Zobrazeno 1 - 10
of 845
pro vyhledávání: '"WANG Zekun"'
Autor:
WANG Zekun, ZHANG Fuxi
Publikováno v:
Shanghai Jiaotong Daxue xuebao, Vol 55, Iss 11, Pp 1445-1452 (2021)
The release of compressive stress and atom diffusion have important influences on the growth of whiskers in 3D electronic packaging, and the compressive stress is also one of the main factors for dynamic recrystallization (DRX). By using the mathemat
Externí odkaz:
https://doaj.org/article/6c2c37c46c7e49829560f03f30f9d051
Autor:
Liu, Jiaheng, Deng, Ken, Liu, Congnan, Yang, Jian, Liu, Shukai, Zhu, He, Zhao, Peng, Chai, Linzheng, Wu, Yanan, Jin, Ke, Zhang, Ge, Wang, Zekun, Zhang, Guoan, Xiang, Bangyu, Su, Wenbo, Zheng, Bo
Repository-level code completion has drawn great attention in software engineering, and several benchmark datasets have been introduced. However, existing repository-level code completion benchmarks usually focus on a limited number of languages (<5)
Externí odkaz:
http://arxiv.org/abs/2410.21157
Autor:
Wang, Zekun Moore, Wang, Shawn, Zhu, Kang, Liu, Jiaheng, Xu, Ke, Fu, Jie, Zhou, Wangchunshu, Huang, Wenhao
Alignment of large language models (LLMs) involves training models on preference-contrastive output pairs to adjust their responses according to human preferences. To obtain such contrastive pairs, traditional methods like RLHF and RLAIF rely on limi
Externí odkaz:
http://arxiv.org/abs/2410.13785
Autor:
Wang, Pei, Wu, Yanan, Wang, Zekun, Liu, Jiaheng, Song, Xiaoshuai, Peng, Zhongyuan, Deng, Ken, Zhang, Chenchen, Wang, Jiakai, Peng, Junran, Zhang, Ge, Guo, Hangyu, Zhang, Zhaoxiang, Su, Wenbo, Zheng, Bo
Large Language Models (LLMs) have displayed massive improvements in reasoning and decision-making skills and can hold natural conversations with users. Recently, many tool-use benchmark datasets have been proposed. However, existing datasets have the
Externí odkaz:
http://arxiv.org/abs/2410.11710
Large Language Models (LLMs) demonstrate impressive capabilities across various domains, including role-playing, creative writing, mathematical reasoning, and coding. Despite these advancements, LLMs still encounter challenges with length control, fr
Externí odkaz:
http://arxiv.org/abs/2410.07035
Autor:
Wang, Zekun, Zhu, King, Xu, Chunpu, Zhou, Wangchunshu, Liu, Jiaheng, Zhang, Yibo, Wang, Jiashuo, Shi, Ning, Li, Siyu, Li, Yizhi, Que, Haoran, Zhang, Zhaoxiang, Zhang, Yuanxing, Zhang, Ge, Xu, Ke, Fu, Jie, Huang, Wenhao
In this paper, we introduce MIO, a novel foundation model built on multimodal tokens, capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner. While the emergence of large language models (LLMs
Externí odkaz:
http://arxiv.org/abs/2409.17692
Autor:
Que, Haoran, Duan, Feiyu, He, Liqun, Mou, Yutao, Zhou, Wangchunshu, Liu, Jiaheng, Rong, Wenge, Wang, Zekun Moore, Yang, Jian, Zhang, Ge, Peng, Junran, Zhang, Zhaoxiang, Zhang, Songyang, Chen, Kai
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks (e.g., long-context understanding), and many benchmarks have been proposed. However, we observe that long text generation capabilities are not we
Externí odkaz:
http://arxiv.org/abs/2409.16191
Autor:
Li, Yizhi, Zhang, Ge, Ma, Yinghao, Yuan, Ruibin, Zhu, Kang, Guo, Hangyu, Liang, Yiming, Liu, Jiaheng, Wang, Zekun, Yang, Jian, Wu, Siwei, Qu, Xingwei, Shi, Jinjie, Zhang, Xinyue, Yang, Zhenzhu, Wang, Xiangzhou, Zhang, Zhaoxiang, Liu, Zachary, Benetos, Emmanouil, Huang, Wenhao, Lin, Chenghua
Recent advancements in multimodal large language models (MLLMs) have aimed to integrate and interpret data across diverse modalities. However, the capacity of these models to concurrently process and reason about multiple modalities remains inadequat
Externí odkaz:
http://arxiv.org/abs/2409.15272
Autor:
Wang, Yuxin, Ma, Minghua, Wang, Zekun, Chen, Jingchang, Fan, Huiming, Shan, Liping, Yang, Qing, Xu, Dongliang, Liu, Ming, Qin, Bing
The colossal parameters and computational overhead of Large Language Models (LLMs) challenge their real-world applications. Network pruning, which targets unstructured or structured sparsity by removing redundant parameters, has recently been explore
Externí odkaz:
http://arxiv.org/abs/2409.13199
Multi-modal large language models (MLLMs) have demonstrated considerable potential across various downstream tasks that require cross-domain knowledge. MLLMs capable of processing videos, known as Video-MLLMs, have attracted broad interest in video-l
Externí odkaz:
http://arxiv.org/abs/2408.14023