Zobrazeno 1 - 10
of 127
pro vyhledávání: '"Cheng, Jiale"'
Autor:
Gui, Jiayi, Liu, Yiming, Cheng, Jiale, Gu, Xiaotao, Liu, Xiao, Wang, Hongning, Dong, Yuxiao, Tang, Jie, Huang, Minlie
Large Language Models (LLMs) have demonstrated notable capabilities across various tasks, showcasing complex problem-solving abilities. Understanding and executing complex rules, along with multi-step planning, are fundamental to logical reasoning an
Externí odkaz:
http://arxiv.org/abs/2408.15778
Autor:
Cheng, Jiale, Lu, Yida, Gu, Xiaotao, Ke, Pei, Liu, Xiao, Dong, Yuxiao, Wang, Hongning, Tang, Jie, Huang, Minlie
Although Large Language Models (LLMs) are becoming increasingly powerful, they still exhibit significant but subtle weaknesses, such as mistakes in instruction-following or coding tasks. As these unexpected errors could lead to severe consequences in
Externí odkaz:
http://arxiv.org/abs/2406.16714
Autor:
GLM, Team, Zeng, Aohan, Xu, Bin, Wang, Bowen, Zhang, Chenhui, Yin, Da, Zhang, Dan, Rojas, Diego, Feng, Guanyu, Zhao, Hanlin, Lai, Hanyu, Yu, Hao, Wang, Hongning, Sun, Jiadai, Zhang, Jiajie, Cheng, Jiale, Gui, Jiayi, Tang, Jie, Zhang, Jing, Sun, Jingyu, Li, Juanzi, Zhao, Lei, Wu, Lindong, Zhong, Lucen, Liu, Mingdao, Huang, Minlie, Zhang, Peng, Zheng, Qinkai, Lu, Rui, Duan, Shuaiqi, Zhang, Shudan, Cao, Shulin, Yang, Shuxun, Tam, Weng Lam, Zhao, Wenyi, Liu, Xiao, Xia, Xiao, Zhang, Xiaohan, Gu, Xiaotao, Lv, Xin, Liu, Xinghan, Liu, Xinyi, Yang, Xinyue, Song, Xixuan, Zhang, Xunkai, An, Yifan, Xu, Yifan, Niu, Yilin, Yang, Yuantao, Li, Yueyan, Bai, Yushi, Dong, Yuxiao, Qi, Zehan, Wang, Zhaoyu, Yang, Zhen, Du, Zhengxiao, Hou, Zhenyu, Wang, Zihan
We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B. They represent our most capable model
Externí odkaz:
http://arxiv.org/abs/2406.12793
Autor:
Ke, Pei, Wen, Bosi, Feng, Zhuoer, Liu, Xiao, Lei, Xuanyu, Cheng, Jiale, Wang, Shengyuan, Zeng, Aohan, Dong, Yuxiao, Wang, Hongning, Tang, Jie, Huang, Minlie
Since the natural language processing (NLP) community started to make large language models (LLMs) act as a critic to evaluate the quality of generated texts, most of the existing works train a critique generation model on the evaluation data labeled
Externí odkaz:
http://arxiv.org/abs/2311.18702
Autor:
Liu, Xiao, Lei, Xuanyu, Wang, Shengyuan, Huang, Yue, Feng, Zhuoer, Wen, Bosi, Cheng, Jiale, Ke, Pei, Xu, Yifan, Tam, Weng Lam, Zhang, Xiaohan, Sun, Lichao, Gu, Xiaotao, Wang, Hongning, Zhang, Jing, Huang, Minlie, Dong, Yuxiao, Tang, Jie
Alignment has become a critical step for instruction-tuned Large Language Models (LLMs) to become helpful assistants. However, the effective evaluation of alignment for emerging Chinese LLMs is still largely unexplored. To fill in this gap, we introd
Externí odkaz:
http://arxiv.org/abs/2311.18743
Autor:
Cheng, Jiale, Liu, Xiao, Zheng, Kehan, Ke, Pei, Wang, Hongning, Dong, Yuxiao, Tang, Jie, Huang, Minlie
Large language models (LLMs) have shown impressive success in various applications. However, these models are often not well aligned with human intents, which calls for additional treatments on them; that is, the alignment problem. To make LLMs bette
Externí odkaz:
http://arxiv.org/abs/2311.04155
With the rapid popularity of large language models such as ChatGPT and GPT-4, a growing amount of attention is paid to their safety concerns. These models may generate insulting and discriminatory content, reflect incorrect social values, and may be
Externí odkaz:
http://arxiv.org/abs/2304.10436
With the development of artificial intelligence, dialogue systems have been endowed with amazing chit-chat capabilities, and there is widespread interest and discussion about whether the generated contents are socially beneficial. In this paper, we p
Externí odkaz:
http://arxiv.org/abs/2302.09270
Due to the lack of human resources for mental health support, there is an increasing demand for employing conversational agents for support. Recent work has demonstrated the effectiveness of dialogue models in providing emotional support. As previous
Externí odkaz:
http://arxiv.org/abs/2212.09235
We formulate a new secure distributed computation problem, where a simulation center can require any linear combination of $ K $ users' data through a caching layer consisting of $ N $ servers. The users, servers, and data collector do not trust each
Externí odkaz:
http://arxiv.org/abs/2212.05914