1 code implementation • 26 Mar 2024 • Zhelun Shi, Zhipin Wang, Hongxing Fan, Zaibin Zhang, Lijun Li, Yongting Zhang, Zhenfei Yin, Lu Sheng, Yu Qiao, Jing Shao
Large Language Models (LLMs) aim to serve as versatile assistants aligned with human values, as defined by the principles of being helpful, honest, and harmless (hhh).
1 code implementation • 18 Mar 2024 • Weikang Zhou, Xiao Wang, Limao Xiong, Han Xia, Yingshuang Gu, Mingxu Chai, Fukang Zhu, Caishuang Huang, Shihan Dou, Zhiheng Xi, Rui Zheng, Songyang Gao, Yicheng Zou, Hang Yan, Yifan Le, Ruohui Wang, Lijun Li, Jing Shao, Tao Gui, Qi Zhang, Xuanjing Huang
This paper introduces EasyJailbreak, a unified framework simplifying the construction and evaluation of jailbreak attacks against LLMs.
1 code implementation • 7 Feb 2024 • Lijun Li, Bowen Dong, Ruohui Wang, Xuhao Hu, WangMeng Zuo, Dahua Lin, Yu Qiao, Jing Shao
In the rapidly evolving landscape of Large Language Models (LLMs), ensuring robust safety measures is paramount.
no code implementations • 26 Jan 2024 • Chaochao Lu, Chen Qian, Guodong Zheng, Hongxing Fan, Hongzhi Gao, Jie Zhang, Jing Shao, Jingyi Deng, Jinlan Fu, Kexin Huang, Kunchang Li, Lijun Li, LiMin Wang, Lu Sheng, Meiqi Chen, Ming Zhang, Qibing Ren, Sirui Chen, Tao Gui, Wanli Ouyang, Yali Wang, Yan Teng, Yaru Wang, Yi Wang, Yinan He, Yingchun Wang, Yixu Wang, Yongting Zhang, Yu Qiao, Yujiong Shen, Yurong Mou, Yuxi Chen, Zaibin Zhang, Zhelun Shi, Zhenfei Yin, Zhipin Wang
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents.
1 code implementation • 22 Jan 2024 • Zaibin Zhang, Yongting Zhang, Lijun Li, Hongzhi Gao, Lijun Wang, Huchuan Lu, Feng Zhao, Yu Qiao, Jing Shao
In this paper, we explore these concerns through the innovative lens of agent psychology, revealing that the dark psychological states of agents constitute a significant threat to safety.
1 code implementation • ICCV 2023 • Lijun Li, Linrui Tian, Xindi Zhang, Qi Wang, Bang Zhang, Mengyuan Liu, Chen Chen
The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited.
no code implementations • 23 May 2023 • Lijun Li, Li'an Zhuo, Bang Zhang, Liefeng Bo, Chen Chen
Hand mesh reconstruction from the monocular image is a challenging task due to its depth ambiguity and severe occlusion, there remains a non-unique mapping between the monocular image and hand mesh.
no code implementations • 21 Jun 2022 • Lijun Li, Li'an Zhuo, Bang Zhang
In this work, we introduce our solution to the EPIC-KITCHENS-100 2022 Action Detection challenge.
no code implementations • ICLR 2019 • Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong
In other words, there is a population of adversarial examples, instead of only one, for any input to a DNN.
1 code implementation • 1 May 2019 • Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong
Powerful adversarial attack methods are vital for understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques.
1 code implementation • 21 Mar 2018 • Lijun Li, Boqing Gong
Although end-to-end (E2E) learning has led to impressive progress on a variety of visual understanding tasks, it is often impeded by hardware constraints (e. g., GPU memory) and is prone to overfitting.