Publications

Authorship Obfuscation in Multilingual Machine-Generated Text Detection
This research from Penn State and KiNiT, benchmarks the effectiveness of 10 authorship obfuscation (AO) techniques against 37 machine-generated text (MGT) detection methods across 11 languages, totaling 4,070 evaluations. It reveals that all AO methods can evade detection in every language, particularly highlighting the efficacy of homoglyph attacks. This underscores the need for improved multilingual MGT detection strategies.
Dominik Macko, Robert Moro, Adaku Uchendu, Ivan Srba, Jason Lucas, Michiharu Yamashita, Nafis Irtiza Tripto, Dongwon Lee, Jakub Simko, Maria Bielikova
PDF Cite Project Slides DOI
Authorship Obfuscation in Multilingual Machine-Generated Text Detection
MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark
This research from Penn State and KiNiT introduces MULTITuDE, a novel multilingual dataset for detecting machine-generated text. Comprised of over 74,000 authentic and artificially-generated texts in 11 languages from 8 models, MULTITuDE benchmarks text generation capabilities in non-English languages and multilingual detection performance. The dataset addresses current gaps in analyzing and systematically evaluating machine text generation and detection across multiple languages.
Dominik Macko, Robert Moro, Adaku Uchendu, Jason Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, Maria Bielikova
PDF Cite Code Dataset Project Slides Video Source Document DOI
MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark
Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation
This research project is a collaboration with Penn State and MIT Lincoln Lab. Our study demonstrates the dual capacity of LLMs for offensive misuse and defense detection against disinformation without requiring additional training.
Jason Lucas, Adaku Uchendu, Michiharu Yamashita, Jooyoung Lee, Shaurya Rohatgi, Dongwon Lee
PDF Cite Code Dataset Project Slides Video Source Document DOI
Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation
Detecting False Claims in Low-Resource Regions: A Case Study of Caribbean Islands
This paper is the first attempt to detect COVID-19 misinformation (in English, Spanish, and Haitian French) populated in the Caribbean regions, using the fact-checked claims in the US (in English).
Jason Lucas, Limeng Cui, Thai Lee, Dongwon Lee
PDF Cite Dataset Project Poster Slides Video Source Document
Detecting False Claims in Low-Resource Regions: A Case Study of Caribbean Islands