Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Dong, Zican"'
Autor:
Tang, Tianyi, Hu, Yiwen, Li, Bingqian, Luo, Wenyang, Qin, Zijing, Sun, Haoxiang, Wang, Jiapeng, Xu, Shiyi, Cheng, Xiaoxue, Guo, Geyang, Peng, Han, Zheng, Bowen, Tang, Yiru, Min, Yingqian, Chen, Yushuo, Chen, Jie, Zhao, Yuanqian, Ding, Luran, Wang, Yuhao, Dong, Zican, Xia, Chunxuan, Li, Junyi, Zhou, Kun, Zhao, Wayne Xin, Wen, Ji-Rong
To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. This library is featured with three main merits: (1) a unified data int
Externí odkaz:
http://arxiv.org/abs/2407.05563
Autor:
Zhu, Yutao, Zhou, Kun, Mao, Kelong, Chen, Wentong, Sun, Yiding, Chen, Zhipeng, Cao, Qian, Wu, Yihan, Chen, Yushuo, Wang, Feng, Zhang, Lei, Li, Junyi, Wang, Xiaolei, Wang, Lei, Zhang, Beichen, Dong, Zican, Cheng, Xiaoxue, Chen, Yuhan, Tang, Xinyu, Hou, Yupeng, Ren, Qiangqiang, Pang, Xincheng, Xie, Shufang, Zhao, Wayne Xin, Dou, Zhicheng, Mao, Jiaxin, Lin, Yankai, Song, Ruihua, Xu, Jun, Chen, Xu, Yan, Rui, Wei, Zhewei, Hu, Di, Huang, Wenbing, Gao, Ze-Feng, Chen, Yueguo, Lu, Weizheng, Wen, Ji-Rong
Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many open-source LLMs have been released with technical reports, the lack of
Externí odkaz:
http://arxiv.org/abs/2406.19853
Autor:
Dong, Zican, Li, Junyi, Men, Xin, Zhao, Wayne Xin, Wang, Bingbing, Tian, Zhen, Chen, Weipeng, Wen, Ji-Rong
Transformer-based large language models (LLMs) typically have a limited context window, resulting in significant performance degradation when processing text beyond the length of the context window. Extensive studies have been proposed to extend the
Externí odkaz:
http://arxiv.org/abs/2405.18009
Large language models (LLMs) have achieved dramatic proficiency over NLP tasks with normal length. Recently, multiple studies have committed to extending the context length and enhancing the long text modeling capabilities of LLMs. To comprehensively
Externí odkaz:
http://arxiv.org/abs/2309.13345
In this paper, we study how to improve the zero-shot reasoning ability of large language models~(LLMs) over structured data in a unified way. Inspired by the study on tool augmentation for LLMs, we develop an \emph{Iterative Reading-then-Reasoning~(I
Externí odkaz:
http://arxiv.org/abs/2305.09645
Autor:
Zhao, Wayne Xin, Zhou, Kun, Li, Junyi, Tang, Tianyi, Wang, Xiaolei, Hou, Yupeng, Min, Yingqian, Zhang, Beichen, Zhang, Junjie, Dong, Zican, Du, Yifan, Yang, Chen, Chen, Yushuo, Chen, Zhipeng, Jiang, Jinhao, Ren, Ruiyang, Li, Yifan, Tang, Xinyu, Liu, Zikang, Liu, Peiyu, Nie, Jian-Yun, Wen, Ji-Rong
Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modelin
Externí odkaz:
http://arxiv.org/abs/2303.18223
Modeling long texts has been an essential technique in the field of natural language processing (NLP). With the ever-growing number of long documents, it is important to develop effective modeling methods that can process and analyze such texts. Howe
Externí odkaz:
http://arxiv.org/abs/2302.14502
Autor:
Tang, Tianyi, Li, Junyi, Chen, Zhipeng, Hu, Yiwen, Yu, Zhuohao, Dai, Wenxun, Dong, Zican, Cheng, Xiaoxue, Wang, Yuhao, Zhao, Wayne Xin, Nie, Jian-Yun, Wen, Ji-Rong
To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2.0, focusing on the use of pre-trained language models (PLMs). To be comprehensive, our library covers $13$ common text generation tasks and
Externí odkaz:
http://arxiv.org/abs/2212.13005