Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Tao, Zhengwei"'
Autor:
Tao, Zhengwei, Jin, Zhi, Zhang, Yifan, Chen, Xiancai, Zhao, Haiyan, Li, Jia, Liang, Bing, Tao, Chongyang, Liu, Qun, Wong, Kam-Fai
Event reasoning is a fundamental ability that underlies many applications. It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of the inter-event relations and the reasoning paradigms. How well LLMs acc
Externí odkaz:
http://arxiv.org/abs/2404.17513
Autor:
Tao, Zhengwei, Lin, Ting-En, Chen, Xiancai, Li, Hangyu, Wu, Yuchuan, Li, Yongbin, Jin, Zhi, Huang, Fei, Tao, Dacheng, Zhou, Jingren
Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications. However, current LLMs that learn from human or external model supervision are costly and may face performance ceilings as task complexity a
Externí odkaz:
http://arxiv.org/abs/2404.14387
Events refer to specific occurrences, incidents, or happenings that take place under a particular background. Event reasoning aims to infer events according to certain relations and predict future events. The cutting-edge techniques for event reasoni
Externí odkaz:
http://arxiv.org/abs/2404.11978
Autor:
Tao, Zhengwei, Jin, Zhi, Huang, Junqiang, Chen, Xiancai, Bai, Xiaoying, Zhao, Haiyan, Zhang, Yifan, Tao, Chongyang
Multi-modal Event Reasoning (MMER) endeavors to endow machines with the ability to comprehend intricate event relations across diverse data modalities. MMER is fundamental and underlies a wide broad of applications. Despite extensive instruction fine
Externí odkaz:
http://arxiv.org/abs/2404.10429
The integration of Large Language Models (LLMs) and knowledge graphs (KGs) has achieved remarkable success in various natural language processing tasks. However, existing methodologies that integrate LLMs and KGs often navigate the task-solving proce
Externí odkaz:
http://arxiv.org/abs/2404.07677
Autor:
Zhao, Yongqiang, Li, Zhenyu, Jin, Zhi, Zhang, Feng, Zhao, Haiyan, Dou, Chengfeng, Tao, Zhengwei, Xu, Xinhai, Liu, Donghong
The Multi-Modal Large Language Model (MLLM) refers to an extension of the Large Language Model (LLM) equipped with the capability to receive and infer multi-modal data. Spatial awareness stands as one of the crucial abilities of MLLM, encompassing di
Externí odkaz:
http://arxiv.org/abs/2310.20357
Events serve as fundamental units of occurrence within various contexts. The processing of event semantics in textual information forms the basis of numerous natural language processing (NLP) applications. Recent studies have begun leveraging large l
Externí odkaz:
http://arxiv.org/abs/2305.15268
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
In the Internet era, oil painting creation requires constant observation and learning before it can be inspired, and the inspiration for oil painting creation is like a godsend. This paper constructs a fast Fourier transform convolutional model based
Externí odkaz:
https://doaj.org/article/1fdeff9b45d84cc09afe8d5073883106
Autor:
Zhao Junchao, Tao Zhengwei
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
To understand the relationship between art teaching strategies and innovative development in preschool children. In this paper, using a sample-control research design, 2 middle classes of a kindergarten were selected as experimental and control class
Externí odkaz:
https://doaj.org/article/7af6b09209254edd94b41fc01cd01cfd