2 citations · 7 across the 10 of their papers we have counts for
10 papers
GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking
Yingjian Chen, Haoran Liu, Yinhong Liu +8
Large language models (LLMs) are widely used, but they often generate subtle factual errors, especially in long-form text. These errors are fatal in some specialized domains such a…
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential
Wei Tang, Yixin Cao, Jiahao Ying +4
Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models (LLMs). Targeting its bottleneck of retriever performance,…
Detecting AI-Generated Video via Frame Consistency
Long Ma, Zhiyuan Yan, Qinglang Guo +3
The escalating quality of video generated by advanced video generation methods results in new security challenges, while there have been few relevant research efforts: 1) There is…
Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable Noise
Qinglong Huang, Haoran Li, Yong Liao +2
Neural Radiance Field (NeRF) has been proposed as an innovative advancement in 3D reconstruction techniques. However, little research has been conducted on the issues of informatio…
FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion
Wei Tang, Zhiqian Wu, Yixin Cao +2
Knowledge graph completion (KGC) aims to predict missing facts in knowledge graphs (KGs), which is crucial as modern KGs remain largely incomplete. While training KGC models on mul…
User Authentication and Identity Inconsistency Detection via Mouse-trajectory Similarity Measurement
Rui Jin, Yong Liao, Pengyuan Zhou
Completely Automated Public Turing Test To Tell Computers and Humans Apart (CAPTCHA) is a type of challenge-response test widely used in authentication systems. A well-known challe…
AHSecAgg and TSKG: Lightweight Secure Aggregation for Federated Learning Without Compromise
Siqing Zhang, Yong Liao, Pengyuan Zhou
Leveraging federated learning (FL) to enable cross-domain privacy-sensitive data mining represents a vital breakthrough to accomplish privacy-preserving learning. However, attacker…
λ-SecAgg: Partial Vector Freezing for Lightweight Secure Aggregation in Federated Learning
Siqing Zhang, Yong Liao, Pengyuan Zhou
Secure aggregation of user update vectors (e.g. gradients) has become a critical issue in the field of federated learning. Many Secure Aggregation Protocols (SAPs) face exorbitant…
3D-GOI: 3D GAN Omni-Inversion for Multifaceted and Multi-object Editing
Haoran Li, Long Ma, Haolin Shi +4
The current GAN inversion methods typically can only edit the appearance and shape of a single object and background while overlooking spatial information. In this work, we propose…
NLPBench: Evaluating Large Language Models on Solving NLP Problems
Linxin Song, Jieyu Zhang, Lechao Cheng +3
Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP). Despite these successes, there remains a…