Zobrazeno 1 - 10
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pro vyhledávání: '"Zhang, Wentao"'
Large language models (LLMs) have demonstrated exceptional performance across a wide range of tasks and domains, with data preparation playing a critical role in achieving these results. Pre-training data typically combines information from multiple
Externí odkaz:
http://arxiv.org/abs/2409.17527
Large Language Models (LLMs) have exhibited exceptional performance across a broad range of tasks and domains. However, they still encounter difficulties in solving mathematical problems due to the rigorous and logical nature of mathematics. Previous
Externí odkaz:
http://arxiv.org/abs/2409.17972
Autor:
Huang, Qiang, Yan, Xiao, Wang, Xin, Rao, Susie Xi, Han, Zhichao, Fu, Fangcheng, Zhang, Wentao, Jiang, Jiawei
Temporal graph neural networks (TGNNs) outperform regular GNNs by incorporating time information into graph-based operations. However, TGNNs adopt specialized models (e.g., TGN, TGAT, and APAN ) and require tailored training frameworks (e.g., TGL and
Externí odkaz:
http://arxiv.org/abs/2409.05477
Autor:
Lu, Keer, Liang, Zheng, Nie, Xiaonan, Pan, Da, Zhang, Shusen, Zhao, Keshi, Chen, Weipeng, Zhou, Zenan, Dong, Guosheng, Zhang, Wentao, Cui, Bin
The effectiveness of long-context modeling is important for Large Language Models (LLMs) in various applications. Despite their potential, LLMs' efficacy in processing long context does not consistently meet expectations, posing significant challenge
Externí odkaz:
http://arxiv.org/abs/2409.00997
Autor:
Li, Xunkai, Zhu, Yinlin, Pang, Boyang, Yan, Guochen, Yan, Yeyu, Li, Zening, Wu, Zhengyu, Zhang, Wentao, Li, Rong-Hua, Wang, Guoren
Federated graph learning (FGL) has emerged as a promising distributed training paradigm for graph neural networks across multiple local systems without direct data sharing. This approach is particularly beneficial in privacy-sensitive scenarios and o
Externí odkaz:
http://arxiv.org/abs/2408.16288
Autor:
Dong, Guosheng, Pan, Da, Sun, Yiding, Zhang, Shusen, Liang, Zheng, Wu, Xin, Shen, Yanjun, Yang, Fan, Sun, Haoze, Li, Tianpeng, Lin, Mingan, Xu, Jianhua, Zhang, Yufan, Nie, Xiaonan, Su, Lei, Wang, Bingning, Zhang, Wentao, Mao, Jiaxin, Zhou, Zenan, Chen, Weipeng
The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several institutions. To mitigate this issue, we open-source the details of a uni
Externí odkaz:
http://arxiv.org/abs/2408.15079
Autor:
An, Wei, Bi, Xiao, Chen, Guanting, Chen, Shanhuang, Deng, Chengqi, Ding, Honghui, Dong, Kai, Du, Qiushi, Gao, Wenjun, Guan, Kang, Guo, Jianzhong, Guo, Yongqiang, Fu, Zhe, He, Ying, Huang, Panpan, Li, Jiashi, Liang, Wenfeng, Liu, Xiaodong, Liu, Xin, Liu, Yiyuan, Liu, Yuxuan, Lu, Shanghao, Lu, Xuan, Nie, Xiaotao, Pei, Tian, Qiu, Junjie, Qu, Hui, Ren, Zehui, Sha, Zhangli, Su, Xuecheng, Sun, Xiaowen, Tan, Yixuan, Tang, Minghui, Wang, Shiyu, Wang, Yaohui, Wang, Yongji, Xie, Ziwei, Xiong, Yiliang, Xu, Yanhong, Ye, Shengfeng, Yu, Shuiping, Zha, Yukun, Zhang, Liyue, Zhang, Haowei, Zhang, Mingchuan, Zhang, Wentao, Zhang, Yichao, Zhao, Chenggang, Zhao, Yao, Zhou, Shangyan, Zhou, Shunfeng, Zou, Yuheng
The rapid progress in Deep Learning (DL) and Large Language Models (LLMs) has exponentially increased demands of computational power and bandwidth. This, combined with the high costs of faster computing chips and interconnects, has significantly infl
Externí odkaz:
http://arxiv.org/abs/2408.14158
With the development of the modern social economy, tourism has become an important way to meet people's spiritual needs, bringing development opportunities to the tourism industry. However, existing large language models (LLMs) face challenges in per
Externí odkaz:
http://arxiv.org/abs/2408.12003
Autor:
Yin, Yuanyang, Zhao, Yaqi, Zhang, Yajie, Lin, Ke, Wang, Jiahao, Tao, Xin, Wan, Pengfei, Zhang, Di, Yin, Baoqun, Zhang, Wentao
Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable perceptual and reasoning abilities, typically comprising a Vision Encoder, an Adapter, and a Large Language Model (LLM). The adapter serves as the critical bridge between
Externí odkaz:
http://arxiv.org/abs/2408.11813
Autor:
Qin, Yanzhao, Zhang, Tao, Shen, Yanjun, Luo, Wenjing, Sun, Haoze, Zhang, Yan, Qiao, Yujing, Chen, Weipeng, Zhou, Zenan, Zhang, Wentao, Cui, Bin
Large Language Models (LLMs) have become instrumental across various applications, with the customization of these models to specific scenarios becoming increasingly critical. System message, a fundamental component of LLMs, is consist of carefully c
Externí odkaz:
http://arxiv.org/abs/2408.10943