4 citations · 12 across the 6 of their papers we have counts for
6 papers
Beemo: Benchmark of Expert-edited Machine-generated Outputs
Ekaterina Artemova, Jason Lucas, Saranya Venkatraman +4
The rapid proliferation of large language models (LLMs) has increased the volume of machine-generated texts (MGTs) and blurred text authorship in various domains. However, most exi…
PlagBench: Exploring the Duality of Large Language Models in Plagiarism Generation and Detection
Jooyoung Lee, Toshini Agrawal, Adaku Uchendu +3
Recent studies have raised concerns about the potential threats large language models (LLMs) pose to academic integrity and copyright protection. Yet, their investigation is predom…
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…
Comparison of L2 Korean pronunciation error patterns from five L1 backgrounds by using automatic phonetic transcription
Eun Jung Yeo, Hyungshin Ryu, Jooyoung Lee +2
This paper presents a large-scale analysis of L2 Korean pronunciation error patterns from five different language backgrounds, Chinese, Vietnamese, Japanese, Thai, and English, by…
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
Adaku Uchendu, Jooyoung Lee, Hua Shen +3
Advances in Large Language Models (e.g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so…
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense
Thai Le, Jooyoung Lee, Kevin Yen +2
We proposes a novel algorithm, ANTHRO, that inductively extracts over 600K human-written text perturbations in the wild and leverages them for realistic adversarial attack. Unlike…