Zobrazeno 1 - 10
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pro vyhledávání: '"Jin, Yonggang"'
Autor:
Jiang, Jin, Yan, Yuchen, Liu, Yang, Jin, Yonggang, Peng, Shuai, Zhang, Mengdi, Cai, Xunliang, Cao, Yixin, Gao, Liangcai, Tang, Zhi
In this paper, we present a novel approach, called LogicPro, to enhance Large Language Models (LLMs) complex Logical reasoning through Program Examples. We do this effectively by simply utilizing widely available algorithmic problems and their code s
Externí odkaz:
http://arxiv.org/abs/2409.12929
Autor:
Wang, Leyan, Jin, Yonggang, Shen, Tianhao, Zheng, Tianyu, Du, Xinrun, Zhang, Chenchen, Huang, Wenhao, Liu, Jiaheng, Wang, Shi, Zhang, Ge, Xiang, Liuyu, He, Zhaofeng
As large language models (LLMs) continue to develop and gain widespread application, the ability of LLMs to exhibit empathy towards diverse group identities and understand their perspectives is increasingly recognized as critical. Most existing bench
Externí odkaz:
http://arxiv.org/abs/2406.14903
Autor:
Bai, Yuelin, Du, Xinrun, Liang, Yiming, Jin, Yonggang, Liu, Ziqiang, Zhou, Junting, Zheng, Tianyu, Zhang, Xincheng, Ma, Nuo, Wang, Zekun, Yuan, Ruibin, Wu, Haihong, Lin, Hongquan, Huang, Wenhao, Zhang, Jiajun, Chen, Wenhu, Lin, Chenghua, Fu, Jie, Yang, Min, Ni, Shiwen, Zhang, Ge
Recently, there have been significant advancements in large language models (LLMs), particularly focused on the English language. These advancements have enabled these LLMs to understand and execute complex instructions with unprecedented accuracy an
Externí odkaz:
http://arxiv.org/abs/2403.18058
Autor:
Jin, Yonggang, Zhang, Ge, Zhao, Hao, Zheng, Tianyu, Guo, Jarvi, Xiang, Liuyu, Yue, Shawn, Huang, Stephen W., He, Zhaofeng, Fu, Jie
Developing a generalist agent is a longstanding objective in artificial intelligence. Previous efforts utilizing extensive offline datasets from various tasks demonstrate remarkable performance in multitasking scenarios within Reinforcement Learning.
Externí odkaz:
http://arxiv.org/abs/2402.04154
Autor:
Wang, Zihao, Cai, Shaofei, Liu, Anji, Jin, Yonggang, Hou, Jinbing, Zhang, Bowei, Lin, Haowei, He, Zhaofeng, Zheng, Zilong, Yang, Yaodong, Ma, Xiaojian, Liang, Yitao
Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle
Externí odkaz:
http://arxiv.org/abs/2311.05997
Autor:
Jin, Yonggang, Wang, Chenxu, Zheng, Tianyu, Xiang, Liuyu, Yang, Yaodong, Zhang, Junge, Fu, Jie, He, Zhaofeng
Deep reinforcement learning algorithms are usually impeded by sampling inefficiency, heavily depending on multiple interactions with the environment to acquire accurate decision-making capabilities. In contrast, humans rely on their hippocampus to re
Externí odkaz:
http://arxiv.org/abs/2306.10698
Publikováno v:
In Cell Reports Physical Science 17 May 2023 4(5)
Autor:
Zhao, Guangyu, Yan, Penghui, Procter, Kerryn, Adesina, Adesoji, Jin, Yonggang, Kennedy, Eric, Stockenhuber, Michael
Publikováno v:
In Journal of Catalysis January 2023 417:140-152
Publikováno v:
In Fuel Processing Technology January 2023 239
Akademický článek
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