Zobrazeno 1 - 10
of 74
pro vyhledávání: '"SU Yusheng"'
Autor:
Chen, Weize, Yuan, Chenfei, Yuan, Jiarui, Su, Yusheng, Qian, Chen, Yang, Cheng, Xie, Ruobing, Liu, Zhiyuan, Sun, Maosong
Natural language (NL) has long been the predominant format for human cognition and communication, and by extension, has been similarly pivotal in the development and application of Large Language Models (LLMs). Yet, besides NL, LLMs have seen various
Externí odkaz:
http://arxiv.org/abs/2402.18439
The recent success of Large Language Models (LLMs) has catalyzed an increasing interest in their self-correction capabilities. This paper presents a comprehensive investigation into the intrinsic self-correction of LLMs, attempting to address the ong
Externí odkaz:
http://arxiv.org/abs/2402.12563
Autor:
Chen, Weize, Su, Yusheng, Zuo, Jingwei, Yang, Cheng, Yuan, Chenfei, Chan, Chi-Min, Yu, Heyang, Lu, Yaxi, Hung, Yi-Hsin, Qian, Chen, Qin, Yujia, Cong, Xin, Xie, Ruobing, Liu, Zhiyuan, Sun, Maosong, Zhou, Jie
Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-world scenarios, cooperation among individuals is often required to en
Externí odkaz:
http://arxiv.org/abs/2308.10848
Autor:
Chan, Chi-Min, Chen, Weize, Su, Yusheng, Yu, Jianxuan, Xue, Wei, Zhang, Shanghang, Fu, Jie, Liu, Zhiyuan
Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost. With the emergence of large language models (LLMs), researchers have explored LLMs' potential as alternatives for human evaluation. While
Externí odkaz:
http://arxiv.org/abs/2308.07201
Autor:
Qian, Chen, Liu, Wei, Liu, Hongzhang, Chen, Nuo, Dang, Yufan, Li, Jiahao, Yang, Cheng, Chen, Weize, Su, Yusheng, Cong, Xin, Xu, Juyuan, Li, Dahai, Liu, Zhiyuan, Sun, Maosong
Software development is a complex task that necessitates cooperation among multiple members with diverse skills. Numerous studies used deep learning to improve specific phases in a waterfall model, such as design, coding, and testing. However, the de
Externí odkaz:
http://arxiv.org/abs/2307.07924
Autor:
Su, Yusheng, Chan, Chi-Min, Cheng, Jiali, Qin, Yujia, Lin, Yankai, Hu, Shengding, Yang, Zonghan, Ding, Ning, Sun, Xingzhi, Xie, Guotong, Liu, Zhiyuan, Sun, Maosong
Parameter-efficient tuning (PET) methods can effectively drive extremely large pre-trained language models (PLMs) by training only minimal parameters. Different PET methods utilize different manually designed tunable modules. In small PLMs, there are
Externí odkaz:
http://arxiv.org/abs/2306.02320
Autor:
Qin, Yujia, Hu, Shengding, Lin, Yankai, Chen, Weize, Ding, Ning, Cui, Ganqu, Zeng, Zheni, Huang, Yufei, Xiao, Chaojun, Han, Chi, Fung, Yi Ren, Su, Yusheng, Wang, Huadong, Qian, Cheng, Tian, Runchu, Zhu, Kunlun, Liang, Shihao, Shen, Xingyu, Xu, Bokai, Zhang, Zhen, Ye, Yining, Li, Bowen, Tang, Ziwei, Yi, Jing, Zhu, Yuzhang, Dai, Zhenning, Yan, Lan, Cong, Xin, Lu, Yaxi, Zhao, Weilin, Huang, Yuxiang, Yan, Junxi, Han, Xu, Sun, Xian, Li, Dahai, Phang, Jason, Yang, Cheng, Wu, Tongshuang, Ji, Heng, Liu, Zhiyuan, Sun, Maosong
Humans possess an extraordinary ability to create and utilize tools, allowing them to overcome physical limitations and explore new frontiers. With the advent of foundation models, AI systems have the potential to be equally adept in tool use as huma
Externí odkaz:
http://arxiv.org/abs/2304.08354
Autor:
Li, Ming, Su, Yusheng, Huang, Hsiu-Yuan, Cheng, Jiali, Hu, Xin, Zhang, Xinmiao, Wang, Huadong, Qin, Yujia, Wang, Xiaozhi, Lindquist, Kristen A., Liu, Zhiyuan, Zhang, Dan
Humans no doubt use language to communicate about their emotional experiences, but does language in turn help humans understand emotions, or is language just a vehicle of communication? This study used a form of artificial intelligence (AI) known as
Externí odkaz:
http://arxiv.org/abs/2302.09582
Autor:
Ding, Ning, Qin, Yujia, Yang, Guang, Wei, Fuchao, Yang, Zonghan, Su, Yusheng, Hu, Shengding, Chen, Yulin, Chan, Chi-Min, Chen, Weize, Yi, Jing, Zhao, Weilin, Wang, Xiaozhi, Liu, Zhiyuan, Zheng, Hai-Tao, Chen, Jianfei, Liu, Yang, Tang, Jie, Li, Juanzi, Sun, Maosong
Despite the success, the process of fine-tuning large-scale PLMs brings prohibitive adaptation costs. In fact, fine-tuning all the parameters of a colossal model and retaining separate instances for different tasks are practically infeasible. This ne
Externí odkaz:
http://arxiv.org/abs/2203.06904
Autor:
Su, Yusheng, Wang, Xiaozhi, Qin, Yujia, Chan, Chi-Min, Lin, Yankai, Wang, Huadong, Wen, Kaiyue, Liu, Zhiyuan, Li, Peng, Li, Juanzi, Hou, Lei, Sun, Maosong, Zhou, Jie
Prompt tuning (PT) is a promising parameter-efficient method to utilize extremely large pre-trained language models (PLMs), which can achieve comparable performance to full-parameter fine-tuning by only tuning a few soft prompts. However, PT requires
Externí odkaz:
http://arxiv.org/abs/2111.06719