Zobrazeno 1 - 10
of 96
pro vyhledávání: '"Huang Weizhe"'
Publikováno v:
Open Medicine, Vol 19, Iss 1, Pp 7-33 (2024)
Advances in lung cancer research applying machine learning (ML) technology have generated many relevant literature. However, there is absence of bibliometric analysis review that aids a comprehensive understanding of this field and its progress. Pres
Externí odkaz:
https://doaj.org/article/6734fdc49ee8414dbcea66c44c152b23
Large Vision-Language Models (LVLMs) represent a significant advancement toward achieving superior multimodal capabilities by enabling powerful Large Language Models (LLMs) to understand visual input. Typically, LVLMs utilize visual encoders, such as
Externí odkaz:
http://arxiv.org/abs/2411.14164
Autor:
Li, Qingchuan, Li, Jiatong, Liu, Tongxuan, Zeng, Yuting, Cheng, Mingyue, Huang, Weizhe, Liu, Qi
Large Language Models (LLMs) have exhibited remarkable potential across a wide array of reasoning tasks, including logical reasoning. Although massive efforts have been made to empower the logical reasoning ability of LLMs via external logical symbol
Externí odkaz:
http://arxiv.org/abs/2410.21779
Autor:
Liu, Tongxuan, Xu, Wenjiang, Huang, Weizhe, Wang, Xingyu, Wang, Jiaxing, Yang, Hailong, Li, Jing
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks but their performance in complex logical reasoning tasks remains unsatisfactory. Although some prompting methods, such as Chain-of-Thought, can improve the re
Externí odkaz:
http://arxiv.org/abs/2409.17539
Autor:
Liu, Tongxuan, Wang, Xingyu, Huang, Weizhe, Xu, Wenjiang, Zeng, Yuting, Jiang, Lei, Yang, Hailong, Li, Jing
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse NLP tasks. Extensive research has explored how to enhance the logical reasoning abilities such as Chain-of-Thought, Chain-of-Thought with Self-Cons
Externí odkaz:
http://arxiv.org/abs/2409.14051
Autor:
Liu, Qi, Zhuang, Yan, Bi, Haoyang, Huang, Zhenya, Huang, Weizhe, Li, Jiatong, Yu, Junhao, Liu, Zirui, Hu, Zirui, Hong, Yuting, Pardos, Zachary A., Ma, Haiping, Zhu, Mengxiao, Wang, Shijin, Chen, Enhong
Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance. Widely adopted across diverse fields like education, healthc
Externí odkaz:
http://arxiv.org/abs/2404.00712
Autor:
Zhuang, Yan, Liu, Qi, Ning, Yuting, Huang, Weizhe, Pardos, Zachary A., Kyllonen, Patrick C., Zu, Jiyun, Mao, Qingyang, Lv, Rui, Huang, Zhenya, Zhao, Guanhao, Zhang, Zheng, Wang, Shijin, Chen, Enhong
As AI systems continue to grow, particularly generative models like Large Language Models (LLMs), their rigorous evaluation is crucial for development and deployment. To determine their adequacy, researchers have developed various large-scale benchma
Externí odkaz:
http://arxiv.org/abs/2306.10512
Publikováno v:
In Sensors and Actuators: A. Physical 16 August 2024 374
Autor:
Huang, Weizhe, Zhang, Yuting, Khan, Taj Wali, Chen, Nuo, Xu, Chunkai, Wang, Enliang, Shan, Xu, Chen, Xiangjun
Publikováno v:
In Radiation Physics and Chemistry April 2025 229
Publikováno v:
In Journal of Constructional Steel Research July 2023 206