Search Results for author: Shenglai Zeng

Found 8 papers, 4 papers with code

The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)

1 code implementation23 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.

Language Modelling Retrieval

Copyright Protection in Generative AI: A Technical Perspective

no code implementations4 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.

Exploring Memorization in Fine-tuned Language Models

no code implementations10 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.

Memorization

On the Generalization of Training-based ChatGPT Detection Methods

1 code implementation2 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.

HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse

1 code implementation7 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.

Federated Learning Privacy Preserving

Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training

no code implementations31 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.

Federated Learning Privacy Preserving

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