14 citations · 26 across the 9 of their papers we have counts for
9 papers
BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages
Jason Lucas, Matt Murtagh-White, Adaku Uchendu +6
Multilingual falsehoods threaten information integrity worldwide, yet detection benchmarks remain confined to English or a few high-resource languages, leaving low-resource linguis…
Unmasking Fake Careers: Detecting Machine-Generated Career Trajectories via Multi-layer Heterogeneous Graphs
Michiharu Yamashita, Thanh Tran, Delvin Ce Zhang +1
The rapid advancement of Large Language Models (LLMs) has enabled the generation of highly realistic synthetic data. We identify a new vulnerability, LLMs generating convincing car…
CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship
Yeon-Chang Lee, JaeHyun Lee, Michiharu Yamashita +2
The problem of career trajectory prediction (CTP) aims to predict one's future employer or job position. While several CTP methods have been developed for this problem, we posit th…
Fake Resume Attacks: Data Poisoning on Online Job Platforms
Michiharu Yamashita, Thanh Tran, Dongwon Lee
While recent studies have exposed various vulnerabilities incurred from data poisoning attacks in many web services, little is known about the vulnerability on online professional…
Authorship Obfuscation in Multilingual Machine-Generated Text Detection
Dominik Macko, Robert Moro, Adaku Uchendu +7
High-quality text generation capability of recent Large Language Models (LLMs) causes concerns about their misuse (e.g., in massive generation/spread of disinformation). Machine-ge…
Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation
Jason Lucas, Adaku Uchendu, Michiharu Yamashita +3
Recent ubiquity and disruptive impacts of large language models (LLMs) have raised concerns about their potential to be misused (.i.e, generating large-scale harmful and misleading…