1 code implementation • 23 Feb 2024 • Shenglai Zeng, Jiankun Zhang, Pengfei He, Yue Xing, Yiding Liu, Han Xu, Jie Ren, Shuaiqiang Wang, Dawei Yin, Yi Chang, Jiliang Tang
In this work, we conduct extensive empirical studies with novel attack methods, which demonstrate the vulnerability of RAG systems on leaking the private retrieval database.
no code implementations • 4 Feb 2024 • Jie Ren, Han Xu, Pengfei He, Yingqian Cui, Shenglai Zeng, Jiankun Zhang, Hongzhi Wen, Jiayuan Ding, Hui Liu, Yi Chang, Jiliang Tang
We examine from two distinct viewpoints: the copyrights pertaining to the source data held by the data owners and those of the generative models maintained by the model builders.
no code implementations • 10 Oct 2023 • Shenglai Zeng, Yaxin Li, Jie Ren, Yiding Liu, Han Xu, Pengfei He, Yue Xing, Shuaiqiang Wang, Jiliang Tang, Dawei Yin
In this work, we conduct the first comprehensive analysis to explore language models' (LMs) memorization during fine-tuning across tasks.
1 code implementation • 2 Oct 2023 • Han Xu, Jie Ren, Pengfei He, Shenglai Zeng, Yingqian Cui, Amy Liu, Hui Liu, Jiliang Tang
ChatGPT is one of the most popular language models which achieve amazing performance on various natural language tasks.
1 code implementation • NeurIPS 2023 • Juanhui Li, Harry Shomer, Haitao Mao, Shenglai Zeng, Yao Ma, Neil Shah, Jiliang Tang, Dawei Yin
Furthermore, new and diverse datasets have also been created to better evaluate the effectiveness of these new models.
no code implementations • 5 Mar 2023 • Jiaqi Wang, Shenglai Zeng, Zewei Long, Yaqing Wang, Houping Xiao, Fenglong Ma
This is a new yet practical scenario in federated learning, i. e., labels-at-server semi-supervised federated learning (SemiFL).
1 code implementation • 7 Nov 2022 • Shenglai Zeng, Zonghang Li, Hongfang Yu, Zhihao Zhang, Long Luo, Bo Li, Dusit Niyato
Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Industrial Metaverse.
no code implementations • 31 Jan 2022 • Shenglai Zeng, Zonghang Li, Hongfang Yu, Yihong He, Zenglin Xu, Dusit Niyato, Han Yu
In this paper, we propose a data heterogeneity-robust FL approach, FedGSP, to address this challenge by leveraging on a novel concept of dynamic Sequential-to-Parallel (STP) collaborative training.