ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP

4 August 2023

Papers citing "ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP"

4 / 4 papers shown

Title
When LLM Meets DRL: Advancing Jailbreaking Efficiency via DRL-guided Search Xuan Chen Yuzhou Nie Wenbo Guo Xiangyu Zhang 112 10 0 28 Jan 2025
On the Challenges of Fuzzing Techniques via Large Language Models Linghan Huang Peizhou Zhao Huaming Chen Lei Ma 16 14 0 01 Feb 2024
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer Fanchao Qi Yangyi Chen Xurui Zhang Mukai Li Zhiyuan Liu Maosong Sun AAML SILM 82 175 0 14 Oct 2021
Mitigating backdoor attacks in LSTM-based Text Classification Systems by Backdoor Keyword Identification Chuanshuai Chen Jiazhu Dai SILM 55 126 0 11 Jul 2020