Zobrazeno 1 - 10
of 1 569
pro vyhledávání: '"Zhao, Chenyang"'
Autor:
Chen, Weize, You, Ziming, Li, Ran, Guan, Yitong, Qian, Chen, Zhao, Chenyang, Yang, Cheng, Xie, Ruobing, Liu, Zhiyuan, Sun, Maosong
The rapid advancement of large language models (LLMs) has paved the way for the development of highly capable autonomous agents. However, existing multi-agent frameworks often struggle with integrating diverse capable third-party agents due to relian
Externí odkaz:
http://arxiv.org/abs/2407.07061
Autor:
Hu, Shengding, Tu, Yuge, Han, Xu, He, Chaoqun, Cui, Ganqu, Long, Xiang, Zheng, Zhi, Fang, Yewei, Huang, Yuxiang, Zhao, Weilin, Zhang, Xinrong, Thai, Zheng Leng, Zhang, Kaihuo, Wang, Chongyi, Yao, Yuan, Zhao, Chenyang, Zhou, Jie, Cai, Jie, Zhai, Zhongwu, Ding, Ning, Jia, Chao, Zeng, Guoyang, Li, Dahai, Liu, Zhiyuan, Sun, Maosong
The burgeoning interest in developing Large Language Models (LLMs) with up to trillion parameters has been met with concerns regarding resource efficiency and practical expense, particularly given the immense cost of experimentation. This scenario un
Externí odkaz:
http://arxiv.org/abs/2404.06395
Autor:
Chen, Xiaoqing, Zhang, Yanyan, Ji, Yingke, Zhang, Yu, Wang, Jianguo, Wu, Xianghu, Zhao, Chenyang, Fang, Liang, Jiang, Biqiang, Zhao, Jianlin, Gan, Xuetao
We demonstrate the post-induction of high-quality microcavity on silicon photonic crystal (PC) waveguide by integrating few-layer GaSe crystal, which promises highly efficient on-chip optical frequency conversions. The integration of GaSe shifts the
Externí odkaz:
http://arxiv.org/abs/2403.01434
Punishment is a common tactic to sustain cooperation and has been extensively studied for a long time. While most of previous game-theoretic work adopt the imitation learning where players imitate the strategies who are better off, the learning logic
Externí odkaz:
http://arxiv.org/abs/2401.16073
Autor:
Luo, Yin, Kong, Qingchao, Xu, Nan, Cao, Jia, Hao, Bao, Qu, Baoyu, Chen, Bo, Zhu, Chao, Zhao, Chenyang, Zhang, Donglei, Feng, Fan, Zhao, Feifei, Sun, Hailong, Yang, Hanxuan, Pan, Haojun, Liu, Hongyu, Guo, Jianbin, Du, Jiangtao, Wang, Jingyi, Li, Junfeng, Sun, Lei, Liu, Liduo, Dong, Lifeng, Liu, Lili, Wang, Lin, Zhang, Liwen, Wang, Minzheng, Wang, Pin, Yu, Ping, Li, Qingxiao, Yan, Rui, Zou, Rui, Li, Ruiqun, Huang, Taiwen, Wang, Xiaodong, Wu, Xiaofei, Peng, Xin, Zhang, Xina, Fang, Xing, Xiao, Xinglin, Hao, Yanni, Dong, Yao, Wang, Yigang, Liu, Ying, Jiang, Yongyu, Wang, Yungan, Wang, Yuqi, Wang, Zhangsheng, Yu, Zhaoxin, Luo, Zhen, Mao, Wenji, Wang, Lei, Zeng, Dajun
As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artific
Externí odkaz:
http://arxiv.org/abs/2312.14862
Recent studies have uncovered the potential of Large Language Models (LLMs) in addressing complex sequential decision-making tasks through the provision of high-level instructions. However, LLM-based agents lack specialization in tackling specific ta
Externí odkaz:
http://arxiv.org/abs/2311.13373
Autor:
He, Nan, Lai, Hanyu, Zhao, Chenyang, Cheng, Zirui, Pan, Junting, Qin, Ruoyu, Lu, Ruofan, Lu, Rui, Zhang, Yunchen, Zhao, Gangming, Hou, Zhaohui, Huang, Zhiyuan, Lu, Shaoqing, Liang, Ding, Zhan, Mingjie
Large Language Models (LLMs) exhibit impressive reasoning and data augmentation capabilities in various NLP tasks. However, what about small models? In this work, we propose TeacherLM-7.1B, capable of annotating relevant fundamentals, chain of though
Externí odkaz:
http://arxiv.org/abs/2310.19019
Large language models (LLMs) enable system builders today to create competent NLP systems through prompting, where they only need to describe the task in natural language and provide a few examples. However, in other ways, LLMs are a step backward fr
Externí odkaz:
http://arxiv.org/abs/2308.12261
Large language models (LLMs) encode a vast amount of world knowledge acquired from massive text datasets. Recent studies have demonstrated that LLMs can assist an embodied agent in solving complex sequential decision making tasks by providing high-le
Externí odkaz:
http://arxiv.org/abs/2306.03604
Policy learning (PL) is a module of a task-oriented dialogue system that trains an agent to make actions in each dialogue turn. Imitating human action is a fundamental problem of PL. However, both supervised learning (SL) and reinforcement learning (
Externí odkaz:
http://arxiv.org/abs/2305.03987