Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Zhou, Pingyi"'
Autor:
Wang, Shengnan, Bai, Youhui, Zhang, Lin, Zhou, Pingyi, Zhao, Shixiong, Zhang, Gong, Wang, Sen, Chen, Renhai, Xu, Hua, Sun, Hongwei
Length generalization failure problem, namely the large language model (LLM) fails to generalize to texts longer than its maximum training length, greatly restricts the application of LLM in the scenarios with streaming long inputs. To address this p
Externí odkaz:
http://arxiv.org/abs/2405.17755
Transformer-based Large Language Models (LLMs) often impose limitations on the length of the text input to ensure the generation of fluent and relevant responses. This constraint restricts their applicability in scenarios involving long texts. We pro
Externí odkaz:
http://arxiv.org/abs/2312.09571
Autor:
Ren, Xiaozhe, Zhou, Pingyi, Meng, Xinfan, Huang, Xinjing, Wang, Yadao, Wang, Weichao, Li, Pengfei, Zhang, Xiaoda, Podolskiy, Alexander, Arshinov, Grigory, Bout, Andrey, Piontkovskaya, Irina, Wei, Jiansheng, Jiang, Xin, Su, Teng, Liu, Qun, Yao, Jun
The scaling of large language models has greatly improved natural language understanding, generation, and reasoning. In this work, we develop a system that trained a trillion-parameter language model on a cluster of Ascend 910 AI processors and MindS
Externí odkaz:
http://arxiv.org/abs/2303.10845
Code completion is a valuable topic in both academia and industry. Recently, large-scale mono-programming-lingual (MonoPL) pre-training models have been proposed to boost the performance of code completion. However, the code completion on low-resourc
Externí odkaz:
http://arxiv.org/abs/2212.09666
Autor:
Christopoulou, Fenia, Lampouras, Gerasimos, Gritta, Milan, Zhang, Guchun, Guo, Yinpeng, Li, Zhongqi, Zhang, Qi, Xiao, Meng, Shen, Bo, Li, Lin, Yu, Hao, Yan, Li, Zhou, Pingyi, Wang, Xin, Ma, Yuchi, Iacobacci, Ignacio, Wang, Yasheng, Liang, Guangtai, Wei, Jiansheng, Jiang, Xin, Wang, Qianxiang, Liu, Qun
We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i.e. the synthesis of programming language solutions given a natural language problem description. We train PanGu-Code
Externí odkaz:
http://arxiv.org/abs/2207.11280
Recent years have witnessed increasing interest in code representation learning, which aims to represent the semantics of source code into distributed vectors. Currently, various works have been proposed to represent the complex semantics of source c
Externí odkaz:
http://arxiv.org/abs/2205.02029
Autor:
Wang, Xin, Wang, Yasheng, Wan, Yao, Mi, Fei, Li, Yitong, Zhou, Pingyi, Liu, Jin, Wu, Hao, Jiang, Xin, Liu, Qun
Automatically generating compilable programs with (or without) natural language descriptions has always been a touchstone problem for computational linguistics and automated software engineering. Existing deep-learning approaches model code generatio
Externí odkaz:
http://arxiv.org/abs/2203.05132
Autor:
Wang, Yihe, Li, Yitong, Wang, Yasheng, Mi, Fei, Zhou, Pingyi, Wang, Xin, Liu, Jin, Jiang, Xin, Liu, Qun
Real human conversation data are complicated, heterogeneous, and noisy, from which building open-domain dialogue systems remains a challenging task. In fact, such dialogue data still contains a wealth of information and knowledge, however, they are n
Externí odkaz:
http://arxiv.org/abs/2201.11367
Autor:
Wang, Xin, Wang, Yasheng, Mi, Fei, Zhou, Pingyi, Wan, Yao, Liu, Xiao, Li, Li, Wu, Hao, Liu, Jin, Jiang, Xin
Code representation learning, which aims to encode the semantics of source code into distributed vectors, plays an important role in recent deep-learning-based models for code intelligence. Recently, many pre-trained language models for source code (
Externí odkaz:
http://arxiv.org/abs/2108.04556
Publikováno v:
In Ceramics International 15 July 2022 48(14):20778-20790