Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Liu, Mickel"'
Autor:
Dai, Josef, Pan, Xuehai, Sun, Ruiyang, Ji, Jiaming, Xu, Xinbo, Liu, Mickel, Wang, Yizhou, Yang, Yaodong
With the development of large language models (LLMs), striking a balance between the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmlessness presents a si
Externí odkaz:
http://arxiv.org/abs/2310.12773
Autor:
Yang, Aiyuan, Xiao, Bin, Wang, Bingning, Zhang, Borong, Bian, Ce, Yin, Chao, Lv, Chenxu, Pan, Da, Wang, Dian, Yan, Dong, Yang, Fan, Deng, Fei, Wang, Feng, Liu, Feng, Ai, Guangwei, Dong, Guosheng, Zhao, Haizhou, Xu, Hang, Sun, Haoze, Zhang, Hongda, Liu, Hui, Ji, Jiaming, Xie, Jian, Dai, JunTao, Fang, Kun, Su, Lei, Song, Liang, Liu, Lifeng, Ru, Liyun, Ma, Luyao, Wang, Mang, Liu, Mickel, Lin, MingAn, Nie, Nuolan, Guo, Peidong, Sun, Ruiyang, Zhang, Tao, Li, Tianpeng, Li, Tianyu, Cheng, Wei, Chen, Weipeng, Zeng, Xiangrong, Wang, Xiaochuan, Chen, Xiaoxi, Men, Xin, Yu, Xin, Pan, Xuehai, Shen, Yanjun, Wang, Yiding, Li, Yiyu, Jiang, Youxin, Gao, Yuchen, Zhang, Yupeng, Zhou, Zenan, Wu, Zhiying
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LL
Externí odkaz:
http://arxiv.org/abs/2309.10305
Autor:
Ji, Jiaming, Liu, Mickel, Dai, Juntao, Pan, Xuehai, Zhang, Chi, Bian, Ce, Sun, Ruiyang, Wang, Yizhou, Yang, Yaodong
In this paper, we introduce the BeaverTails dataset, aimed at fostering research on safety alignment in large language models (LLMs). This dataset uniquely separates annotations of helpfulness and harmlessness for question-answering pairs, thus offer
Externí odkaz:
http://arxiv.org/abs/2307.04657
Autor:
Ji, Jiaming, Zhou, Jiayi, Zhang, Borong, Dai, Juntao, Pan, Xuehai, Sun, Ruiyang, Huang, Weidong, Geng, Yiran, Liu, Mickel, Yang, Yaodong
AI systems empowered by reinforcement learning (RL) algorithms harbor the immense potential to catalyze societal advancement, yet their deployment is often impeded by significant safety concerns. Particularly in safety-critical applications, research
Externí odkaz:
http://arxiv.org/abs/2305.09304
This paper presents a multi-agent reinforcement learning (MARL) scheme for proactive Multi-Camera Collaboration in 3D Human Pose Estimation in dynamic human crowds. Traditional fixed-viewpoint multi-camera solutions for human motion capture (MoCap) a
Externí odkaz:
http://arxiv.org/abs/2303.03767