Zobrazeno 1 - 10
of 215
pro vyhledávání: '"Wu, Junhong"'
Large Language Models (LLMs) have achieved impressive results across numerous NLP tasks but still encounter difficulties in machine translation. Traditional methods to improve translation have typically involved fine-tuning LLMs using parallel corpor
Externí odkaz:
http://arxiv.org/abs/2410.13944
Large Language Models (LLMs) have achieved state-of-the-art performance across numerous tasks. However, these advancements have predominantly benefited "first-class" languages such as English and Chinese, leaving many other languages underrepresented
Externí odkaz:
http://arxiv.org/abs/2410.08964
Ensembling various LLMs to unlock their complementary potential and leverage their individual strengths is highly valuable. Previous studies typically focus on two main paradigms: sample-level and token-level ensembles. Sample-level ensemble methods
Externí odkaz:
http://arxiv.org/abs/2409.18583
The recent release of GPT-4o showcased the potential of end-to-end multimodal models, not just in terms of low latency but also in their ability to understand and generate expressive speech with rich emotions. While the details are unknown to the ope
Externí odkaz:
http://arxiv.org/abs/2406.03872
In the evolving landscape of Neural Machine Translation (NMT), the pretrain-then-finetune paradigm has yielded impressive results. However, the persistent challenge of Catastrophic Forgetting (CF) remains a hurdle. While previous work has introduced
Externí odkaz:
http://arxiv.org/abs/2404.04846
Autor:
Wang, Chen, Liao, Minpeng, Huang, Zhongqiang, Lu, Jinliang, Wu, Junhong, Liu, Yuchen, Zong, Chengqing, Zhang, Jiajun
The emergence of large language models (LLMs) has sparked significant interest in extending their remarkable language capabilities to speech. However, modality alignment between speech and text still remains an open problem. Current solutions can be
Externí odkaz:
http://arxiv.org/abs/2309.00916
Autor:
Liu, Ruyi1,2,3 (AUTHOR) ruyiliu@xidian.edu.cn, Wu, Junhong1,2,3 (AUTHOR) 22031212398@stu.xidian.edu.cn, Lu, Wenyi1,2,3 (AUTHOR) 23031212091@stu.xidian.edu.cn, Miao, Qiguang1,2,3 (AUTHOR) qgmiao@xidian.edu.cn, Zhang, Huan4 (AUTHOR) zhmfpp@163.com, Liu, Xiangzeng1,2,3 (AUTHOR) zxlu@xidian.edu.cn, Lu, Zixiang1,2,3 (AUTHOR), Li, Long5 (AUTHOR) lilong@guet.edu.cn
Publikováno v:
Remote Sensing. Jun2024, Vol. 16 Issue 12, p2056. 37p.
Autor:
Yang, Xia, Lu, Juntao, Su, Fangyu, Wu, Junhong, Wang, Xinhao, Hu, Zhaokun, Yan, Zhaoyang, Xu, Huanchen, Shang, Xiaobin, Guo, Wei
Publikováno v:
In Translational Oncology December 2024 50
Autor:
Li, Dan, Sun, Jieyi, Fu, Yibo, Hong, Wentao, Wang, Heli, Yang, Qian, Wu, Junhong, Yang, Sen, Xu, Jianhui, Zhang, Yunfei, Deng, Yirong, Zhong, Yin, Peng, Ping'an
Publikováno v:
In Water Research 1 January 2024 248
Autor:
Wu Junhong
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
Inefficiencies in supply chain logistics management, particularly in certain regions, often result in “broken chain” incidents that significantly impact the efficiency of e-commerce operations and degrade consumer experiences. This paper addresse
Externí odkaz:
https://doaj.org/article/fe1ed79e019e4499a6b7b12c7e2543ea