3 citations · 3 across the 4 of their papers we have counts for
4 papers
FNBench: Benchmarking Robust Federated Learning against Noisy Labels
Xuefeng Jiang, Jia Li, Nannan Wu +7
Robustness to label noise within data is a significant challenge in federated learning (FL). From the data-centric perspective, the data quality of distributed datasets can not be…
Jailbreak-as-a-Service++: Unveiling Distributed AI-Driven Malicious Information Campaigns Powered by LLM Crowdsourcing
Yu Yan, Sheng Sun, Mingfeng Li +6
To prevent the misuse of Large Language Models (LLMs) for malicious purposes, numerous efforts have been made to develop the safety alignment mechanisms of LLMs. However, as multip…
from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors
Yu Yan, Sheng Sun, Zenghao Duan +5
Current studies have exposed the risk of Large Language Models (LLMs) generating harmful content by jailbreak attacks. However, they overlook that the direct generation of harmful…
Na'vi or Knave: Jailbreaking Language Models via Metaphorical Avatars
Yu Yan, Sheng Sun, Junqi Tong +2
Metaphor serves as an implicit approach to convey information, while enabling the generalized comprehension of complex subjects. However, metaphor can potentially be exploited to b…