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ORCIDJán Cegin
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2020 – today
- 2024
[c7]Ján Cegin, Branislav Pecher, Jakub Simko, Ivan Srba, Mária Bieliková, Peter Brusilovsky:
Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation. ACL (1) 2024: 13148-13171- [c6]Branislav Pecher, Ján Cegin, Róbert Belanec, Jakub Simko, Ivan Srba, Mária Bieliková:
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy Interpolation. EMNLP (Findings) 2024: 11005-11044 - [i5]Ján Cegin, Branislav Pecher, Jakub Simko, Ivan Srba, Mária Bieliková, Peter Brusilovsky:
Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation. CoRR abs/2401.06643 (2024) - [i4]Branislav Pecher, Ján Cegin, Róbert Belanec, Jakub Simko, Ivan Srba, Mária Bieliková:
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy Interpolation. CoRR abs/2406.12471 (2024) - [i3]Ján Cegin, Jakub Simko, Peter Brusilovsky:
LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs? CoRR abs/2408.16502 (2024) - [i2]Ján Cegin, Branislav Pecher, Jakub Simko, Ivan Srba, Mária Bieliková, Peter Brusilovsky:
Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classification. CoRR abs/2410.10756 (2024) - 2023
- [c5]Ján Cegin, Jakub Simko, Peter Brusilovsky:
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness. EMNLP 2023: 1889-1905 - [i1]Ján Cegin, Jakub Simko, Peter Brusilovsky:
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness. CoRR abs/2305.12947 (2023) - 2022
- [c4]Ján Cegin, Jakub Simko, Peter Brusilovsky:
A Game for Crowdsourcing Adversarial Examples for False Information Detection. AiOfAi@IJCAI 2022: 13-25 - 2020
- [c3]Ján Cegin, Karol Rástocný
:
Test Data Generation for MC/DC Criterion using Reinforcement Learning. ICST Workshops 2020: 354-357 - [c2]Ján Cegin, Karol Rástocný, Mária Bieliková:
Synthesized dataset for search-based test data generation methods focused on MC/DC criterion. QRS Companion 2020: 680-681 - [c1]Ján Cegin:
Machine learning based test data generation for safety-critical software. ESEC/SIGSOFT FSE 2020: 1678-1681
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