6 citations · 28 across the 16 of their papers we have counts for
7 papers · 1 filter
Keeping an Eye on LLM Unlearning: The Hidden Risk and Remedy
Jie Ren, Zhenwei Dai, Xianfeng Tang +9
Although Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, growing concerns have emerged over the misuse of sensitive, copyrighte…
Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation
Jiankun Zhang, Shenglai Zeng, Jie Ren +4
Multimodal Retrieval-Augmented Generation (MRAG) systems enhance LMMs by integrating external multimodal databases, but introduce unexplored privacy vulnerabilities. While text-bas…
Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data
Shenglai Zeng, Jiankun Zhang, Pengfei He +7
Retrieval-augmented generation (RAG) enhances the outputs of language models by integrating relevant information retrieved from external knowledge sources. However, when the retrie…
The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)
Shenglai Zeng, Jiankun Zhang, Pengfei He +8
Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model with proprietary and private data, where data privacy is a pivotal concern. Whereas extens…
Copyright Protection in Generative AI: A Technical Perspective
Jie Ren, Han Xu, Pengfei He +10
Generative AI has witnessed rapid advancement in recent years, expanding their capabilities to create synthesized content such as text, images, audio, and code. The high fidelity a…
A Robust Semantics-based Watermark for Large Language Model against Paraphrasing
Jie Ren, Han Xu, Yiding Liu +4
Large language models (LLMs) have show great ability in various natural language tasks. However, there are concerns that LLMs are possible to be used improperly or even illegally.…
Stealthy Backdoor Attack via Confidence-driven Sampling
Pengfei He, Yue Xing, Han Xu +6
Backdoor attacks aim to surreptitiously insert malicious triggers into DNN models, granting unauthorized control during testing scenarios. Existing methods lack robustness against…