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20232026
most citedThe Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)

6 citations · 28 across the 16 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CR2023★ 3 cited

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.…

cs.CL2023

On the Generalization of Training-based ChatGPT Detection Methods

Han Xu, Jie Ren, Pengfei He +5

ChatGPT is one of the most popular language models which achieve amazing performance on various natural language tasks. Consequently, there is also an urgent need to detect the tex…

cs.AI2023★ 2 cited

Exploring Memorization in Fine-tuned Language Models

Shenglai Zeng, Yaxin Li, Jie Ren +7

Large language models (LLMs) have shown great capabilities in various tasks but also exhibited memorization of training data, raising tremendous privacy and copyright concerns. Whi…

cs.CR2023★ 1 cited

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…

cs.CV2023★ 4 cited

FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models

Yingqian Cui, Jie Ren, Yuping Lin +7

Text-to-image generative models, especially those based on latent diffusion models (LDMs), have demonstrated outstanding ability in generating high-quality and high-resolution imag…