no code implementations • 18 Dec 2023 • Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Xinyi Wang, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, Xing Xie
Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it.
no code implementations • 25 Oct 2023 • Xiting Wang, Liming Jiang, Jose Hernandez-Orallo, David Stillwell, Luning Sun, Fang Luo, Xing Xie
Comprehensive and accurate evaluation of general-purpose AI systems such as large language models allows for effective mitigation of their risks and deepened understanding of their capabilities.
no code implementations • 14 Jul 2023 • Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, Xing Xie
In addition to those deterministic tasks that can be automatically evaluated using existing metrics, we conducted a human study with 106 participants to assess the quality of generative tasks using both vanilla and emotional prompts.
1 code implementation • 5 Jun 2021 • Zhenfeng Shao, JiaMing Wang, Lianbing Deng, Xiao Huang, Tao Lu, Fang Luo, Ruiqian Zhang, Xianwei Lv, Chaoya Dang, Qing Ding, Zhiqiang Wang
In this paper, we introduce a challenging global large-scale ship database (called GLSD), designed specifically for ship detection tasks.