8 papers
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 shared task for authorship attribution of machine-generated texts. Exact replication was not possible becau…
MultiCW: A Large-Scale Balanced Benchmark Dataset for Training Robust Check-Worthiness Detection Models
Martin Hyben, Sebastian Kula, Jan Cegin +3
Large Language Models (LLMs) are beginning to reshape how media professionals verify information, yet automated support for detecting check-worthy claims a key step in the fact-che…
Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification
Branislav Pecher, Jan Cegin, Robert Belanec +3
Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, making them promising tools in both high- and low-resource languages. One particularly valuable…
RoSE: Round-robin Synthetic Data Evaluation for Selecting LLM Generators without Human Test Sets
Jan Cegin, Branislav Pecher, Ivan Srba +1
LLMs are powerful generators of synthetic data, which are used for training smaller, specific models. This is especially valuable for low-resource languages, where human-labelled d…
Political Leaning and Politicalness Classification of Texts
Matous Volf, Jakub Simko
This paper addresses the challenge of automatically classifying text according to political leaning and politicalness using transformer models. We compose a comprehensive overview…
A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages
Tatiana Anikina, Jan Cegin, Jakub Simko +1
Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. However, a comparison of various generation strategie…