2 code implementations • 21 Feb 2024 • Michal Spiegel, Dominik Macko
SemEval-2024 Task 8 is focused on multigenerator, multidomain, and multilingual black-box machine-generated text detection.
no code implementations • 15 Jan 2024 • Dominik Macko, Robert Moro, Adaku Uchendu, Ivan Srba, Jason Samuel Lucas, Michiharu Yamashita, Nafis Irtiza Tripto, Dongwon Lee, Jakub Simko, Maria Bielikova
However, it is susceptible to authorship obfuscation (AO) methods, such as paraphrasing, which can cause MGTs to evade detection.
1 code implementation • 21 Nov 2023 • Michal Spiegel, Dominik Macko
In the era of large language models generating high quality texts, it is a necessity to develop methods for detection of machine-generated text to avoid harmful use or simply due to annotation purposes.
1 code implementation • 15 Nov 2023 • Ivan Vykopal, Matúš Pikuliak, Ivan Srba, Robert Moro, Dominik Macko, Maria Bielikova
Automated disinformation generation is often listed as an important risk associated with large language models (LLMs).
no code implementations • 14 Nov 2023 • Nafis Irtiza Tripto, Saranya Venkatraman, Dominik Macko, Robert Moro, Ivan Srba, Adaku Uchendu, Thai Le, Dongwon Lee
In the realm of text manipulation and linguistic transformation, the question of authorship has always been a subject of fascination and philosophical inquiry.
1 code implementation • 20 Oct 2023 • Dominik Macko, Robert Moro, Adaku Uchendu, Jason Samuel Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, Maria Bielikova
There is a lack of research into capabilities of recent LLMs to generate convincing text in languages other than English and into performance of detectors of machine-generated text in multilingual settings.
1 code implementation • 10 Oct 2023 • Dominik Macko, Patrik Goldschmidt, Peter Pištek, Daniela Chudá
We perform our experiments by employing a hybrid anomaly detection approach in network flow data.