14 papers
Authorship Attribution in Multilingual Machine-Generated Texts
Lucio La Cava, Dominik Macko, Róbert Móro +2
As Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult…
mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection
Dominik Macko
SemEval-2026 Task 10 is focused on conspiracy detection. Specifically, the goal is to detect whether a Reddit comment expresses a conspiracy belief. Our submitted mdok-style system…
mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection
Dominik Macko, Alok Debnath, Jakub Simko
SemEval-2026 Task 9 is focused on multilingual polarization detection. Specifically, it covers the identification of multilingual, multicultural and multievent polarization along t…
mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code
Adam Skurla, Dominik Macko, Jakub Simko
Multi-domain detection of the machine-generated code snippets in various programming languages is a challenging task. SemEval-2026 Task~13 copes with this challenge in various angl…
Interpretable Predictability-Based AI Text Detection: A Replication Study
Adam Skurla, Dominik Macko, Jakub Simko
This paper replicates and extends the system used in the AuTexTification 2023 shared task for authorship attribution of machine-generated texts. First, we tried to reproduce the or…
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…