63 citations · 190 across the 37 of their papers we have counts for
12 papers · 1 filter
ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction
Lvhua Wu, Xuefeng Jiang, Sheng Sun +4
The rapid spread of fake news threatens social stability and public trust, highlighting the urgent need for its effective detection. Although large language models (LLMs) show pote…
Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs
Yu Yan, Sheng Sun, Zhe Wang +6
With the development of Large Language Models (LLMs), numerous efforts have revealed their vulnerabilities to jailbreak attacks. Although these studies have driven the progress in…
Robust Federated Learning against Noisy Clients via Masked Optimization
Xuefeng Jiang, Tian Wen, Zhiqin Yang +5
In recent years, federated learning (FL) has made significant advance in privacy-sensitive applications. However, it can be hard to ensure that FL participants provide well-annotat…
Recursive Offloading for LLM Serving in Multi-tier Networks
Zhiyuan Wu, Sheng Sun, Yuwei Wang +5
Heterogeneous device-edge-cloud computing infrastructures have become widely adopted in telecommunication operators and Wide Area Networks (WANs), offering multi-tier computational…
SVAFD: A Secure and Verifiable Co-Aggregation Protocol for Federated Distillation
Tian Wen, Sheng Sun, Yuwei Wang +4
Secure Aggregation (SA) is an indispensable component of Federated Learning (FL) that concentrates on privacy preservation while allowing for robust aggregation. However, most SA d…
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…
Collaborative Stance Detection via Small-Large Language Model Consistency Verification
Yu Yan, Sheng Sun, Zixiang Tang +2
Stance detection on social media aims to identify attitudes expressed in tweets towards specific targets. Current studies prioritize Large Language Models (LLMs) over Small Languag…
Investigating Large Language Models for Code Vulnerability Detection: An Experimental Study
Xuefeng Jiang, Lvhua Wu, Sheng Sun +5
Code vulnerability detection (CVD) is essential for addressing and preventing system security issues, playing a crucial role in ensuring software security. Previous learning-based…
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection
Qingxiang Liu, Xiaoliang Luo, Chenghao Liu +5
Unsupervised anomaly detection of multivariate time series is a challenging task, given the requirements of deriving a compact detection criterion without accessing the anomaly poi…
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration
Zhiyuan Wu, Sheng Sun, Yuwei Wang +5
The rise of End-Edge-Cloud Collaboration (EECC) offers a promising paradigm for Artificial Intelligence (AI) model training across end devices, edge servers, and cloud data centers…