a few neighbors... 详细信息
来源: 评论
Tetris:A Heuristic Static Memory Management Framework for Uniform Memory Multicore Neural Network Accelerators
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Journal of computer science & Technology 2022年 第6期37卷 1255-1270页
作者: Xiao-Bing Chen Hao Qi Shao-Hui Peng Yi-Min Zhuang Tian Zhi Yun-Ji Chen Distinguished Member,CCF State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Technology University of Science and Technology of ChinaHeFei230026China Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Shanghai200031China 不详
Uniform memory multicore neural network accelerators(UNNAs)furnish huge computing power to emerging neural network ***,with neural network architectures going deeper and wider,the limited memory capacity has become a ... 详细信息
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Sampling Methods for Efficient Training of Graph Convolutional Networks:A Survey
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IEEE/CAA Journal of Automatica Sinica 2022年 第2期9卷 205-234页
作者: Xin Liu Mingyu Yan Lei Deng Guoqi Li Xiaochun Ye Dongrui Fan State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100086 School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049 IEEE Department of Precision Instrument Center for Brain Inspired Computing ResearchTsinghua UniversityBeijing 100084China
Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph *** GCN performs well compared with other methods,it still faces **... 详细信息
来源: 评论