Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Zu, Can"'
Autor:
Zhao, Jun, Zu, Can, Xu, Hao, Lu, Yi, He, Wei, Ding, Yiwen, Gui, Tao, Zhang, Qi, Huang, Xuanjing
Large language models (LLMs) have demonstrated impressive performance in understanding language and executing complex reasoning tasks. However, LLMs with long context windows have been notorious for their expensive training costs and high inference l
Externí odkaz:
http://arxiv.org/abs/2402.11550
Autor:
Xu, Nuo, Zhao, Jun, Zu, Can, Li, Sixian, Chen, Lu, Zhang, Zhihao, Zheng, Rui, Dou, Shihan, Qin, Wenjuan, Gui, Tao, Zhang, Qi, Huang, Xuanjing
Faithfulness, expressiveness, and elegance is the constant pursuit in machine translation. However, traditional metrics like \textit{BLEU} do not strictly align with human preference of translation quality. In this paper, we explore leveraging reinfo
Externí odkaz:
http://arxiv.org/abs/2402.11525
Autor:
Wang, Xiao, Zhou, Weikang, Zu, Can, Xia, Han, Chen, Tianze, Zhang, Yuansen, Zheng, Rui, Ye, Junjie, Zhang, Qi, Gui, Tao, Kang, Jihua, Yang, Jingsheng, Li, Siyuan, Du, Chunsai
Large language models have unlocked strong multi-task capabilities from reading instructive prompts. However, recent studies have shown that existing large models still have difficulty with information extraction tasks. For example, gpt-3.5-turbo ach
Externí odkaz:
http://arxiv.org/abs/2304.08085
Autor:
Ye, Junjie, Chen, Xuanting, Xu, Nuo, Zu, Can, Shao, Zekai, Liu, Shichun, Cui, Yuhan, Zhou, Zeyang, Gong, Chao, Shen, Yang, Zhou, Jie, Chen, Siming, Gui, Tao, Zhang, Qi, Huang, Xuanjing
GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities. However, despite the abundance of research on the difference in capabiliti
Externí odkaz:
http://arxiv.org/abs/2303.10420
Autor:
Chen, Xuanting, Ye, Junjie, Zu, Can, Xu, Nuo, Zheng, Rui, Peng, Minlong, Zhou, Jie, Gui, Tao, Zhang, Qi, Huang, Xuanjing
The GPT-3.5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks, showcasing their strong understanding and reasoning capabilities. However, their robustness and abilities to handle various complexities o
Externí odkaz:
http://arxiv.org/abs/2303.00293
Akademický článek
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Autor:
Xu H; The Second Department of Orthopaedics, First People's Hospital of Xiaoshan, Hangzhou, Zhejian, China. xxuoo365@yahoo.com.cn, Bi DW, Ma HT, Wang H, Chen YM, Yang YS, Zu C
Publikováno v:
Zhongguo gu shang = China journal of orthopaedics and traumatology [Zhongguo Gu Shang] 2013 Apr; Vol. 26 (4), pp. 344-6.