no code implementations • 15 Feb 2024 • Wanli Yang, Fei Sun, Xinyu Ma, Xun Liu, Dawei Yin, Xueqi Cheng
In this work, we reveal a critical phenomenon: even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks.
no code implementations • 22 Jan 2024 • Hexiang Tan, Fei Sun, Wanli Yang, Yuanzhuo Wang, Qi Cao, Xueqi Cheng
While auxiliary information has become a key to enhancing Large Language Models (LLMs), relatively little is known about how LLMs merge these contexts, specifically contexts generated by LLMs and those retrieved from external sources.
1 code implementation • IEEE Access 2019 • Ansi Zhang, Shaobo Li, Yuxin Cui, Wanli Yang, Rongzhi Dong and Jianjun Hu
In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data.