Test Data Generation for MC/DC Criterion using Reinforcement Learning. - Ján Cegin; Karol Rástocný (ICST Workshops, 2020)

Synthesized dataset for search-based test data generation methods focused on MC/DC criterion. - Ján Cegin; Karol Rástocný; Mária Bieliková (QRS Companion, 2020)

Machine learning based test data generation for safety-critical software. - Ján Cegin (ESEC/SIGSOFT FSE, 2020)

A Game for Crowdsourcing Adversarial Examples for False Information Detection. - Ján Cegin; Jakub Simko; Peter Brusilovsky (AiOfAi@IJCAI, 2022)

ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness. - Ján Cegin; Jakub Simko; Peter Brusilovsky (EMNLP, 2023)

Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation. - Ján Cegin; Branislav Pecher; Jakub Simko; Ivan Srba; Mária Bieliková; Peter Brusilovsky (ACL (1), 2024)

Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy Interpolation. - Branislav Pecher; Ján Cegin; Róbert Belanec; Jakub Simko; Ivan Srba; Mária Bieliková (EMNLP (Findings), 2024)

Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classification. - Ján Cegin; Branislav Pecher; Jakub Simko; Ivan Srba; Mária Bieliková; Peter Brusilovsky (CoRR, 2024)

LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs? - Ján Cegin; Jakub Simko; Peter Brusilovsky (NAACL (Long Papers), 2025)

A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages. - Tatiana Anikina; Ján Cegin; Jakub Simko; Simon Ostermann 002 (CoRR, 2025)